"""simple docstring"""
from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         LayoutLMvaConfig,
         TFLayoutLMvaForQuestionAnswering,
         TFLayoutLMvaForSequenceClassification,
         TFLayoutLMvaForTokenClassification,
         TFLayoutLMvaModel,
     )

if is_vision_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor



class 					lowerCAmelCase_      :



    """simple docstring"""

    def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=2						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=2						,      lowerCAmelCase=7						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=99						,      lowerCAmelCase=36						,      lowerCAmelCase=2						,      lowerCAmelCase=4						,      lowerCAmelCase=37						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=16						,      lowerCAmelCase=2						,      lowerCAmelCase=0.02						,      lowerCAmelCase=6						,      lowerCAmelCase=6						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=None						,      lowerCAmelCase=10_00						,      ):






          """simple docstring"""

          snake_case        =     parent
          snake_case        =     batch_size
          snake_case        =     num_channels
          snake_case        =     image_size
          snake_case        =     patch_size
          snake_case        =     is_training
          snake_case        =     use_input_mask
          snake_case        =     use_token_type_ids
          snake_case        =     use_labels
          snake_case        =     vocab_size
          snake_case        =     hidden_size
          snake_case        =     num_hidden_layers
          snake_case        =     num_attention_heads
          snake_case        =     intermediate_size
          snake_case        =     hidden_act
          snake_case        =     hidden_dropout_prob
          snake_case        =     attention_probs_dropout_prob
          snake_case        =     max_position_embeddings
          snake_case        =     type_vocab_size
          snake_case        =     type_sequence_label_size
          snake_case        =     initializer_range
          snake_case        =     coordinate_size
          snake_case        =     shape_size
          snake_case        =     num_labels
          snake_case        =     num_choices
          snake_case        =     scope
          snake_case        =     range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          snake_case        =     text_seq_length
          snake_case        =     (image_size // patch_size) ** 2 + 1
          snake_case        =     self.text_seq_length + self.image_seq_length

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     ids_tensor([self.batch_size, self.text_seq_length]						,      self.vocab_size      )

          snake_case        =     ids_tensor([self.batch_size, self.text_seq_length, 4]						,      self.range_bbox      )
          snake_case        =     bbox.numpy()
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]      ):
                for j in range(bbox.shape[1]      ):
                      if bbox[i, j, 3] < bbox[i, j, 1]:
                            snake_case        =     bbox[i, j, 3]
                            snake_case        =     bbox[i, j, 1]
                            snake_case        =     tmp_coordinate
                      if bbox[i, j, 2] < bbox[i, j, 0]:
                            snake_case        =     bbox[i, j, 2]
                            snake_case        =     bbox[i, j, 0]
                            snake_case        =     tmp_coordinate
          snake_case        =     tf.constant(lowerCAmelCase      )

          snake_case        =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

          snake_case        =     None
          if self.use_input_mask:
                snake_case        =     random_attention_mask([self.batch_size, self.text_seq_length]      )

          snake_case        =     None
          if self.use_token_type_ids:
                snake_case        =     ids_tensor([self.batch_size, self.text_seq_length]						,      self.type_vocab_size      )

          snake_case        =     None
          snake_case        =     None
          if self.use_labels:
                snake_case        =     ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )
                snake_case        =     ids_tensor([self.batch_size, self.text_seq_length]						,      self.num_labels      )

          snake_case        =     LayoutLMvaConfig(
              vocab_size=self.vocab_size						,      hidden_size=self.hidden_size						,      num_hidden_layers=self.num_hidden_layers						,      num_attention_heads=self.num_attention_heads						,      intermediate_size=self.intermediate_size						,      hidden_act=self.hidden_act						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      max_position_embeddings=self.max_position_embeddings						,      type_vocab_size=self.type_vocab_size						,      initializer_range=self.initializer_range						,      coordinate_size=self.coordinate_size						,      shape_size=self.shape_size						,      input_size=self.image_size						,      patch_size=self.patch_size						,      )

          return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     TFLayoutLMvaModel(config=lowerCAmelCase      )

          # text + image
          snake_case        =     model(lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      training=lowerCAmelCase      )
          snake_case        =     model(
              lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      training=lowerCAmelCase						,      )
          snake_case        =     model(lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      training=lowerCAmelCase      )

          self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)      )

          # text only
          snake_case        =     model(lowerCAmelCase						,      training=lowerCAmelCase      )
          self.parent.assertEqual(
              result.last_hidden_state.shape						,      (self.batch_size, self.text_seq_length, self.hidden_size)      )

          # image only
          snake_case        =     model({'pixel_values': pixel_values}						,      training=lowerCAmelCase      )
          self.parent.assertEqual(
              result.last_hidden_state.shape						,      (self.batch_size, self.image_seq_length, self.hidden_size)      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     self.num_labels
          snake_case        =     TFLayoutLMvaForSequenceClassification(config=lowerCAmelCase      )
          snake_case        =     model(
              lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      labels=lowerCAmelCase						,      training=lowerCAmelCase						,      )
          self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     self.num_labels
          snake_case        =     TFLayoutLMvaForTokenClassification(config=lowerCAmelCase      )
          snake_case        =     model(
              lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      labels=lowerCAmelCase						,      training=lowerCAmelCase						,      )
          self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.text_seq_length, self.num_labels)      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     2
          snake_case        =     TFLayoutLMvaForQuestionAnswering(config=lowerCAmelCase      )
          snake_case        =     model(
              lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      start_positions=lowerCAmelCase						,      end_positions=lowerCAmelCase						,      training=lowerCAmelCase						,      )
          self.parent.assertEqual(result.start_logits.shape						,      (self.batch_size, self.seq_length)      )
          self.parent.assertEqual(result.end_logits.shape						,      (self.batch_size, self.seq_length)      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     self.prepare_config_and_inputs()
          ((snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case)   ,(snake_case))        =     config_and_inputs
          snake_case        =     {
              'input_ids': input_ids,
              'bbox': bbox,
              'pixel_values': pixel_values,
              'token_type_ids': token_type_ids,
              'attention_mask': input_mask,
          }
          return config, inputs_dict





@require_tf
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      Any     					=  (
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    _lowerCAmelCase       :      Optional[int]     					=  (
        {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    _lowerCAmelCase       :      Any     					=  False
    _lowerCAmelCase       :      Tuple     					=  False
    _lowerCAmelCase       :      Optional[int]     					=  False

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          return True

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase=False      ):






          """simple docstring"""

          snake_case        =     copy.deepcopy(lowerCAmelCase      )

          if model_class in get_values(lowerCAmelCase      ):
                snake_case        =     {
                    k: tf.tile(tf.expand_dims(lowerCAmelCase						,      1      )						,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)      )
                    if isinstance(lowerCAmelCase						,      tf.Tensor      ) and v.ndim > 0
                    else v
                    for k, v in inputs_dict.items()
                }

          if return_labels:
                if model_class in get_values(lowerCAmelCase      ):
                      snake_case        =     tf.ones(self.model_tester.batch_size						,      dtype=tf.intaa      )
                elif model_class in get_values(lowerCAmelCase      ):
                      snake_case        =     tf.zeros(self.model_tester.batch_size						,      dtype=tf.intaa      )
                      snake_case        =     tf.zeros(self.model_tester.batch_size						,      dtype=tf.intaa      )
                elif model_class in get_values(lowerCAmelCase      ):
                      snake_case        =     tf.zeros(self.model_tester.batch_size						,      dtype=tf.intaa      )
                elif model_class in get_values(lowerCAmelCase      ):
                      snake_case        =     tf.zeros(
                          (self.model_tester.batch_size, self.model_tester.text_seq_length)						,      dtype=tf.intaa      )

          return inputs_dict

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     TFLayoutLMvaModelTester(self      )
          snake_case        =     ConfigTester(self						,      config_class=lowerCAmelCase						,      hidden_size=37      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          self.config_tester.run_common_tests()

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                snake_case        =     model_class(lowerCAmelCase      )
                if getattr(lowerCAmelCase						,      'hf_compute_loss'						,      lowerCAmelCase      ):
                      # The number of elements in the loss should be the same as the number of elements in the label
                      snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )
                      snake_case        =     prepared_for_class[
                          sorted(prepared_for_class.keys() - inputs_dict.keys()						,      reverse=lowerCAmelCase      )[0]
                      ]
                      snake_case        =     added_label.shape.as_list()[:1]

                      # Test that model correctly compute the loss with kwargs
                      snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )
                      snake_case        =     prepared_for_class.pop('input_ids'      )

                      snake_case        =     model(lowerCAmelCase						,      **lowerCAmelCase      )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

                      # Test that model correctly compute the loss when we mask some positions
                      snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )
                      snake_case        =     prepared_for_class.pop('input_ids'      )
                      if "labels" in prepared_for_class:
                            snake_case        =     prepared_for_class['labels'].numpy()
                            if len(labels.shape      ) > 1 and labels.shape[1] != 1:
                                  snake_case        =     -1_00
                                  snake_case        =     tf.convert_to_tensor(lowerCAmelCase      )
                                  snake_case        =     model(lowerCAmelCase						,      **lowerCAmelCase      )[0]
                                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )
                                  self.assertTrue(not np.any(np.isnan(loss.numpy()      )      )      )

                # Test that model correctly compute the loss with a dict
                      snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )
                      snake_case        =     model(lowerCAmelCase      )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

                      # Test that model correctly compute the loss with a tuple
                      snake_case        =     self._prepare_for_class(inputs_dict.copy()						,      lowerCAmelCase						,      return_labels=lowerCAmelCase      )

                      # Get keys that were added with the _prepare_for_class function
                      snake_case        =     prepared_for_class.keys() - inputs_dict.keys()
                      snake_case        =     inspect.signature(model.call      ).parameters
                      snake_case        =     list(signature.keys()      )

                      # Create a dictionary holding the location of the tensors in the tuple
                      snake_case        =     {0: 'input_ids'}
                      for label_key in label_keys:
                            snake_case        =     signature_names.index(lowerCAmelCase      )
                            snake_case        =     label_key
                      snake_case        =     sorted(tuple_index_mapping.items()      )
                      # Initialize a list with their default values, update the values and convert to a tuple
                      snake_case        =     []

                      for name in signature_names:
                            if name != "kwargs":
                                  list_input.append(signature[name].default      )

                      for index, value in sorted_tuple_index_mapping:
                            snake_case        =     prepared_for_class[value]

                      snake_case        =     tuple(lowerCAmelCase      )

                      # Send to model
                      snake_case        =     model(tuple_input[:-1]      )[0]

                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          (
              (
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,
          )        =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          (
              (
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,
          )        =     self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                snake_case        =     type
                self.model_tester.create_and_check_model(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          (
              (
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,
          )        =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(
              lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          (
              (
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,
          )        =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(
              lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          (
              (
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,(
              snake_case
          )   ,
          )        =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(
              lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

    @slow
    def 						snake_case	(			self      ):






          """simple docstring"""

          for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case        =     TFLayoutLMvaModel.from_pretrained(lowerCAmelCase      )
                self.assertIsNotNone(lowerCAmelCase      )





def 				lowerCAmelCase__      (     )   ->			str:





      """simple docstring"""






      snake_case        =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
      return image



@require_tf
class 					lowerCAmelCase_      (     unittest.TestCase   ):



    """simple docstring"""

    @cached_property
    def 						snake_case	(			self      ):






          """simple docstring"""

          return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase      ) if is_vision_available() else None

    @slow
    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'      )

          snake_case        =     self.default_image_processor
          snake_case        =     prepare_img()
          snake_case        =     image_processor(images=lowerCAmelCase						,      return_tensors='tf'      ).pixel_values

          snake_case        =     tf.constant([[1, 2]]      )
          snake_case        =     tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]      )						,      axis=0      )

          # forward pass
          snake_case        =     model(input_ids=lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      training=lowerCAmelCase      )

          # verify the logits
          snake_case        =     (1, 1_99, 7_68)
          self.assertEqual(outputs.last_hidden_state.shape						,      lowerCAmelCase      )

          snake_case        =     tf.constant(
              [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]      )

          self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]						,      lowerCAmelCase						,      atol=1E-4      )      )



"""simple docstring"""
import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    BioGptForCausalLM,
					    BioGptForSequenceClassification,
					    BioGptForTokenClassification,
					    BioGptModel,
					    BioGptTokenizer,
					)
					from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST



class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=13						,      lowerCAmelCase=7						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=False						,      lowerCAmelCase=True						,      lowerCAmelCase=99						,      lowerCAmelCase=32						,      lowerCAmelCase=5						,      lowerCAmelCase=4						,      lowerCAmelCase=37						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=16						,      lowerCAmelCase=2						,      lowerCAmelCase=0.02						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=None						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     seq_length
										snake_case        =     is_training
										snake_case        =     use_input_mask
										snake_case        =     use_token_type_ids
										snake_case        =     use_labels
										snake_case        =     vocab_size
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     intermediate_size
										snake_case        =     hidden_act
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     max_position_embeddings
										snake_case        =     type_vocab_size
										snake_case        =     type_sequence_label_size
										snake_case        =     initializer_range
										snake_case        =     num_labels
										snake_case        =     num_choices
										snake_case        =     scope

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size      )

										snake_case        =     None
										if self.use_input_mask:
																snake_case        =     random_attention_mask([self.batch_size, self.seq_length]      )

										snake_case        =     None
										if self.use_token_type_ids:
																snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.type_vocab_size      )

										snake_case        =     None
										snake_case        =     None
										snake_case        =     None
										if self.use_labels:
																snake_case        =     ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )
																snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.num_labels      )
																snake_case        =     ids_tensor([self.batch_size]						,      self.num_choices      )

										snake_case        =     self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

				def 						snake_case	(			self      ):






										"""simple docstring"""

										return BioGptConfig(
										    vocab_size=self.vocab_size						,      hidden_size=self.hidden_size						,      num_hidden_layers=self.num_hidden_layers						,      num_attention_heads=self.num_attention_heads						,      intermediate_size=self.intermediate_size						,      hidden_act=self.hidden_act						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      max_position_embeddings=self.max_position_embeddings						,      type_vocab_size=self.type_vocab_size						,      is_decoder=lowerCAmelCase						,      initializer_range=self.initializer_range						,      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )
										snake_case        =     model(lowerCAmelCase      )
										self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()

										# create attention mask
										snake_case        =     torch.ones(input_ids.shape						,      dtype=torch.long						,      device=lowerCAmelCase      )
										snake_case        =     self.seq_length // 2
										snake_case        =     0

										# first forward pass
										snake_case   ,snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      ).to_tuple()

										# create hypothetical next token and extent to next_input_ids
										snake_case        =     ids_tensor((self.batch_size, 1)						,      config.vocab_size      )

										# change a random masked slice from input_ids
										snake_case        =     ids_tensor((1,)						,      lowerCAmelCase      ).item() + 1
										snake_case        =     ids_tensor((self.batch_size, 1)						,      config.vocab_size      ).squeeze(-1      )
										snake_case        =     random_other_next_tokens

										# append to next input_ids and attn_mask
										snake_case        =     torch.cat([input_ids, next_tokens]						,      dim=-1      )
										snake_case        =     torch.cat(
										    [attn_mask, torch.ones((attn_mask.shape[0], 1)						,      dtype=torch.long						,      device=lowerCAmelCase      )]						,      dim=1						,      )

										# get two different outputs
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )['last_hidden_state']
										snake_case        =     model(lowerCAmelCase						,      past_key_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase      )['last_hidden_state']

										# select random slice
										snake_case        =     ids_tensor((1,)						,      output_from_past.shape[-1]      ).item()
										snake_case        =     output_from_no_past[:, -1, random_slice_idx].detach()
										snake_case        =     output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowerCAmelCase						,      lowerCAmelCase						,      atol=1E-3      )      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(config=lowerCAmelCase      ).to(lowerCAmelCase      ).eval()

										snake_case        =     torch.ones(input_ids.shape						,      dtype=torch.long						,      device=lowerCAmelCase      )

										# first forward pass
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      use_cache=lowerCAmelCase      )

										snake_case   ,snake_case        =     outputs.to_tuple()

										# create hypothetical multiple next token and extent to next_input_ids
										snake_case        =     ids_tensor((self.batch_size, 3)						,      config.vocab_size      )
										snake_case        =     ids_tensor((self.batch_size, 3)						,      2      )

										# append to next input_ids and
										snake_case        =     torch.cat([input_ids, next_tokens]						,      dim=-1      )
										snake_case        =     torch.cat([attention_mask, next_attn_mask]						,      dim=-1      )

										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )['last_hidden_state']
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      past_key_values=lowerCAmelCase      )[
										    'last_hidden_state'
										]

										# select random slice
										snake_case        =     ids_tensor((1,)						,      output_from_past.shape[-1]      ).item()
										snake_case        =     output_from_no_past[:, -3:, random_slice_idx].detach()
										snake_case        =     output_from_past[:, :, random_slice_idx].detach()

										self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowerCAmelCase						,      lowerCAmelCase						,      atol=1E-3      )      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase						,      lowerCAmelCase=False      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										if gradient_checkpointing:
																model.gradient_checkpointing_enable()

										snake_case        =     model(lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.parent.assertEqual(result.loss.shape						,      ()      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)      )
										result.loss.backward()

				def 						snake_case	(			self						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(lowerCAmelCase      )
										snake_case        =     model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers      )
										for key in model.state_dict().keys():
																if "c_proj" in key and "weight" in key:
																						self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]      ) - model_std      )						,      0.0_01      )
																						self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]      ) - 0.0      )						,      0.01      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     BioGptForTokenClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.num_labels)      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     config_and_inputs
										snake_case        =     {'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict





@require_torch
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      List[Any]     					=  (
				    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
				    if is_torch_available()
				    else ()
				)
				_lowerCAmelCase       :      str     					=  (BioGptForCausalLM,) if is_torch_available() else ()
				_lowerCAmelCase       :      str     					=  (
				    {
				        """feature-extraction""": BioGptModel,
				        """text-classification""": BioGptForSequenceClassification,
				        """text-generation""": BioGptForCausalLM,
				        """token-classification""": BioGptForTokenClassification,
				        """zero-shot""": BioGptForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_lowerCAmelCase       :      List[str]     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptModelTester(self      )
										snake_case        =     ConfigTester(self						,      config_class=lowerCAmelCase						,      hidden_size=37      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																snake_case        =     type
																self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase						,      gradient_checkpointing=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
										model.to(lowerCAmelCase      )
										snake_case        =     BioGptTokenizer.from_pretrained('microsoft/biogpt'      )

										snake_case        =     'left'

										# Define PAD Token = EOS Token = 50256
										snake_case        =     tokenizer.eos_token
										snake_case        =     model.config.eos_token_id

										# use different length sentences to test batching
										snake_case        =     [
										    'Hello, my dog is a little',
										    'Today, I',
										]

										snake_case        =     tokenizer(lowerCAmelCase						,      return_tensors='pt'						,      padding=lowerCAmelCase      )
										snake_case        =     inputs['input_ids'].to(lowerCAmelCase      )

										snake_case        =     model.generate(
										    input_ids=lowerCAmelCase						,      attention_mask=inputs['attention_mask'].to(lowerCAmelCase      )						,      )

										snake_case        =     tokenizer(sentences[0]						,      return_tensors='pt'      ).input_ids.to(lowerCAmelCase      )
										snake_case        =     model.generate(input_ids=lowerCAmelCase      )

										snake_case        =     inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
										snake_case        =     tokenizer(sentences[1]						,      return_tensors='pt'      ).input_ids.to(lowerCAmelCase      )
										snake_case        =     model.generate(input_ids=lowerCAmelCase						,      max_length=model.config.max_length - num_paddings      )

										snake_case        =     tokenizer.batch_decode(lowerCAmelCase						,      skip_special_tokens=lowerCAmelCase      )
										snake_case        =     tokenizer.decode(output_non_padded[0]						,      skip_special_tokens=lowerCAmelCase      )
										snake_case        =     tokenizer.decode(output_padded[0]						,      skip_special_tokens=lowerCAmelCase      )

										snake_case        =     [
										    'Hello, my dog is a little bit bigger than a little bit.',
										    'Today, I have a good idea of how to use the information',
										]
										self.assertListEqual(lowerCAmelCase						,      lowerCAmelCase      )
										self.assertListEqual(lowerCAmelCase						,      [non_padded_sentence, padded_sentence]      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case        =     BioGptModel.from_pretrained(lowerCAmelCase      )
																self.assertIsNotNone(lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
										snake_case        =     3
										snake_case        =     input_dict['input_ids']
										snake_case        =     input_ids.ne(1      ).to(lowerCAmelCase      )
										snake_case        =     ids_tensor([self.model_tester.batch_size]						,      self.model_tester.type_sequence_label_size      )
										snake_case        =     BioGptForSequenceClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.assertEqual(result.logits.shape						,      (self.model_tester.batch_size, self.model_tester.num_labels)      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
										snake_case        =     3
										snake_case        =     'multi_label_classification'
										snake_case        =     input_dict['input_ids']
										snake_case        =     input_ids.ne(1      ).to(lowerCAmelCase      )
										snake_case        =     ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]						,      self.model_tester.type_sequence_label_size      ).to(torch.float      )
										snake_case        =     BioGptForSequenceClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.assertEqual(result.logits.shape						,      (self.model_tester.batch_size, self.model_tester.num_labels)      )





@require_torch
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
										snake_case        =     torch.tensor([[2, 48_05, 9, 6_56, 21]]      )
										snake_case        =     model(lowerCAmelCase      )[0]

										snake_case        =     4_23_84

										snake_case        =     torch.Size((1, 5, vocab_size)      )
										self.assertEqual(output.shape						,      lowerCAmelCase      )

										snake_case        =     torch.tensor(
										    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]      )

										self.assertTrue(torch.allclose(output[:, :3, :3]						,      lowerCAmelCase						,      atol=1E-4      )      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptTokenizer.from_pretrained('microsoft/biogpt'      )
										snake_case        =     BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
										model.to(lowerCAmelCase      )

										torch.manual_seed(0      )
										snake_case        =     tokenizer('COVID-19 is'						,      return_tensors='pt'      ).to(lowerCAmelCase      )
										snake_case        =     model.generate(
										    **lowerCAmelCase						,      min_length=1_00						,      max_length=10_24						,      num_beams=5						,      early_stopping=lowerCAmelCase						,      )
										snake_case        =     tokenizer.decode(output_ids[0]						,      skip_special_tokens=lowerCAmelCase      )

										snake_case        =     (
										    'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
										    ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
										    ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
										    ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
										    ' more than 800,000 deaths.'
										)
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )


