"""simple docstring"""
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    LayoutLMvaForQuestionAnswering,
					    LayoutLMvaForSequenceClassification,
					    LayoutLMvaForTokenClassification,
					    LayoutLMvaModel,
					)
					from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

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=3						,      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        =     text_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        =     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      )
										# 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        =     t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																												snake_case        =     bbox[i, j, 2]
																												snake_case        =     bbox[i, j, 0]
																												snake_case        =     t

										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						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

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

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

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

										# text only
										snake_case        =     model(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=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						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     LayoutLMvaForSequenceClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(
										    lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      labels=lowerCAmelCase						,      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)      )

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






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     LayoutLMvaForTokenClassification(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(
										    lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      labels=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						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     LayoutLMvaForQuestionAnswering(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(
										    lowerCAmelCase						,      bbox=lowerCAmelCase						,      pixel_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      start_positions=lowerCAmelCase						,      end_positions=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_torch
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[Any]     					=  False
				_lowerCAmelCase       :      Any     					=  False
				_lowerCAmelCase       :      str     					=  False

				_lowerCAmelCase       :      List[Any]     					=  (
				    (
				        LayoutLMvaModel,
				        LayoutLMvaForSequenceClassification,
				        LayoutLMvaForTokenClassification,
				        LayoutLMvaForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowerCAmelCase       :      str     					=  (
				    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
				    if is_torch_available()
				    else {}
				)

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






										"""simple docstring"""

										return True

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				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: v.unsqueeze(1      ).expand(-1						,      self.model_tester.num_choices						,      -1      ).contiguous()
																    if isinstance(lowerCAmelCase						,      torch.Tensor      ) and v.ndim > 1
																    else v
																    for k, v in inputs_dict.items()
																}
										if return_labels:
																if model_class in get_values(lowerCAmelCase      ):
																						snake_case        =     torch.ones(self.model_tester.batch_size						,      dtype=torch.long						,      device=lowerCAmelCase      )
																elif model_class in get_values(lowerCAmelCase      ):
																						snake_case        =     torch.zeros(
																						    self.model_tester.batch_size						,      dtype=torch.long						,      device=lowerCAmelCase      )
																						snake_case        =     torch.zeros(
																						    self.model_tester.batch_size						,      dtype=torch.long						,      device=lowerCAmelCase      )
																elif model_class in [
																    *get_values(lowerCAmelCase      ),
																]:
																						snake_case        =     torch.zeros(
																						    self.model_tester.batch_size						,      dtype=torch.long						,      device=lowerCAmelCase      )
																elif model_class in [
																    *get_values(lowerCAmelCase      ),
																]:
																						snake_case        =     torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.text_seq_length)						,      dtype=torch.long						,      device=lowerCAmelCase						,      )

										return inputs_dict

				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_for_sequence_classification(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case        =     LayoutLMvaModel.from_pretrained(lowerCAmelCase      )
																self.assertIsNotNone(lowerCAmelCase      )





def 				lowerCAmelCase__      (     )   ->			List[Any]:





						"""simple docstring"""






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



@require_torch
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        =     LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'      ).to(lowerCAmelCase      )

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

										snake_case        =     torch.tensor([[1, 2]]      )
										snake_case        =     torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]      ).unsqueeze(0      )

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

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

										snake_case        =     torch.tensor(
										    [[-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]]      ).to(lowerCAmelCase      )

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



"""simple docstring"""
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     torch.nn.Linear(10						,      10      )
										snake_case        =     torch.optim.SGD(model.parameters()						,      0.1      )
										snake_case        =     Accelerator()
										snake_case        =     accelerator.prepare(lowerCAmelCase      )
										try:
																pickle.loads(pickle.dumps(lowerCAmelCase      )      )
										except Exception as e:
																self.fail(F"""Accelerated optimizer pickling failed with {e}"""      )
										AcceleratorState._reset_state()


