from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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.models.xglm.modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
      )





@require_tf
class 					A							:
   __UpperCAmelCase			:			int              =      XGLMConfig
   __UpperCAmelCase			:			Optional[int]              =      {}
   __UpperCAmelCase			:			Any              =      'gelu'


   def __init__(self					:						Union[str, Any]	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						str=1_4	,  __UpperCAmelCase					:						List[Any]=7	,  __UpperCAmelCase					:						Any=True	,  __UpperCAmelCase					:						Dict=True	,  __UpperCAmelCase					:						Any=True	,  __UpperCAmelCase					:						Optional[int]=9_9	,  __UpperCAmelCase					:						Dict=3_2	,  __UpperCAmelCase					:						Dict=2	,  __UpperCAmelCase					:						str=4	,  __UpperCAmelCase					:						Optional[int]=3_7	,  __UpperCAmelCase					:						List[Any]="gelu"	,  __UpperCAmelCase					:						Tuple=0.1	,  __UpperCAmelCase					:						Optional[Any]=0.1	,  __UpperCAmelCase					:						Tuple=5_1_2	,  __UpperCAmelCase					:						int=0.02	,  )						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  seq_length
         UpperCAmelCase__								=  is_training
         UpperCAmelCase__								=  use_input_mask
         UpperCAmelCase__								=  use_labels
         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  d_model
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  ffn_dim
         UpperCAmelCase__								=  activation_function
         UpperCAmelCase__								=  activation_dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  0
         UpperCAmelCase__								=  2
         UpperCAmelCase__								=  1


   def       lowercase_				(self					:						Any		)						->    Any:
         """simple docstring"""



         return XGLMConfig.from_pretrained("facebook/xglm-564M"		)


   def       lowercase_				(self					:						int		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size		)	,  clip_value_min=0	,  clip_value_max=3		)

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

         UpperCAmelCase__								=  self.get_config()

         UpperCAmelCase__								=  floats_tensor([self.num_hidden_layers, self.num_attention_heads]	,  2		)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )


   def       lowercase_				(self					:						int		)						->    Tuple:
         """simple docstring"""



         return XGLMConfig(
             vocab_size=self.vocab_size	,  d_model=self.hidden_size	,  num_layers=self.num_hidden_layers	,  attention_heads=self.num_attention_heads	,  ffn_dim=self.ffn_dim	,  activation_function=self.activation_function	,  activation_dropout=self.activation_dropout	,  attention_dropout=self.attention_dropout	,  max_position_embeddings=self.max_position_embeddings	,  initializer_range=self.initializer_range	,  use_cache=__UpperCAmelCase	,  bos_token_id=self.bos_token_id	,  eos_token_id=self.eos_token_id	,  pad_token_id=self.pad_token_id	,  return_dict=__UpperCAmelCase	,  )





   def       lowercase_				(self					:						List[Any]		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.prepare_config_and_inputs()

         (
             (
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				
         )								=  config_and_inputs

         UpperCAmelCase__								=  {
             "input_ids": input_ids,
             "head_mask": head_mask,
         }

         return config, inputs_dict





@require_tf
class 					A							(    UpperCAmelCase_		,    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			List[Any]              =      (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
   __UpperCAmelCase			:			Union[str, Any]              =      (TFXGLMForCausalLM,) if is_tf_available() else ()
   __UpperCAmelCase			:			List[str]              =      (
       {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
   )
   __UpperCAmelCase			:			Any              =      False
   __UpperCAmelCase			:			List[Any]              =      False
   __UpperCAmelCase			:			Union[str, Any]              =      False


   def       lowercase_				(self					:						Any		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  TFXGLMModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self	,  config_class=__UpperCAmelCase	,  n_embd=3_7		)


   def       lowercase_				(self					:						str		)						->    Tuple:
         """simple docstring"""



         self.config_tester.run_common_tests()


   @slow
   def       lowercase_				(self					:						Tuple		)						->    str:
         """simple docstring"""



         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase__								=  TFXGLMModel.from_pretrained(__UpperCAmelCase		)
               self.assertIsNotNone(__UpperCAmelCase		)





   @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."		)
   def       lowercase_				(self					:						Union[str, Any]		)						->    int:
         """simple docstring"""



         super().test_resize_token_embeddings()





@require_tf
class 					A							(    unittest.TestCase					):


   @slow
   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						int=True		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
         UpperCAmelCase__								=  tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]	,  dtype=tf.intaa		)  # The dog
         # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
         # fmt: off
         UpperCAmelCase__								=  [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
         # fmt: on
         UpperCAmelCase__								=  model.generate(__UpperCAmelCase	,  do_sample=__UpperCAmelCase	,  num_beams=1		)
         if verify_outputs:
               self.assertListEqual(output_ids[0].numpy().tolist()	,  __UpperCAmelCase		)


   @slow
   def       lowercase_				(self					:						Any		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)
         UpperCAmelCase__								=  TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)

         tf.random.set_seed(0		)
         UpperCAmelCase__								=  tokenizer("Today is a nice day and"	,  return_tensors="tf"		)
         UpperCAmelCase__								=  tokenized.input_ids
         # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
         with tf.device(":/CPU:0"		):
               UpperCAmelCase__								=  model.generate(__UpperCAmelCase	,  do_sample=__UpperCAmelCase	,  seed=[7, 0]		)
         UpperCAmelCase__								=  tokenizer.decode(output_ids[0]	,  skip_special_tokens=__UpperCAmelCase		)

         UpperCAmelCase__								=  (
             "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
         )
         self.assertEqual(__UpperCAmelCase	,  __UpperCAmelCase		)





   @slow
   def       lowercase_				(self					:						str		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
         UpperCAmelCase__								=  XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)

         UpperCAmelCase__								=  "left"

         # use different length sentences to test batching
         UpperCAmelCase__								=  [
             "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
             "left-padding, such as in batched generation. The output for the sequence below should be the same "
             "regardless of whether left padding is applied or not. When",
             "Hello, my dog is a little",
         ]

         UpperCAmelCase__								=  tokenizer(__UpperCAmelCase	,  return_tensors="tf"	,  padding=__UpperCAmelCase		)
         UpperCAmelCase__								=  inputs["input_ids"]

         UpperCAmelCase__								=  model.generate(input_ids=__UpperCAmelCase	,  attention_mask=inputs["attention_mask"]	,  max_new_tokens=1_2		)

         UpperCAmelCase__								=  tokenizer(sentences[0]	,  return_tensors="tf"		).input_ids
         UpperCAmelCase__								=  model.generate(input_ids=__UpperCAmelCase	,  max_new_tokens=1_2		)

         UpperCAmelCase__								=  tokenizer(sentences[1]	,  return_tensors="tf"		).input_ids
         UpperCAmelCase__								=  model.generate(input_ids=__UpperCAmelCase	,  max_new_tokens=1_2		)

         UpperCAmelCase__								=  tokenizer.batch_decode(__UpperCAmelCase	,  skip_special_tokens=__UpperCAmelCase		)
         UpperCAmelCase__								=  tokenizer.decode(output_non_padded[0]	,  skip_special_tokens=__UpperCAmelCase		)
         UpperCAmelCase__								=  tokenizer.decode(output_padded[0]	,  skip_special_tokens=__UpperCAmelCase		)

         UpperCAmelCase__								=  [
             "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
             "left-padding, such as in batched generation. The output for the sequence below should be the same "
             "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
             "a single",
             "Hello, my dog is a little bit of a shy one, but he is very friendly",
         ]
         self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)
         self.assertListEqual(__UpperCAmelCase	,  [non_padded_sentence, padded_sentence]		)




from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= Dict[str, Any]
UpperCamelCase__    							= List[Prediction]





@add_end_docstrings(UpperCAmelCase_					)
class 					A							(    UpperCAmelCase_					):


   def __init__(self					:						Any	,  *__UpperCAmelCase					:						List[str]	,  **__UpperCAmelCase					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



         super().__init__(*__UpperCAmelCase	,  **__UpperCAmelCase		)

         if self.framework == "tf":
               raise ValueError(f"""The {self.__class__} is only available in PyTorch."""		)

         requires_backends(self	,  "vision"		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)


   def       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  {}
         if "threshold" in kwargs:
               UpperCAmelCase__								=  kwargs["threshold"]
         return {}, {}, postprocess_kwargs


   def __call__(self					:						Optional[int]	,  *__UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Union[Predictions, List[Prediction]]:
         """simple docstring"""



         return super().__call__(*__UpperCAmelCase	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.IntTensor([[image.height, image.width]]		)
         UpperCAmelCase__								=  self.image_processor(images=[image]	,  return_tensors="pt"		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  self.tokenizer(text=inputs["words"]	,  boxes=inputs["boxes"]	,  return_tensors="pt"		)
         UpperCAmelCase__								=  target_size
         return inputs


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_inputs.pop("target_size"		)
         UpperCAmelCase__								=  self.model(**__UpperCAmelCase		)
         UpperCAmelCase__								=  outputs.__class__({"target_size": target_size, **outputs}		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  model_inputs["bbox"]
         return model_outputs


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict=0.9		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_outputs["target_size"]
         if self.tokenizer is not None:
               # This is a LayoutLMForTokenClassification variant.
               # The OCR got the boxes and the model classified the words.
               UpperCAmelCase__		,				UpperCAmelCase__								=  target_size[0].tolist()

               def unnormalize(__UpperCAmelCase					:						List[str]		):
                     return self._get_bounding_box(
                         torch.Tensor(
                             [
                                 (width * bbox[0] / 1_0_0_0),
                                 (height * bbox[1] / 1_0_0_0),
                                 (width * bbox[2] / 1_0_0_0),
                                 (height * bbox[3] / 1_0_0_0),
                             ]		)		)

               UpperCAmelCase__		,				UpperCAmelCase__								=  model_outputs["logits"].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
               UpperCAmelCase__								=  [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
               UpperCAmelCase__								=  [unnormalize(__UpperCAmelCase		) for bbox in model_outputs["bbox"].squeeze(0		)]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		) for vals in zip(scores.tolist()	,  __UpperCAmelCase	,  __UpperCAmelCase		) if vals[0] > threshold]
         else:
               # This is a regular ForObjectDetectionModel
               UpperCAmelCase__								=  self.image_processor.post_process_object_detection(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
               UpperCAmelCase__								=  raw_annotations[0]
               UpperCAmelCase__								=  raw_annotation["scores"]
               UpperCAmelCase__								=  raw_annotation["labels"]
               UpperCAmelCase__								=  raw_annotation["boxes"]

               UpperCAmelCase__								=  scores.tolist()
               UpperCAmelCase__								=  [self.model.config.idalabel[label.item()] for label in labels]
               UpperCAmelCase__								=  [self._get_bounding_box(__UpperCAmelCase		) for box in boxes]

               # {"scores": [...], ...} --> [{"score":x, ...}, ...]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [
                   dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		)
                   for vals in zip(raw_annotation["scores"]	,  raw_annotation["labels"]	,  raw_annotation["boxes"]		)
               ]

         return annotation





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						"torch.Tensor"		)						->    Dict[str, int]:
         """simple docstring"""



         if self.framework != "pt":
               raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."		)
         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  box.int().tolist()
         UpperCAmelCase__								=  {
             "xmin": xmin,
             "ymin": ymin,
             "xmax": xmax,
             "ymax": ymax,
         }
         return bbox



