import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    OpenAIGPTConfig,
							    OpenAIGPTDoubleHeadsModel,
							    OpenAIGPTForSequenceClassification,
							    OpenAIGPTLMHeadModel,
							    OpenAIGPTModel,
							)


class 						lowercase    :




							def __init__(						self   :					int	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]=13	,							_UpperCamelCase   :					Optional[Any]=7	,							_UpperCamelCase   :					Union[str, Any]=True	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Any=True	,							_UpperCamelCase   :					int=99	,							_UpperCamelCase   :					List[str]=32	,							_UpperCamelCase   :					Optional[int]=5	,							_UpperCamelCase   :					Optional[Any]=4	,							_UpperCamelCase   :					Dict=37	,							_UpperCamelCase   :					int="gelu"	,							_UpperCamelCase   :					Optional[int]=0.1	,							_UpperCamelCase   :					Optional[Any]=0.1	,							_UpperCamelCase   :					Any=512	,							_UpperCamelCase   :					Dict=16	,							_UpperCamelCase   :					Dict=2	,							_UpperCamelCase   :					Any=0.0_2	,							_UpperCamelCase   :					List[str]=3	,							_UpperCamelCase   :					Optional[int]=4	,							_UpperCamelCase   :					Union[str, Any]=None	,							)					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       seq_length
												SCREAMING_SNAKE_CASE    			=       is_training
												SCREAMING_SNAKE_CASE    			=       use_token_type_ids
												SCREAMING_SNAKE_CASE    			=       use_labels
												SCREAMING_SNAKE_CASE    			=       vocab_size
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings
												SCREAMING_SNAKE_CASE    			=       type_vocab_size
												SCREAMING_SNAKE_CASE    			=       type_sequence_label_size
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       num_labels
												SCREAMING_SNAKE_CASE    			=       num_choices
												SCREAMING_SNAKE_CASE    			=       scope
												SCREAMING_SNAKE_CASE    			=       self.vocab_size - 1




							def 	__snake_case(						self   :					int      )					->				List[str]:



												'''simple docstring'''

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

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

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

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTConfig(
												    vocab_size=self.vocab_size	,							n_embd=self.hidden_size	,							n_layer=self.num_hidden_layers	,							n_head=self.num_attention_heads	,							n_positions=self.max_position_embeddings	,							pad_token_id=self.pad_token_id	,							)

												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,							2      )

												return (
												    config,
												    input_ids,
												    head_mask,
												    token_type_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Dict	,							*_UpperCamelCase   :					str      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTModel(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()

												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							head_mask=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )

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




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Optional[Any]	,							*_UpperCamelCase   :					str      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTLMHeadModel(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()

												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.loss.shape	,							()      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)      )




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					Union[str, Any]	,							*_UpperCamelCase   :					str      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTDoubleHeadsModel(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()

												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.loss.shape	,							()      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)      )




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					List[Any]	,							*_UpperCamelCase   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.num_labels
												SCREAMING_SNAKE_CASE    			=       OpenAIGPTForSequenceClassification(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()

												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.type_sequence_label_size      )
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)      )





							def 	__snake_case(						self   :					int      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
												(
												    (
												    SCREAMING_SNAKE_CASE   
												)			,   (
												    SCREAMING_SNAKE_CASE   
												)			,   (
												    SCREAMING_SNAKE_CASE   
												)			,   (
												    SCREAMING_SNAKE_CASE   
												)			,   (
												    SCREAMING_SNAKE_CASE   
												)			,   (
												    SCREAMING_SNAKE_CASE   
												)			,   (
												    SCREAMING_SNAKE_CASE   
												)			,      
												)    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {
												    "input_ids": input_ids,
												    "token_type_ids": token_type_ids,
												    "head_mask": head_mask,
												}

												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       a				,       a				,       unittest.TestCase     ):
							lowercase__						:       List[str]								=							(
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							lowercase__						:       int								=							(
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							lowercase__						:       Any								=							(
							    {
							        """feature-extraction""": OpenAIGPTModel,
							        """text-classification""": OpenAIGPTForSequenceClassification,
							        """text-generation""": OpenAIGPTLMHeadModel,
							        """zero-shot""": OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Optional[int]      )					->				int:



												'''simple docstring'''

												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
																	# tiny config could not be created.
																	return True

												return False




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Optional[Any]=False      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       super()._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )

												if return_labels:
																	if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																						SCREAMING_SNAKE_CASE    			=       torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)	,							dtype=torch.long	,							device=_UpperCamelCase	,							)
																						SCREAMING_SNAKE_CASE    			=       inputs_dict["labels"]
																						SCREAMING_SNAKE_CASE    			=       inputs_dict["labels"]
																						SCREAMING_SNAKE_CASE    			=       torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices)	,							dtype=torch.long	,							device=_UpperCamelCase	,							)
																						SCREAMING_SNAKE_CASE    			=       torch.zeros(
																						    self.model_tester.batch_size	,							dtype=torch.long	,							device=_UpperCamelCase      )
												return inputs_dict




							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							n_embd=37      )




							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_model(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*_UpperCamelCase      )




							def 	__snake_case(						self   :					List[str]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_double_lm_head_model(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[int]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_UpperCamelCase      )





							@slow
							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE    			=       OpenAIGPTModel.from_pretrained(_UpperCamelCase      )
																	self.assertIsNotNone(_UpperCamelCase      )



@require_torch
class 						lowercase    (     unittest.TestCase     ):




							@slow
							def 	__snake_case(						self   :					Any      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"      )
												model.to(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       torch.tensor([[481, 4_735, 544]]	,							dtype=torch.long	,							device=_UpperCamelCase      )  # the president is
												SCREAMING_SNAKE_CASE    			=       [
												    481,
												    4_735,
												    544,
												    246,
												    963,
												    870,
												    762,
												    239,
												    244,
												    40_477,
												    244,
												    249,
												    719,
												    881,
												    487,
												    544,
												    240,
												    244,
												    603,
												    481,
												]  # the president is a very good man. " \n " i\'m sure he is, " said the

												SCREAMING_SNAKE_CASE    			=       model.generate(_UpperCamelCase	,							do_sample=_UpperCamelCase      )
												self.assertListEqual(output_ids[0].tolist()	,							_UpperCamelCase      )

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
							import PIL

# soft dependency
if is_pytesseract_available():
							import pytesseract

_lowerCamelCase					:   Union[str, Any]							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Optional[Any]  ):
					return [
					    int(1_0_0_0 * (box[0] / width)  ),
					    int(1_0_0_0 * (box[1] / height)  ),
					    int(1_0_0_0 * (box[2] / width)  ),
					    int(1_0_0_0 * (box[3] / height)  ),
					]





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							Optional[str]      , UpperCAmelCase__     :							Optional[str] = None  ):
					SCREAMING_SNAKE_CASE    			=       tesseract_config if tesseract_config is not None else ""

					# apply OCR
					SCREAMING_SNAKE_CASE    			=       to_pil_image(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       pil_image.size
					SCREAMING_SNAKE_CASE    			=       pytesseract.image_to_data(UpperCAmelCase__      , lang=UpperCAmelCase__      , output_type="dict"      , config=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       data["text"], data["left"], data["top"], data["width"], data["height"]

					# filter empty words and corresponding coordinates
					SCREAMING_SNAKE_CASE    			=       [idx for idx, word in enumerate(UpperCAmelCase__  ) if not word.strip()]
					SCREAMING_SNAKE_CASE    			=       [word for idx, word in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE    			=       [coord for idx, coord in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE    			=       [coord for idx, coord in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE    			=       [coord for idx, coord in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE    			=       [coord for idx, coord in enumerate(UpperCAmelCase__  ) if idx not in irrelevant_indices]

					# turn coordinates into (left, top, left+width, top+height) format
					SCREAMING_SNAKE_CASE    			=       []
					for x, y, w, h in zip(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  ):
										SCREAMING_SNAKE_CASE    			=       [x, y, x + w, y + h]
										actual_boxes.append(UpperCAmelCase__  )

					# finally, normalize the bounding boxes
					SCREAMING_SNAKE_CASE    			=       []
					for box in actual_boxes:
										normalized_boxes.append(normalize_box(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )  )

					assert len(UpperCAmelCase__  ) == len(UpperCAmelCase__  ), "Not as many words as there are bounding boxes"

					return words, normalized_boxes


class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							["""pixel_values"""]




							def __init__(						self   :					int	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Dict[str, int] = None	,							_UpperCamelCase   :					PILImageResampling = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[str] = ""	,							**_UpperCamelCase   :					Optional[int]	,							)					->				None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"height": 224, "width": 224}
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       resample
												SCREAMING_SNAKE_CASE    			=       apply_ocr
												SCREAMING_SNAKE_CASE    			=       ocr_lang
												SCREAMING_SNAKE_CASE    			=       tesseract_config




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					Dict[str, int]	,							_UpperCamelCase   :					PILImageResampling = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					Any	,							)					->				np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )
												if "height" not in size or "width" not in size:
																	raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"      )
												SCREAMING_SNAKE_CASE    			=       (size["height"], size["width"])
												return resize(_UpperCamelCase	,							size=_UpperCamelCase	,							resample=_UpperCamelCase	,							data_format=_UpperCamelCase	,							**_UpperCamelCase      )





							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					ImageInput	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					Dict[str, int] = None	,							_UpperCamelCase   :					PILImageResampling = None	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							_UpperCamelCase   :					ChannelDimension = ChannelDimension.FIRST	,							**_UpperCamelCase   :					str	,							)					->				PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE    			=       size if size is not None else self.size
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE    			=       apply_ocr if apply_ocr is not None else self.apply_ocr
												SCREAMING_SNAKE_CASE    			=       ocr_lang if ocr_lang is not None else self.ocr_lang
												SCREAMING_SNAKE_CASE    			=       tesseract_config if tesseract_config is not None else self.tesseract_config

												SCREAMING_SNAKE_CASE    			=       make_list_of_images(_UpperCamelCase      )

												if not valid_images(_UpperCamelCase      ):
																	raise ValueError(
																	    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																	    "torch.Tensor, tf.Tensor or jax.ndarray."      )

												if do_resize and size is None:
																	raise ValueError("Size must be specified if do_resize is True."      )

												# All transformations expect numpy arrays.
												SCREAMING_SNAKE_CASE    			=       [to_numpy_array(_UpperCamelCase      ) for image in images]

												if apply_ocr:
																	requires_backends(self	,							"pytesseract"      )
																	SCREAMING_SNAKE_CASE    			=       []
																	SCREAMING_SNAKE_CASE    			=       []
																	for image in images:
																						SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       apply_tesseract(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																						words_batch.append(_UpperCamelCase      )
																						boxes_batch.append(_UpperCamelCase      )

												if do_resize:
																	SCREAMING_SNAKE_CASE    			=       [self.resize(image=_UpperCamelCase	,							size=_UpperCamelCase	,							resample=_UpperCamelCase      ) for image in images]

												# flip color channels from RGB to BGR (as Detectron2 requires this)
												SCREAMING_SNAKE_CASE    			=       [flip_channel_order(_UpperCamelCase      ) for image in images]
												SCREAMING_SNAKE_CASE    			=       [to_channel_dimension_format(_UpperCamelCase	,							_UpperCamelCase      ) for image in images]

												SCREAMING_SNAKE_CASE    			=       BatchFeature(data={"pixel_values": images}	,							tensor_type=_UpperCamelCase      )

												if apply_ocr:
																	SCREAMING_SNAKE_CASE    			=       words_batch
																	SCREAMING_SNAKE_CASE    			=       boxes_batch
												return data




