import unittest

from transformers import BertGenerationConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import BertGenerationDecoder, BertGenerationEncoder


class   lowercase      :




							def __init__(  self				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Optional[Any]=13		,							_UpperCamelCase				:   int=7		,							_UpperCamelCase				:   Tuple=True		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   Union[str, Any]=99		,							_UpperCamelCase				:   Tuple=32		,							_UpperCamelCase				:   Tuple=5		,							_UpperCamelCase				:   Tuple=4		,							_UpperCamelCase				:   List[Any]=37		,							_UpperCamelCase				:   Any="gelu"		,							_UpperCamelCase				:   List[str]=0.1		,							_UpperCamelCase				:   List[str]=0.1		,							_UpperCamelCase				:   List[str]=50		,							_UpperCamelCase				:   List[Any]=0.0_2		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   Dict=None		,							)   ->			Dict:



												'''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_input_mask
												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           =    initializer_range
												SCREAMING_SNAKE_CASE           =    use_labels
												SCREAMING_SNAKE_CASE           =    scope




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



												'''simple docstring'''

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

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

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

												SCREAMING_SNAKE_CASE           =    self.get_config()

												return config, input_ids, input_mask, token_labels




							def 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''simple docstring'''

												return BertGenerationConfig(
												    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		,							is_decoder=_lowercase		,							initializer_range=self.initializer_range		,							)




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



												'''simple docstring'''

												(
												    (
												    SCREAMING_SNAKE_CASE    
												)     ,				(
												    SCREAMING_SNAKE_CASE    
												)     ,				(
												    SCREAMING_SNAKE_CASE    
												)     ,				(
												    SCREAMING_SNAKE_CASE    
												)     ,				    
												)           =    self.prepare_config_and_inputs()

												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
												SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.seq_length]		,							vocab_size=2 )

												return (
												    config,
												    input_ids,
												    input_mask,
												    token_labels,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    BertGenerationEncoder(config=_lowercase )
												model.to(_lowercase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_lowercase		,							attention_mask=_lowercase )
												SCREAMING_SNAKE_CASE           =    model(_lowercase )
												self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, self.seq_length, self.hidden_size) )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    BertGenerationEncoder(config=_lowercase )
												model.to(_lowercase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(
												    _lowercase		,							attention_mask=_lowercase		,							encoder_hidden_states=_lowercase		,							encoder_attention_mask=_lowercase		,							)
												SCREAMING_SNAKE_CASE           =    model(
												    _lowercase		,							attention_mask=_lowercase		,							encoder_hidden_states=_lowercase		,							)
												self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, self.seq_length, self.hidden_size) )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    BertGenerationDecoder(config=_lowercase ).to(_lowercase ).eval()

												# first forward pass
												SCREAMING_SNAKE_CASE           =    model(
												    _lowercase		,							attention_mask=_lowercase		,							encoder_hidden_states=_lowercase		,							encoder_attention_mask=_lowercase		,							use_cache=_lowercase		,							)
												SCREAMING_SNAKE_CASE           =    outputs.past_key_values

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

												# append to next input_ids and
												SCREAMING_SNAKE_CASE           =    torch.cat([input_ids, next_tokens]		,							dim=-1 )
												SCREAMING_SNAKE_CASE           =    torch.cat([input_mask, next_mask]		,							dim=-1 )

												SCREAMING_SNAKE_CASE           =    model(
												    _lowercase		,							attention_mask=_lowercase		,							encoder_hidden_states=_lowercase		,							encoder_attention_mask=_lowercase		,							output_hidden_states=_lowercase		,							)["hidden_states"][0]
												SCREAMING_SNAKE_CASE           =    model(
												    _lowercase		,							attention_mask=_lowercase		,							encoder_hidden_states=_lowercase		,							encoder_attention_mask=_lowercase		,							past_key_values=_lowercase		,							output_hidden_states=_lowercase		,							)["hidden_states"][0]

												# select random slice
												SCREAMING_SNAKE_CASE           =    ids_tensor((1,)		,							output_from_past.shape[-1] ).item()
												SCREAMING_SNAKE_CASE           =    output_from_no_past[:, -3:, random_slice_idx].detach()
												SCREAMING_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(_lowercase		,							_lowercase		,							atol=1e-3 ) )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    BertGenerationDecoder(_lowercase )
												model.to(_lowercase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_lowercase		,							attention_mask=_lowercase		,							labels=_lowercase )
												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.seq_length, self.vocab_size) )





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE           =    {"input_ids": input_ids, "attention_mask": input_mask}
												return config, inputs_dict



@require_torch
class   lowercase      (       _lowerCAmelCase			,    _lowerCAmelCase			,    _lowerCAmelCase			,    unittest.TestCase       ):
							lowercase__  :    Optional[Any]									=							(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
							lowercase__  :    Dict									=							(BertGenerationDecoder,) if is_torch_available() else ()
							lowercase__  :    Dict									=							(
							    {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
							    if is_torch_available()
							    else {}
							)




							def 		__snake_case(  self				:   Dict )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    BertGenerationEncoderTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_lowercase		,							hidden_size=37 )




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



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 		__snake_case(  self				:   Tuple )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_lowercase )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE           =    "bert"
												self.model_tester.create_and_check_model(_lowercase		,							_lowercase		,							_lowercase		,							_lowercase )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(*_lowercase )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(*_lowercase )




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''simple docstring'''

												(
												    (
												    SCREAMING_SNAKE_CASE    
												)     ,				(
												    SCREAMING_SNAKE_CASE    
												)     ,				(
												    SCREAMING_SNAKE_CASE    
												)     ,				(
												    SCREAMING_SNAKE_CASE    
												)     ,				(
												    SCREAMING_SNAKE_CASE    
												)     ,				(
												    SCREAMING_SNAKE_CASE    
												)     ,				    
												)           =    self.model_tester.prepare_config_and_inputs_for_decoder()

												SCREAMING_SNAKE_CASE           =    None

												self.model_tester.create_and_check_model_as_decoder(
												    _lowercase		,							_lowercase		,							_lowercase		,							_lowercase		,							_lowercase		,							_lowercase		,							)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_for_causal_lm(*_lowercase )





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
												self.assertIsNotNone(_lowercase )



@require_torch
class   lowercase      (       unittest.TestCase       ):




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
												SCREAMING_SNAKE_CASE           =    torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]] )
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    model(_lowercase )[0]
												SCREAMING_SNAKE_CASE           =    torch.Size([1, 8, 1_024] )
												self.assertEqual(output.shape		,							_lowercase )
												SCREAMING_SNAKE_CASE           =    torch.tensor(
												    [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]] )
												self.assertTrue(torch.allclose(output[:, :3, :3]		,							_lowercase		,							atol=1e-4 ) )



@require_torch
class   lowercase      (       unittest.TestCase       ):




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
												SCREAMING_SNAKE_CASE           =    torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]] )
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    model(_lowercase )[0]
												SCREAMING_SNAKE_CASE           =    torch.Size([1, 8, 50_358] )
												self.assertEqual(output.shape		,							_lowercase )
												SCREAMING_SNAKE_CASE           =    torch.tensor(
												    [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]] )
												self.assertTrue(torch.allclose(output[:, :3, :3]		,							_lowercase		,							atol=1e-4 ) )





from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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   :				str     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[str]					):
					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]					):
					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				:   float = 1 / 255		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[str] = ""		,							**_UpperCamelCase				:   Union[str, Any]		,							)   ->			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           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_value
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD
												SCREAMING_SNAKE_CASE           =    apply_ocr
												SCREAMING_SNAKE_CASE           =    ocr_lang
												SCREAMING_SNAKE_CASE           =    tesseract_config




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



												'''simple docstring'''

												return rescale(_UpperCamelCase		,							scale=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




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



												'''simple docstring'''

												return normalize(_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )





							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   ImageInput		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   Optional[Any]=None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[Union[str, TensorType]] = None		,							_UpperCamelCase				:   ChannelDimension = ChannelDimension.FIRST		,							**_UpperCamelCase				:   List[Any]		,							)   ->			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           =    do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else self.image_std
												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." )

												if do_rescale and rescale_factor is None:
																	raise ValueError("Rescale factor must be specified if do_rescale is True." )

												if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError("If do_normalize is True, image_mean and image_std must be specified." )

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

												# Tesseract OCR to get words + normalized bounding boxes
												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]

												if do_rescale:
																	SCREAMING_SNAKE_CASE           =    [self.rescale(image=_UpperCamelCase		,							scale=_UpperCamelCase ) for image in images]

												if do_normalize:
																	SCREAMING_SNAKE_CASE           =    [self.normalize(image=_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_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




