





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						_SCREAMING_SNAKE_CASE      	=		"""platform"""

						import jax
						import jax.numpy as jnp

						from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    shift_tokens_right,
						)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=None   , __a=None   , __a=None   , __a=None   , __a=None   , __a=None   , ):
	if attention_mask is None:
		snake_case_		:			Optional[int]											=	np.where(input_ids != config.pad_token_id   , 1   , 0   )
	if decoder_attention_mask is None:
		snake_case_		:			Dict											=	np.where(decoder_input_ids != config.pad_token_id   , 1   , 0   )
	if head_mask is None:
		snake_case_		:			List[str]											=	np.ones((config.encoder_layers, config.encoder_attention_heads)   )
	if decoder_head_mask is None:
		snake_case_		:			List[Any]											=	np.ones((config.decoder_layers, config.decoder_attention_heads)   )
	if cross_attn_head_mask is None:
		snake_case_		:			List[str]											=	np.ones((config.decoder_layers, config.decoder_attention_heads)   )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	}
class SCREAMING_SNAKE_CASE_						:






						def __init__(      self      :      Optional[int]		,   _A      :      List[str]		,   _A      :      Union[str, Any]=13		,   _A      :      Union[str, Any]=7		,   _A      :      Union[str, Any]=True		,   _A      :      List[Any]=False		,   _A      :      Dict=99		,   _A      :      Any=16		,   _A      :      Tuple=2		,   _A      :      Dict=4		,   _A      :      str=4		,   _A      :      List[Any]="gelu"		,   _A      :      int=0.1		,   _A      :      Optional[Any]=0.1		,   _A      :      List[str]=32		,   _A      :      List[str]=2		,   _A      :      List[Any]=1		,   _A      :      List[str]=0		,   _A      :      List[Any]=0.0_2		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			Any											=	parent
							snake_case_		:			Tuple											=	batch_size
							snake_case_		:			Optional[Any]											=	seq_length
							snake_case_		:			Dict											=	is_training
							snake_case_		:			Optional[Any]											=	use_labels
							snake_case_		:			int											=	vocab_size
							snake_case_		:			Optional[Any]											=	hidden_size
							snake_case_		:			Optional[Any]											=	num_hidden_layers
							snake_case_		:			Dict											=	num_attention_heads
							snake_case_		:			Tuple											=	intermediate_size
							snake_case_		:			Optional[Any]											=	hidden_act
							snake_case_		:			Optional[Any]											=	hidden_dropout_prob
							snake_case_		:			Dict											=	attention_probs_dropout_prob
							snake_case_		:			Optional[int]											=	max_position_embeddings
							snake_case_		:			List[Any]											=	eos_token_id
							snake_case_		:			List[str]											=	pad_token_id
							snake_case_		:			int											=	bos_token_id
							snake_case_		:			List[Any]											=	initializer_range






						def UpperCAmelCase_				(      self      :      Dict	)							->    int:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,   self.vocab_size	)		,   3		,   self.vocab_size	)
							snake_case_		:			List[str]											=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,   dtype=np.intaa	))		,   -1	)

							snake_case_		:			List[str]											=	shift_tokens_right(_A		,   1		,   2	)

							snake_case_		:			Optional[Any]											=	BlenderbotSmallConfig(
							    vocab_size=self.vocab_size		,   d_model=self.hidden_size		,   encoder_layers=self.num_hidden_layers		,   decoder_layers=self.num_hidden_layers		,   encoder_attention_heads=self.num_attention_heads		,   decoder_attention_heads=self.num_attention_heads		,   encoder_ffn_dim=self.intermediate_size		,   decoder_ffn_dim=self.intermediate_size		,   dropout=self.hidden_dropout_prob		,   attention_dropout=self.attention_probs_dropout_prob		,   max_position_embeddings=self.max_position_embeddings		,   eos_token_id=self.eos_token_id		,   bos_token_id=self.bos_token_id		,   pad_token_id=self.pad_token_id		,   initializer_range=self.initializer_range		,   use_cache=_A		,   )
							snake_case_		:			Optional[int]											=	prepare_blenderbot_inputs_dict(_A		,   _A		,   _A	)
							return config, inputs_dict






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







							"""simple docstring"""


							snake_case_						,snake_case_		:			int											=	self.prepare_config_and_inputs()
							return config, inputs_dict






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







							"""simple docstring"""


							snake_case_		:			List[str]											=	20
							snake_case_		:			Union[str, Any]											=	model_class_name(_A	)

							snake_case_		:			Union[str, Any]											=	model.encode(inputs_dict['input_ids']	)

							snake_case_						,snake_case_		:			Optional[Any]											=	(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							snake_case_		:			List[str]											=	model.init_cache(decoder_input_ids.shape[0]		,   _A		,   _A	)
							snake_case_		:			List[str]											=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,   dtype='i4'	)

							snake_case_		:			Any											=	jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )
							snake_case_		:			Any											=	model.decode(
							    decoder_input_ids[:, :-1]		,   _A		,   decoder_attention_mask=_A		,   past_key_values=_A		,   decoder_position_ids=_A		,   )

							snake_case_		:			Union[str, Any]											=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype='i4'	)
							snake_case_		:			Dict											=	model.decode(
							    decoder_input_ids[:, -1:]		,   _A		,   decoder_attention_mask=_A		,   past_key_values=outputs_cache.past_key_values		,   decoder_position_ids=_A		,   )

							snake_case_		:			Dict											=	model.decode(_A		,   _A	)

							snake_case_		:			Dict											=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
							self.parent.assertTrue(diff < 1E-3		,   msg=F"""Max diff is {diff}"""	)






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      Tuple		,   _A      :      int		,   _A      :      int	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	20
							snake_case_		:			Any											=	model_class_name(_A	)

							snake_case_		:			Union[str, Any]											=	model.encode(inputs_dict['input_ids']	)

							snake_case_						,snake_case_		:			str											=	(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							snake_case_		:			str											=	jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
							    ]		,   axis=-1		,   )

							snake_case_		:			Union[str, Any]											=	model.init_cache(decoder_input_ids.shape[0]		,   _A		,   _A	)
							snake_case_		:			Union[str, Any]											=	jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )

							snake_case_		:			List[Any]											=	model.decode(
							    decoder_input_ids[:, :-1]		,   _A		,   decoder_attention_mask=_A		,   past_key_values=_A		,   decoder_position_ids=_A		,   )
							snake_case_		:			Optional[int]											=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype='i4'	)
							snake_case_		:			Dict											=	model.decode(
							    decoder_input_ids[:, -1:]		,   _A		,   past_key_values=outputs_cache.past_key_values		,   decoder_attention_mask=_A		,   decoder_position_ids=_A		,   )

							snake_case_		:			Optional[Any]											=	model.decode(_A		,   _A		,   decoder_attention_mask=_A	)

							snake_case_		:			Optional[Any]											=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
							self.parent.assertTrue(diff < 1E-3		,   msg=F"""Max diff is {diff}"""	)
@require_flax
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):
						__magic_name__:			Union[str, Any]       =       99






						def UpperCAmelCase_				(      self      :      int	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ]		,   dtype=np.intaa		,   )

							snake_case_		:			Optional[int]											=	input_ids.shape[0]
							snake_case_		:			int											=	BlenderbotSmallConfig(
							    vocab_size=self.vocab_size		,   d_model=24		,   encoder_layers=2		,   decoder_layers=2		,   encoder_attention_heads=2		,   decoder_attention_heads=2		,   encoder_ffn_dim=32		,   decoder_ffn_dim=32		,   max_position_embeddings=48		,   eos_token_id=2		,   pad_token_id=1		,   bos_token_id=0		,   )
							return config, input_ids, batch_size






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







							"""simple docstring"""


							snake_case_						,snake_case_						,snake_case_		:			List[str]											=	self._get_config_and_data()
							snake_case_		:			Tuple											=	FlaxBlenderbotSmallForConditionalGeneration(_A	)
							snake_case_		:			Union[str, Any]											=	lm_model(input_ids=_A	)
							snake_case_		:			int											=	(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['logits'].shape		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			int											=	BlenderbotSmallConfig(
							    vocab_size=self.vocab_size		,   d_model=14		,   encoder_layers=2		,   decoder_layers=2		,   encoder_attention_heads=2		,   decoder_attention_heads=2		,   encoder_ffn_dim=8		,   decoder_ffn_dim=8		,   max_position_embeddings=48		,   )
							snake_case_		:			Dict											=	FlaxBlenderbotSmallForConditionalGeneration(_A	)
							snake_case_		:			Optional[Any]											=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,   dtype=np.intaa	)
							snake_case_		:			List[Any]											=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,   dtype=np.intaa	)
							snake_case_		:			List[str]											=	lm_model(input_ids=_A		,   decoder_input_ids=_A	)
							snake_case_		:			Optional[Any]											=	(*summary.shape, config.vocab_size)
							self.assertEqual(outputs['logits'].shape		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,   dtype=np.intaa	)
							snake_case_		:			str											=	shift_tokens_right(_A		,   1		,   2	)
							snake_case_		:			Dict											=	np.equal(_A		,   1	).astype(np.floataa	).sum()
							snake_case_		:			int											=	np.equal(_A		,   1	).astype(np.floataa	).sum()
							self.assertEqual(shifted.shape		,   input_ids.shape	)
							self.assertEqual(_A		,   n_pad_before - 1	)
							self.assertTrue(np.equal(shifted[:, 0]		,   2	).all()	)
@require_flax
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase       ,				snake_case_					):
						__magic_name__:			Dict       =       True
						__magic_name__:			List[Any]       =       (
						    (
						        FlaxBlenderbotSmallModel,
						        FlaxBlenderbotSmallForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						__magic_name__:			Optional[int]       =       (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






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







							"""simple docstring"""


							snake_case_		:			Any											=	FlaxBlenderbotSmallModelTester(self	)






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







							"""simple docstring"""


							snake_case_						,snake_case_		:			Union[str, Any]											=	self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward(_A		,   _A		,   _A	)






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







							"""simple docstring"""


							snake_case_						,snake_case_		:			Tuple											=	self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward_with_attn_mask(_A		,   _A		,   _A	)






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







							"""simple docstring"""


							snake_case_						,snake_case_		:			List[str]											=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__	):
									snake_case_		:			Union[str, Any]											=	self._prepare_for_class(_A		,   _A	)
									snake_case_		:			List[Any]											=	model_class(_A	)

									@jax.jit
									def encode_jitted(_A      :      Any		,   _A      :      Any=None		,   **_A      :      Union[str, Any]	):
										return model.encode(input_ids=_A		,   attention_mask=_A	)

									with self.subTest('JIT Enabled'	):
										snake_case_		:			Optional[Any]											=	encode_jitted(**_A	).to_tuple()

									with self.subTest('JIT Disabled'	):
										with jax.disable_jit():
											snake_case_		:			Dict											=	encode_jitted(**_A	).to_tuple()

									self.assertEqual(len(_A	)		,   len(_A	)	)
									for jitted_output, output in zip(_A		,   _A	):
										self.assertEqual(jitted_output.shape		,   output.shape	)






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







							"""simple docstring"""


							snake_case_						,snake_case_		:			Optional[Any]											=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__	):
									snake_case_		:			Tuple											=	model_class(_A	)
									snake_case_		:			Optional[int]											=	model.encode(inputs_dict['input_ids']		,   inputs_dict['attention_mask']	)

									snake_case_		:			List[Any]											=	{
									    'decoder_input_ids': inputs_dict['decoder_input_ids'],
									    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
									    'encoder_outputs': encoder_outputs,
									}

									@jax.jit
									def decode_jitted(_A      :      Tuple		,   _A      :      List[Any]		,   _A      :      List[str]	):
										return model.decode(
										    decoder_input_ids=_A		,   decoder_attention_mask=_A		,   encoder_outputs=_A		,   )

									with self.subTest('JIT Enabled'	):
										snake_case_		:			int											=	decode_jitted(**_A	).to_tuple()

									with self.subTest('JIT Disabled'	):
										with jax.disable_jit():
											snake_case_		:			List[Any]											=	decode_jitted(**_A	).to_tuple()

									self.assertEqual(len(_A	)		,   len(_A	)	)
									for jitted_output, output in zip(_A		,   _A	):
										self.assertEqual(jitted_output.shape		,   output.shape	)






						@slow
						def UpperCAmelCase_				(      self      :      Any	)							->    int:







							"""simple docstring"""


							for model_class_name in self.all_model_classes:
								snake_case_		:			Tuple											=	model_class_name.from_pretrained('facebook/blenderbot_small-90M'	)
								# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
								snake_case_		:			Tuple											=	np.ones((1, 1)	) * model.config.eos_token_id
								snake_case_		:			Tuple											=	model(_A	)
								self.assertIsNotNone(_A	)








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

_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	return [
	    int(10_00 * (box[0] / width)   ),
	    int(10_00 * (box[1] / height)   ),
	    int(10_00 * (box[2] / width)   ),
	    int(10_00 * (box[3] / height)   ),
	]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a = None   ):
	snake_case_		:			int											=	tesseract_config if tesseract_config is not None else ''

	# apply OCR
	snake_case_		:			str											=	to_pil_image(__a   )
	snake_case_						,snake_case_		:			str											=	pil_image.size
	snake_case_		:			str											=	pytesseract.image_to_data(__a   , lang=__a   , output_type='dict'   , config=__a   )
	snake_case_						,snake_case_						,snake_case_						,snake_case_						,snake_case_		:			Dict											=	data['text'], data['left'], data['top'], data['width'], data['height']

	# filter empty words and corresponding coordinates
	snake_case_		:			str											=	[idx for idx, word in enumerate(__a   ) if not word.strip()]
	snake_case_		:			Optional[Any]											=	[word for idx, word in enumerate(__a   ) if idx not in irrelevant_indices]
	snake_case_		:			Any											=	[coord for idx, coord in enumerate(__a   ) if idx not in irrelevant_indices]
	snake_case_		:			Any											=	[coord for idx, coord in enumerate(__a   ) if idx not in irrelevant_indices]
	snake_case_		:			List[Any]											=	[coord for idx, coord in enumerate(__a   ) if idx not in irrelevant_indices]
	snake_case_		:			int											=	[coord for idx, coord in enumerate(__a   ) if idx not in irrelevant_indices]

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

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

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

	return words, normalized_boxes


class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       ["pixel_values"]






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







							"""simple docstring"""


							super().__init__(**_A	)
							snake_case_		:			Optional[int]											=	size if size is not None else {'height': 224, 'width': 224}
							snake_case_		:			List[str]											=	get_size_dict(_A	)

							snake_case_		:			int											=	do_resize
							snake_case_		:			int											=	size
							snake_case_		:			Any											=	resample
							snake_case_		:			List[str]											=	apply_ocr
							snake_case_		:			str											=	ocr_lang
							snake_case_		:			Dict											=	tesseract_config






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







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	get_size_dict(_A	)
							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()}"""	)
							snake_case_		:			Optional[Any]											=	(size['height'], size['width'])
							return resize(_A		,   size=_A		,   resample=_A		,   data_format=_A		,   **_A	)






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







							"""simple docstring"""


							snake_case_		:			Dict											=	do_resize if do_resize is not None else self.do_resize
							snake_case_		:			Optional[Any]											=	size if size is not None else self.size
							snake_case_		:			Union[str, Any]											=	get_size_dict(_A	)
							snake_case_		:			Dict											=	resample if resample is not None else self.resample
							snake_case_		:			Tuple											=	apply_ocr if apply_ocr is not None else self.apply_ocr
							snake_case_		:			int											=	ocr_lang if ocr_lang is not None else self.ocr_lang
							snake_case_		:			int											=	tesseract_config if tesseract_config is not None else self.tesseract_config

							snake_case_		:			str											=	make_list_of_images(_A	)

							if not valid_images(_A	):
								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.
							snake_case_		:			List[str]											=	[to_numpy_array(_A	) for image in images]

							if apply_ocr:
								requires_backends(self		,   'pytesseract'	)
								snake_case_		:			str											=	[]
								snake_case_		:			List[Any]											=	[]
								for image in images:
									snake_case_						,snake_case_		:			Tuple											=	apply_tesseract(_A		,   _A		,   _A	)
									words_batch.append(_A	)
									boxes_batch.append(_A	)

							if do_resize:
								snake_case_		:			Tuple											=	[self.resize(image=_A		,   size=_A		,   resample=_A	) for image in images]

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							snake_case_		:			int											=	[flip_channel_order(_A	) for image in images]
							snake_case_		:			Any											=	[to_channel_dimension_format(_A		,   _A	) for image in images]

							snake_case_		:			List[str]											=	BatchFeature(data={'pixel_values': images}		,   tensor_type=_A	)

							if apply_ocr:
								snake_case_		:			Dict											=	words_batch
								snake_case_		:			List[str]											=	boxes_batch
							return data

