


'''simple docstring'''






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 		a__    (							a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				LongformerTokenizer
							lowercase__      :       int     						=				True
							lowercase__      :       Optional[int]     						=				LongformerTokenizerFast
							lowercase__      :       List[Any]     						=				True







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														lowerCAmelCase__	       =     [
														    '''l''',
														    '''o''',
														    '''w''',
														    '''e''',
														    '''r''',
														    '''s''',
														    '''t''',
														    '''i''',
														    '''d''',
														    '''n''',
														    '''\u0120''',
														    '''\u0120l''',
														    '''\u0120n''',
														    '''\u0120lo''',
														    '''\u0120low''',
														    '''er''',
														    '''\u0120lowest''',
														    '''\u0120newer''',
														    '''\u0120wider''',
														    '''<unk>''',
														]
														lowerCAmelCase__	       =     dict(zip(lowerCamelCase_ ,   range(len(lowerCamelCase_       )       )       )       )
														lowerCAmelCase__	       =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
														lowerCAmelCase__	       =     {'''unk_token''': '''<unk>'''}

														lowerCAmelCase__	       =     os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''vocab_file''']       )
														lowerCAmelCase__	       =     os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''merges_file''']       )
														with open(self.vocab_file ,   '''w''' ,   encoding='''utf-8'''       ) as fp:
																					fp.write(json.dumps(lowerCamelCase_       ) + '''\n'''       )
														with open(self.merges_file ,   '''w''' ,   encoding='''utf-8'''       ) as fp:
																					fp.write('''\n'''.join(lowerCamelCase_       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							Union[str, Any]:
														kwargs.update(self.special_tokens_map       )
														return self.tokenizer_class.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							Any:
														kwargs.update(self.special_tokens_map       )
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							int:
														lowerCAmelCase__	       =     '''lower newer'''
														lowerCAmelCase__	       =     '''lower newer'''
														return input_text, output_text







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__	       =     self.tokenizer_class(self.vocab_file ,   self.merges_file ,   **self.special_tokens_map       )
														lowerCAmelCase__	       =     '''lower newer'''
														lowerCAmelCase__	       =     ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
														lowerCAmelCase__	       =     tokenizer.tokenize(lowerCamelCase_       )  # , add_prefix_space=True)
														self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

														lowerCAmelCase__	       =     tokens + [tokenizer.unk_token]
														lowerCAmelCase__	       =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_       ) ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     self.get_tokenizer()

														self.assertListEqual(tokenizer.encode('''Hello world!''' ,   add_special_tokens=lowerCamelCase_       ) ,   [0, 3_14_14, 2_32, 3_28, 2]       )
														self.assertListEqual(
														    tokenizer.encode('''Hello world! cécé herlolip 418''' ,   add_special_tokens=lowerCamelCase_       ) ,   [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] ,   )







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__	       =     self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''       )

														lowerCAmelCase__	       =     tokenizer.encode('''sequence builders''' ,   add_special_tokens=lowerCamelCase_       )
														lowerCAmelCase__	       =     tokenizer.encode('''multi-sequence build''' ,   add_special_tokens=lowerCamelCase_       )

														lowerCAmelCase__	       =     tokenizer.encode(
														    '''sequence builders''' ,   add_special_tokens=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_       )
														lowerCAmelCase__	       =     tokenizer.encode(
														    '''sequence builders''' ,   '''multi-sequence build''' ,   add_special_tokens=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_       )

														lowerCAmelCase__	       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase_       )
														lowerCAmelCase__	       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase_ ,   lowerCamelCase_       )

														assert encoded_sentence == encoded_text_from_decode
														assert encoded_pair == encoded_pair_from_decode







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     self.get_tokenizer()

														lowerCAmelCase__	       =     '''Encode this sequence.'''
														lowerCAmelCase__	       =     tokenizer.byte_encoder[''' '''.encode('''utf-8'''       )[0]]

														# Testing encoder arguments
														lowerCAmelCase__	       =     tokenizer.encode(lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_       )
														lowerCAmelCase__	       =     tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
														self.assertNotEqual(lowerCamelCase_ ,   lowerCamelCase_       )

														lowerCAmelCase__	       =     tokenizer.encode(lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_       )
														lowerCAmelCase__	       =     tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
														self.assertEqual(lowerCamelCase_ ,   lowerCamelCase_       )

														tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}       )
														lowerCAmelCase__	       =     tokenizer.encode(lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
														lowerCAmelCase__	       =     tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
														self.assertNotEqual(lowerCamelCase_ ,   lowerCamelCase_       )

														# Testing spaces after special tokens
														lowerCAmelCase__	       =     '''<mask>'''
														tokenizer.add_special_tokens(
														    {'''mask_token''': AddedToken(lowerCamelCase_ ,   lstrip=lowerCamelCase_ ,   rstrip=lowerCamelCase_       )}       )  # mask token has a left space
														lowerCAmelCase__	       =     tokenizer.convert_tokens_to_ids(lowerCamelCase_       )

														lowerCAmelCase__	       =     '''Encode <mask> sequence'''
														lowerCAmelCase__	       =     '''Encode <mask>sequence'''

														lowerCAmelCase__	       =     tokenizer.encode(lowerCamelCase_       )
														lowerCAmelCase__	       =     encoded.index(lowerCamelCase_       )
														lowerCAmelCase__	       =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
														self.assertEqual(lowerCamelCase_ ,   lowerCamelCase_       )

														lowerCAmelCase__	       =     tokenizer.encode(lowerCamelCase_       )
														lowerCAmelCase__	       =     encoded.index(lowerCamelCase_       )
														lowerCAmelCase__	       =     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
														self.assertNotEqual(lowerCamelCase_ ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(lowerCamelCase_ ,   **lowerCamelCase_       )
																												lowerCAmelCase__	       =     self.tokenizer_class.from_pretrained(lowerCamelCase_ ,   **lowerCamelCase_       )
																												lowerCAmelCase__	       =     '''A, <mask> AllenNLP sentence.'''
																												lowerCAmelCase__	       =     tokenizer_r.encode_plus(lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_ ,   return_token_type_ids=lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_p.encode_plus(lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_ ,   return_token_type_ids=lowerCamelCase_       )

																												# token_type_ids should put 0 everywhere
																												self.assertEqual(sum(tokens_r['''token_type_ids''']       ) ,   sum(tokens_p['''token_type_ids''']       )       )

																												# attention_mask should put 1 everywhere, so sum over length should be 1
																												self.assertEqual(
																												    sum(tokens_r['''attention_mask''']       ) / len(tokens_r['''attention_mask''']       ) ,   sum(tokens_p['''attention_mask''']       ) / len(tokens_p['''attention_mask''']       ) ,   )

																												lowerCAmelCase__	       =     tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']       )
																												lowerCAmelCase__	       =     tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']       )

																												# Rust correctly handles the space before the mask while python doesnt
																												self.assertSequenceEqual(tokens_p['''input_ids'''] ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]       )
																												self.assertSequenceEqual(tokens_r['''input_ids'''] ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]       )

																												self.assertSequenceEqual(
																												    lowerCamelCase_ ,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )
																												self.assertSequenceEqual(
																												    lowerCamelCase_ ,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														for trim_offsets, add_prefix_space in itertools.product([True, False] ,   repeat=2       ):
																					lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																					    self.tmpdirname ,   use_fast=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_ ,   trim_offsets=lowerCamelCase_       )

																					lowerCAmelCase__	       =     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
																					lowerCAmelCase__	       =     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

																					self.assertEqual(pre_tokenizer_state['''add_prefix_space'''] ,   lowerCamelCase_       )

																					self.assertEqual(post_processor_state['''add_prefix_space'''] ,   lowerCamelCase_       )
																					self.assertEqual(post_processor_state['''trim_offsets'''] ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
														# `trim_offsets`
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																												lowerCAmelCase__	       =     '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																												lowerCAmelCase__	       =     F"""{text_of_1_token} {text_of_1_token}"""

																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																												    lowerCamelCase_ ,   use_fast=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_ ,   trim_offsets=lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_r(lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
																												self.assertEqual(encoding.offset_mapping[0] ,   (0, len(lowerCamelCase_       ))       )
																												self.assertEqual(
																												    encoding.offset_mapping[1] ,   (len(lowerCamelCase_       ) + 1, len(lowerCamelCase_       ) + 1 + len(lowerCamelCase_       )) ,   )

																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																												    lowerCamelCase_ ,   use_fast=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_ ,   trim_offsets=lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_r(lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
																												self.assertEqual(encoding.offset_mapping[0] ,   (0, len(lowerCamelCase_       ))       )
																												self.assertEqual(
																												    encoding.offset_mapping[1] ,   (len(lowerCamelCase_       ) + 1, len(lowerCamelCase_       ) + 1 + len(lowerCamelCase_       )) ,   )

																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																												    lowerCamelCase_ ,   use_fast=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_ ,   trim_offsets=lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_r(lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
																												self.assertEqual(encoding.offset_mapping[0] ,   (0, len(lowerCamelCase_       ))       )
																												self.assertEqual(
																												    encoding.offset_mapping[1] ,   (len(lowerCamelCase_       ), len(lowerCamelCase_       ) + 1 + len(lowerCamelCase_       )) ,   )

																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																												    lowerCamelCase_ ,   use_fast=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_ ,   trim_offsets=lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_r(lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
																												self.assertEqual(encoding.offset_mapping[0] ,   (0, len(lowerCamelCase_       ))       )
																												self.assertEqual(
																												    encoding.offset_mapping[1] ,   (len(lowerCamelCase_       ), len(lowerCamelCase_       ) + 1 + len(lowerCamelCase_       )) ,   )

																												lowerCAmelCase__	       =     F""" {text}"""

																												# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																												#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																												# )
																												# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																												# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																												# self.assertEqual(
																												#     encoding.offset_mapping[1],
																												#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																												# )

																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																												    lowerCamelCase_ ,   use_fast=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_ ,   trim_offsets=lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_r(lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
																												self.assertEqual(encoding.offset_mapping[0] ,   (1, 1 + len(lowerCamelCase_       ))       )
																												self.assertEqual(
																												    encoding.offset_mapping[1] ,   (1 + len(lowerCamelCase_       ) + 1, 1 + len(lowerCamelCase_       ) + 1 + len(lowerCamelCase_       )) ,   )

																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																												    lowerCamelCase_ ,   use_fast=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_ ,   trim_offsets=lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_r(lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
																												self.assertEqual(encoding.offset_mapping[0] ,   (0, 1 + len(lowerCamelCase_       ))       )
																												self.assertEqual(
																												    encoding.offset_mapping[1] ,   (1 + len(lowerCamelCase_       ), 1 + len(lowerCamelCase_       ) + 1 + len(lowerCamelCase_       )) ,   )

																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(
																												    lowerCamelCase_ ,   use_fast=lowerCamelCase_ ,   add_prefix_space=lowerCamelCase_ ,   trim_offsets=lowerCamelCase_       )
																												lowerCAmelCase__	       =     tokenizer_r(lowerCamelCase_ ,   return_offsets_mapping=lowerCamelCase_ ,   add_special_tokens=lowerCamelCase_       )
																												self.assertEqual(encoding.offset_mapping[0] ,   (0, 1 + len(lowerCamelCase_       ))       )
																												self.assertEqual(
																												    encoding.offset_mapping[1] ,   (1 + len(lowerCamelCase_       ), 1 + len(lowerCamelCase_       ) + 1 + len(lowerCamelCase_       )) ,   )



'''simple docstring'''






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

__UpperCAmelCase       =							logging.get_logger(__name__)


def  _snake_case       (					A			,   A			,   A	)						->   Optional[Any]:
							return [
							    int(1000 * (box[0] / width)	),
							    int(1000 * (box[1] / height)	),
							    int(1000 * (box[2] / width)	),
							    int(1000 * (box[3] / height)	),
							]


def  _snake_case       (					A			,   A			,   A	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     to_pil_image(A	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     pil_image.size
							lowerCAmelCase__	       =     pytesseract.image_to_data(A			,   lang=A			,   output_type='''dict'''			,   config=A	)
							lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

							# finally, normalize the bounding boxes
							lowerCAmelCase__	       =     []
							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 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				["pixel_values"]







							def __init__(		self ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = PILImageResampling.BILINEAR ,   lowerCamelCase_ = True ,   lowerCamelCase_ = 1 / 2_55 ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = "" ,   **lowerCamelCase_ ,   )	->							None:
														super().__init__(**lowerCamelCase_       )
														lowerCAmelCase__	       =     size if size is not None else {'''height''': 2_24, '''width''': 2_24}
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )

														lowerCAmelCase__	       =     do_resize
														lowerCAmelCase__	       =     size
														lowerCAmelCase__	       =     resample
														lowerCAmelCase__	       =     do_rescale
														lowerCAmelCase__	       =     rescale_value
														lowerCAmelCase__	       =     do_normalize
														lowerCAmelCase__	       =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														lowerCAmelCase__	       =     image_std if image_std is not None else IMAGENET_STANDARD_STD
														lowerCAmelCase__	       =     apply_ocr
														lowerCAmelCase__	       =     ocr_lang
														lowerCAmelCase__	       =     tesseract_config







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = PILImageResampling.BILINEAR ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )
														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()}"""       )
														lowerCAmelCase__	       =     (size['''height'''], size['''width'''])
														return resize(lowerCamelCase_ ,   size=lowerCamelCase_ ,   resample=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														return rescale(lowerCamelCase_ ,   scale=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														return normalize(lowerCamelCase_ ,   mean=lowerCamelCase_ ,   std=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_=None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = ChannelDimension.FIRST ,   **lowerCamelCase_ ,   )	->							PIL.Image.Image:
														lowerCAmelCase__	       =     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase__	       =     size if size is not None else self.size
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )
														lowerCAmelCase__	       =     resample if resample is not None else self.resample
														lowerCAmelCase__	       =     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase__	       =     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase__	       =     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase__	       =     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase__	       =     image_std if image_std is not None else self.image_std
														lowerCAmelCase__	       =     apply_ocr if apply_ocr is not None else self.apply_ocr
														lowerCAmelCase__	       =     ocr_lang if ocr_lang is not None else self.ocr_lang
														lowerCAmelCase__	       =     tesseract_config if tesseract_config is not None else self.tesseract_config

														lowerCAmelCase__	       =     make_list_of_images(lowerCamelCase_       )

														if not valid_images(lowerCamelCase_       ):
																					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.
														lowerCAmelCase__	       =     [to_numpy_array(lowerCamelCase_       ) for image in images]

														# Tesseract OCR to get words + normalized bounding boxes
														if apply_ocr:
																					requires_backends(self ,   '''pytesseract'''       )
																					lowerCAmelCase__	       =     []
																					lowerCAmelCase__	       =     []
																					for image in images:
																												lowerCAmelCase__       , lowerCAmelCase__	       =     apply_tesseract(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )
																												words_batch.append(lowerCamelCase_       )
																												boxes_batch.append(lowerCamelCase_       )

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

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

														if do_normalize:
																					lowerCAmelCase__	       =     [self.normalize(image=lowerCamelCase_ ,   mean=lowerCamelCase_ ,   std=lowerCamelCase_       ) for image in images]

														lowerCAmelCase__	       =     [to_channel_dimension_format(lowerCamelCase_ ,   lowerCamelCase_       ) for image in images]

														lowerCAmelCase__	       =     BatchFeature(data={'''pixel_values''': images} ,   tensor_type=lowerCamelCase_       )

														if apply_ocr:
																					lowerCAmelCase__	       =     words_batch
																					lowerCAmelCase__	       =     boxes_batch
														return data