






'''simple docstring'''

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     SCREAMING_SNAKE_CASE__     =       "pt"
elif is_tf_available():
     SCREAMING_SNAKE_CASE__     =       "tf"
else:
     SCREAMING_SNAKE_CASE__     =       "jax"






class     a_						(      __lowerCAmelCase     ,	unittest.TestCase    ):
  lowercase							=				ByTaTokenizer
  lowercase							=				False

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



        """simple docstring"""






        super().setUp()
        UpperCamelCase      = ByTaTokenizer()
        tokenizer.save_pretrained(self.tmpdirname				)

  @cached_property
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        return ByTaTokenizer.from_pretrained("""google/byt5-small"""				)

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				ByTaTokenizer:



        """simple docstring"""






        return self.tokenizer_class.from_pretrained(self.tmpdirname      ,		**lowerCamelCase__				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=20      ,		_SCREAMING_SNAKE_CASE=5				)  ->				Tuple[str, list]:



        """simple docstring"""






        UpperCamelCase      = []
        for i in range(len(lowerCamelCase__				)				):
              try:
                    UpperCamelCase      = tokenizer.decode([i]      ,		clean_up_tokenization_spaces=lowerCamelCase__				)
              except UnicodeDecodeError:
                    pass
              toks.append((i, tok)				)

        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: re.match(R"""^[ a-zA-Z]+$"""      ,		t[1]				)      ,		lowerCamelCase__				)				)
        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: [t[0]] == tokenizer.encode(t[1]      ,		add_special_tokens=lowerCamelCase__				)      ,		lowerCamelCase__				)				)
        if max_length is not None and len(lowerCamelCase__				) > max_length:
              UpperCamelCase      = toks[:max_length]
        if min_length is not None and len(lowerCamelCase__				) < min_length and len(lowerCamelCase__				) > 0:
              while len(lowerCamelCase__				) < min_length:
                    UpperCamelCase      = toks + toks
                    # toks_str = [t[1] for t in toks]
        UpperCamelCase      = [t[0] for t in toks]

        # Ensure consistency
        UpperCamelCase      = tokenizer.decode(lowerCamelCase__      ,		clean_up_tokenization_spaces=lowerCamelCase__				)
        if " " not in output_txt and len(lowerCamelCase__				) > 1:
              UpperCamelCase      = (
                  tokenizer.decode([toks_ids[0]]      ,		clean_up_tokenization_spaces=lowerCamelCase__				)
                  + ''' '''
                  + tokenizer.decode(toks_ids[1:]      ,		clean_up_tokenization_spaces=lowerCamelCase__				)
              )
        if with_prefix_space:
              UpperCamelCase      = ''' ''' + output_txt
        UpperCamelCase      = tokenizer.encode(lowerCamelCase__      ,		add_special_tokens=lowerCamelCase__				)
        return output_txt, output_ids

  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.ta_base_tokenizer
        UpperCamelCase      = tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]				)
        UpperCamelCase      = tokenizer(["""hi""", """I went to the gym""", """"""]				)
        self.assertListEqual(batch_with_eos_added["""input_ids"""]      ,		batch_without_eos_added["""input_ids"""]				)

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.ta_base_tokenizer
        UpperCamelCase      = '''Unicode €.'''
        UpperCamelCase      = tokenizer(lowerCamelCase__				)
        UpperCamelCase      = [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
        self.assertEqual(encoded["""input_ids"""]      ,		lowerCamelCase__				)

        # decoding
        UpperCamelCase      = tokenizer.decode(lowerCamelCase__				)
        self.assertEqual(lowerCamelCase__      ,		"""Unicode €.</s>"""				)

        UpperCamelCase      = tokenizer("""e è é ê ë"""				)
        UpperCamelCase      = [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
        self.assertEqual(encoded["""input_ids"""]      ,		lowerCamelCase__				)
        # decoding
        UpperCamelCase      = tokenizer.decode(lowerCamelCase__				)
        self.assertEqual(lowerCamelCase__      ,		"""e è é ê ë</s>"""				)

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""				)				)      ,		"""e è é ê ë</s>"""				)

  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.ta_base_tokenizer
        UpperCamelCase      = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        # fmt: off
        UpperCamelCase      = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
        # fmt: on
        UpperCamelCase      = tokenizer(lowerCamelCase__      ,		padding=lowerCamelCase__      ,		return_tensors=lowerCamelCase__				)
        self.assertIsInstance(lowerCamelCase__      ,		lowerCamelCase__				)

        if FRAMEWORK != "jax":
              UpperCamelCase      = list(batch.input_ids.numpy()[0]				)
        else:
              UpperCamelCase      = list(batch.input_ids.tolist()[0]				)

        self.assertListEqual(lowerCamelCase__      ,		lowerCamelCase__				)

        self.assertEqual((2, 37)      ,		batch.input_ids.shape				)
        self.assertEqual((2, 37)      ,		batch.attention_mask.shape				)

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.ta_base_tokenizer
        UpperCamelCase      = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCamelCase      = tokenizer(lowerCamelCase__      ,		padding=lowerCamelCase__      ,		return_tensors=lowerCamelCase__				)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn("""input_ids"""      ,		lowerCamelCase__				)
        self.assertIn("""attention_mask"""      ,		lowerCamelCase__				)
        self.assertNotIn("""decoder_input_ids"""      ,		lowerCamelCase__				)
        self.assertNotIn("""decoder_attention_mask"""      ,		lowerCamelCase__				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.ta_base_tokenizer
        UpperCamelCase      = [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        UpperCamelCase      = tokenizer(
            text_target=lowerCamelCase__      ,		max_length=32      ,		padding="""max_length"""      ,		truncation=lowerCamelCase__      ,		return_tensors=lowerCamelCase__				)
        self.assertEqual(32      ,		targets["""input_ids"""].shape[1]				)

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = self.ta_base_tokenizer
        UpperCamelCase      = ['''A long paragraph for summarization. </s>''']
        UpperCamelCase      = ['''Summary of the text. </s>''']
        # fmt: off
        UpperCamelCase      = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
        UpperCamelCase      = [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
        # fmt: on

        UpperCamelCase      = tokenizer(lowerCamelCase__      ,		text_target=lowerCamelCase__				)

        self.assertEqual(lowerCamelCase__      ,		batch["""input_ids"""][0]				)
        self.assertEqual(lowerCamelCase__      ,		batch["""labels"""][0]				)

  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.get_tokenizers()
        for tokenizer in tokenizers:
              with self.subTest(F"{tokenizer.__class__.__name__}"				):
                    self.assertNotEqual(tokenizer.model_max_length      ,		42				)

                    # Now let's start the test
        UpperCamelCase      = self.get_tokenizers()
        for tokenizer in tokenizers:
              with self.subTest(F"{tokenizer.__class__.__name__}"				):
                    # Isolate this from the other tests because we save additional tokens/etc
                    UpperCamelCase      = tempfile.mkdtemp()

                    UpperCamelCase      = ''' He is very happy, UNwant\u00E9d,running'''
                    UpperCamelCase      = tokenizer.encode(lowerCamelCase__      ,		add_special_tokens=lowerCamelCase__				)
                    tokenizer.save_pretrained(lowerCamelCase__				)

                    UpperCamelCase      = tokenizer.__class__.from_pretrained(lowerCamelCase__				)
                    UpperCamelCase      = after_tokenizer.encode(lowerCamelCase__      ,		add_special_tokens=lowerCamelCase__				)
                    self.assertListEqual(lowerCamelCase__      ,		lowerCamelCase__				)

                    shutil.rmtree(lowerCamelCase__				)

        UpperCamelCase      = self.get_tokenizers(model_max_length=42				)
        for tokenizer in tokenizers:
              with self.subTest(F"{tokenizer.__class__.__name__}"				):
                    # Isolate this from the other tests because we save additional tokens/etc
                    UpperCamelCase      = tempfile.mkdtemp()

                    UpperCamelCase      = ''' He is very happy, UNwant\u00E9d,running'''
                    tokenizer.add_tokens(["""bim""", """bambam"""]				)
                    UpperCamelCase      = tokenizer.additional_special_tokens
                    additional_special_tokens.append("""new_additional_special_token"""				)
                    tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}				)
                    UpperCamelCase      = tokenizer.encode(lowerCamelCase__      ,		add_special_tokens=lowerCamelCase__				)
                    tokenizer.save_pretrained(lowerCamelCase__				)

                    UpperCamelCase      = tokenizer.__class__.from_pretrained(lowerCamelCase__				)
                    UpperCamelCase      = after_tokenizer.encode(lowerCamelCase__      ,		add_special_tokens=lowerCamelCase__				)
                    self.assertListEqual(lowerCamelCase__      ,		lowerCamelCase__				)
                    self.assertIn("""new_additional_special_token"""      ,		after_tokenizer.additional_special_tokens				)
                    self.assertEqual(after_tokenizer.model_max_length      ,		42				)

                    UpperCamelCase      = tokenizer.__class__.from_pretrained(lowerCamelCase__      ,		model_max_length=43				)
                    self.assertEqual(tokenizer.model_max_length      ,		43				)

                    shutil.rmtree(lowerCamelCase__				)

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = []
        if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())				)

        if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())				)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer_utils.save_pretrained(lowerCamelCase__				)

                    with open(os.path.join(lowerCamelCase__      ,		"""special_tokens_map.json"""				)      ,		encoding="""utf-8"""				) as json_file:
                          UpperCamelCase      = json.load(lowerCamelCase__				)

                    with open(os.path.join(lowerCamelCase__      ,		"""tokenizer_config.json"""				)      ,		encoding="""utf-8"""				) as json_file:
                          UpperCamelCase      = json.load(lowerCamelCase__				)

                    UpperCamelCase      = [F"<extra_id_{i}>" for i in range(125				)]

                    UpperCamelCase      = added_tokens_extra_ids + [
                        '''an_additional_special_token'''
                    ]
                    UpperCamelCase      = added_tokens_extra_ids + [
                        '''an_additional_special_token'''
                    ]

                    with open(os.path.join(lowerCamelCase__      ,		"""special_tokens_map.json"""				)      ,		"""w"""      ,		encoding="""utf-8"""				) as outfile:
                          json.dump(lowerCamelCase__      ,		lowerCamelCase__				)
                    with open(os.path.join(lowerCamelCase__      ,		"""tokenizer_config.json"""				)      ,		"""w"""      ,		encoding="""utf-8"""				) as outfile:
                          json.dump(lowerCamelCase__      ,		lowerCamelCase__				)

                    # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                    # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                    # "special_tokens_map.json" files
                    UpperCamelCase      = tokenizer_class.from_pretrained(
                        lowerCamelCase__      ,		)
                    self.assertIn(
                        """an_additional_special_token"""      ,		tokenizer_without_change_in_init.additional_special_tokens				)
                    # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                    self.assertEqual(
                        ["""an_additional_special_token"""]      ,		tokenizer_without_change_in_init.convert_ids_to_tokens(
                            tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]				)				)      ,		)

                    # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                    UpperCamelCase      = added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""      ,		lstrip=lowerCamelCase__				)]
                    UpperCamelCase      = tokenizer_class.from_pretrained(
                        lowerCamelCase__      ,		additional_special_tokens=lowerCamelCase__      ,		)

                    self.assertIn("""a_new_additional_special_token"""      ,		tokenizer.additional_special_tokens				)
                    self.assertEqual(
                        ["""a_new_additional_special_token"""]      ,		tokenizer.convert_ids_to_tokens(
                            tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]				)				)      ,		)

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = []
        if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())				)

        if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())				)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer_utils.save_pretrained(lowerCamelCase__				)

                    UpperCamelCase      = tokenizer_class.from_pretrained(lowerCamelCase__				)

                    self.assertTrue(tokenizer.decode([255]				) == """"""				)

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = self.get_tokenizers(fast=lowerCamelCase__      ,		do_lower_case=lowerCamelCase__				)
        for tokenizer in tokenizers:
              with self.subTest(F"{tokenizer.__class__.__name__}"				):
                    UpperCamelCase      = ['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                    UpperCamelCase      = tokenizer.convert_tokens_to_string(lowerCamelCase__				)

                    self.assertIsInstance(lowerCamelCase__      ,		lowerCamelCase__				)


  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.get_tokenizers()
        for tokenizer in tokenizers:
              with self.subTest(F"{tokenizer.__class__.__name__}"				):
                    UpperCamelCase      = [
                        '''bos_token''',
                        '''eos_token''',
                        '''unk_token''',
                        '''sep_token''',
                        '''pad_token''',
                        '''cls_token''',
                        '''mask_token''',
                    ]

                    UpperCamelCase      = 0
                    UpperCamelCase      = tokenizer.convert_ids_to_tokens(
                        lowerCamelCase__      ,		skip_special_tokens=lowerCamelCase__				)

                    for attr in attributes_list:
                          setattr(lowerCamelCase__      ,		attr + """_id"""      ,		lowerCamelCase__				)
                          self.assertEqual(getattr(lowerCamelCase__      ,		lowerCamelCase__				)      ,		lowerCamelCase__				)
                          self.assertEqual(getattr(lowerCamelCase__      ,		attr + """_id"""				)      ,		lowerCamelCase__				)

                          setattr(lowerCamelCase__      ,		attr + """_id"""      ,		lowerCamelCase__				)
                          self.assertEqual(getattr(lowerCamelCase__      ,		lowerCamelCase__				)      ,		lowerCamelCase__				)
                          self.assertEqual(getattr(lowerCamelCase__      ,		attr + """_id"""				)      ,		lowerCamelCase__				)

                    setattr(lowerCamelCase__      ,		"""additional_special_tokens_ids"""      ,		[]				)
                    self.assertListEqual(getattr(lowerCamelCase__      ,		"""additional_special_tokens"""				)      ,		[]				)
                    self.assertListEqual(getattr(lowerCamelCase__      ,		"""additional_special_tokens_ids"""				)      ,		[]				)

                    setattr(lowerCamelCase__      ,		"""additional_special_tokens_ids"""      ,		[token_id_to_test_setters]				)
                    self.assertListEqual(getattr(lowerCamelCase__      ,		"""additional_special_tokens"""				)      ,		[token_to_test_setters]				)
                    self.assertListEqual(getattr(lowerCamelCase__      ,		"""additional_special_tokens_ids"""				)      ,		[token_id_to_test_setters]				)








'''simple docstring'''

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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			int:
      return [
          int(1000 * (box[0] / width)   ),
          int(1000 * (box[1] / height)   ),
          int(1000 * (box[2] / width)   ),
          int(1000 * (box[3] / height)   ),
      ]







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = None   )->			Dict:
      UpperCamelCase      = tesseract_config if tesseract_config is not None else """"""

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

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

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

      # finally, normalize the bounding boxes
      UpperCamelCase      = []
      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     a_						(      lowerCamelCase    ):
  lowercase							=				["""pixel_values"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = ""      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






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

        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = resample
        UpperCamelCase      = apply_ocr
        UpperCamelCase      = ocr_lang
        UpperCamelCase      = tesseract_config

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				np.ndarray:



        """simple docstring"""






        UpperCamelCase      = get_size_dict(_SCREAMING_SNAKE_CASE				)
        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()}"				)
        UpperCamelCase      = (size["""height"""], size["""width"""])
        return resize(_SCREAMING_SNAKE_CASE      ,		size=_SCREAMING_SNAKE_CASE      ,		resample=_SCREAMING_SNAKE_CASE      ,		data_format=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				PIL.Image.Image:



        """simple docstring"""






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

        UpperCamelCase      = make_list_of_images(_SCREAMING_SNAKE_CASE				)

        if not valid_images(_SCREAMING_SNAKE_CASE				):
              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.
        UpperCamelCase      = [to_numpy_array(_SCREAMING_SNAKE_CASE				) for image in images]

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

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

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

        UpperCamelCase      = BatchFeature(data={"""pixel_values""": images}      ,		tensor_type=_SCREAMING_SNAKE_CASE				)

        if apply_ocr:
              UpperCamelCase      = words_batch
              UpperCamelCase      = boxes_batch
        return data
