


'''simple docstring'''







import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


A__:    str					    =       get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
A__:    Any					    =       get_tests_dir('''fixtures/vocab.json''')
A__:    List[str]					    =       get_tests_dir('''fixtures''')







class A__       (						unittest.TestCase      ):
      __UpperCamelCase       :  Any	       =							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






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




             '''simple docstring'''
             _a    : int     					=0






      def __UpperCAmelCase       ( self			:Dict	)       ->					Tuple:




             '''simple docstring'''
             _a    : Tuple     					=AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""	)
             self.assertIsInstance(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)






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




             '''simple docstring'''
             with tempfile.TemporaryDirectory() as tmpdirname:
                    _a    : Any     					=WavaVecaConfig()
                    _a    : Union[str, Any]     					=AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""	)

                    # save in new folder
                    model_config.save_pretrained(SCREAMING_SNAKE_CASE	)
                    processor.save_pretrained(SCREAMING_SNAKE_CASE	)

                    _a    : str     					=AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)

             self.assertIsInstance(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)






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




             '''simple docstring'''
             with tempfile.TemporaryDirectory() as tmpdirname:
                    # copy relevant files
                    copyfile(SCREAMING_SNAKE_CASE     ,						os.path.join(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)	)
                    copyfile(SCREAMING_SNAKE_CASE     ,						os.path.join(SCREAMING_SNAKE_CASE     ,						"""vocab.json"""	)	)

                    _a    : Optional[int]     					=AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)

             self.assertIsInstance(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)






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




             '''simple docstring'''
             with tempfile.TemporaryDirectory() as tmpdirname:
                    _a    : Tuple     					=WavaVecaFeatureExtractor()
                    _a    : str     					=AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""	)

                    _a    : Union[str, Any]     					=WavaVecaProcessor(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)

                    # save in new folder
                    processor.save_pretrained(SCREAMING_SNAKE_CASE	)

                    # drop `processor_class` in tokenizer
                    with open(os.path.join(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)     ,						"""r"""	) as f:
                           _a    : Any     					=json.load(SCREAMING_SNAKE_CASE	)
                           config_dict.pop("""processor_class"""	)

                    with open(os.path.join(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)     ,						"""w"""	) as f:
                           f.write(json.dumps(SCREAMING_SNAKE_CASE	)	)

                    _a    : List[Any]     					=AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)

             self.assertIsInstance(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:int	)       ->					Any:




             '''simple docstring'''
             with tempfile.TemporaryDirectory() as tmpdirname:
                    _a    : List[Any]     					=WavaVecaFeatureExtractor()
                    _a    : Tuple     					=AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""	)

                    _a    : int     					=WavaVecaProcessor(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)

                    # save in new folder
                    processor.save_pretrained(SCREAMING_SNAKE_CASE	)

                    # drop `processor_class` in feature extractor
                    with open(os.path.join(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)     ,						"""r"""	) as f:
                           _a    : List[Any]     					=json.load(SCREAMING_SNAKE_CASE	)
                           config_dict.pop("""processor_class"""	)

                    with open(os.path.join(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)     ,						"""w"""	) as f:
                           f.write(json.dumps(SCREAMING_SNAKE_CASE	)	)

                    _a    : List[str]     					=AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)

             self.assertIsInstance(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)






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




             '''simple docstring'''
             with tempfile.TemporaryDirectory() as tmpdirname:
                    _a    : Optional[int]     					=WavaVecaConfig(processor_class="""Wav2Vec2Processor"""	)
                    model_config.save_pretrained(SCREAMING_SNAKE_CASE	)
                    # copy relevant files
                    copyfile(SCREAMING_SNAKE_CASE     ,						os.path.join(SCREAMING_SNAKE_CASE     ,						"""vocab.json"""	)	)
                    # create emtpy sample processor
                    with open(os.path.join(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)     ,						"""w"""	) as f:
                           f.write("""{}"""	)

                    _a    : str     					=AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)

             self.assertIsInstance(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)






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




             '''simple docstring'''
             # If remote code is not set, we will time out when asking whether to load the model.
             with self.assertRaises(SCREAMING_SNAKE_CASE	):
                    _a    : Union[str, Any]     					=AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""	)
             # If remote code is disabled, we can't load this config.
             with self.assertRaises(SCREAMING_SNAKE_CASE	):
                    _a    : Any     					=AutoProcessor.from_pretrained(
                        """hf-internal-testing/test_dynamic_processor"""     ,						trust_remote_code=SCREAMING_SNAKE_CASE	)

             _a    : Dict     					=AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""     ,						trust_remote_code=SCREAMING_SNAKE_CASE	)
             self.assertTrue(processor.special_attribute_present	)
             self.assertEqual(processor.__class__.__name__     ,						"""NewProcessor"""	)

             _a    : Optional[int]     					=processor.feature_extractor
             self.assertTrue(feature_extractor.special_attribute_present	)
             self.assertEqual(feature_extractor.__class__.__name__     ,						"""NewFeatureExtractor"""	)

             _a    : str     					=processor.tokenizer
             self.assertTrue(tokenizer.special_attribute_present	)
             if is_tokenizers_available():
                    self.assertEqual(tokenizer.__class__.__name__     ,						"""NewTokenizerFast"""	)

                    # Test we can also load the slow version
                    _a    : Optional[Any]     					=AutoProcessor.from_pretrained(
                        """hf-internal-testing/test_dynamic_processor"""     ,						trust_remote_code=SCREAMING_SNAKE_CASE     ,						use_fast=SCREAMING_SNAKE_CASE	)
                    _a    : List[Any]     					=new_processor.tokenizer
                    self.assertTrue(new_tokenizer.special_attribute_present	)
                    self.assertEqual(new_tokenizer.__class__.__name__     ,						"""NewTokenizer"""	)
             else:
                    self.assertEqual(tokenizer.__class__.__name__     ,						"""NewTokenizer"""	)






      def __UpperCAmelCase       ( self			:Tuple	)       ->					Dict:




             '''simple docstring'''
             try:
                    AutoConfig.register("""custom"""     ,						SCREAMING_SNAKE_CASE	)
                    AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                    AutoTokenizer.register(SCREAMING_SNAKE_CASE     ,						slow_tokenizer_class=SCREAMING_SNAKE_CASE	)
                    AutoProcessor.register(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                    # Trying to register something existing in the Transformers library will raise an error
                    with self.assertRaises(SCREAMING_SNAKE_CASE	):
                           AutoProcessor.register(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)

                    # Now that the config is registered, it can be used as any other config with the auto-API
                    _a    : List[str]     					=CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE	)

                    with tempfile.TemporaryDirectory() as tmp_dir:
                           _a    : Optional[int]     					=os.path.join(SCREAMING_SNAKE_CASE     ,						"""vocab.txt"""	)
                           with open(SCREAMING_SNAKE_CASE     ,						"""w"""     ,						encoding="""utf-8"""	) as vocab_writer:
                                  vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]	)	)
                           _a    : int     					=CustomTokenizer(SCREAMING_SNAKE_CASE	)

                    _a    : Union[str, Any]     					=CustomProcessor(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)

                    with tempfile.TemporaryDirectory() as tmp_dir:
                           processor.save_pretrained(SCREAMING_SNAKE_CASE	)
                           _a    : List[str]     					=AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)
                           self.assertIsInstance(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)

             finally:
                    if "custom" in CONFIG_MAPPING._extra_content:
                           del CONFIG_MAPPING._extra_content["custom"]
                    if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                           del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
                    if CustomConfig in TOKENIZER_MAPPING._extra_content:
                           del TOKENIZER_MAPPING._extra_content[CustomConfig]
                    if CustomConfig in PROCESSOR_MAPPING._extra_content:
                           del PROCESSOR_MAPPING._extra_content[CustomConfig]






      def __UpperCAmelCase       ( self			:Tuple	)       ->					str:




             '''simple docstring'''







             class A__       (						UpperCAmelCase__      ):
                   __UpperCamelCase       :  str	       =							False







             class A__       (						UpperCAmelCase__      ):
                   __UpperCamelCase       :  Optional[int]	       =							False







             class A__       (						UpperCAmelCase__      ):
                   __UpperCamelCase       :  List[Any]	       =							"AutoFeatureExtractor"
                   __UpperCamelCase       :  Optional[int]	       =							"AutoTokenizer"
                   __UpperCamelCase       :  Tuple	       =							False

             try:
                    AutoConfig.register("""custom"""     ,						SCREAMING_SNAKE_CASE	)
                    AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                    AutoTokenizer.register(SCREAMING_SNAKE_CASE     ,						slow_tokenizer_class=SCREAMING_SNAKE_CASE	)
                    AutoProcessor.register(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                    # If remote code is not set, the default is to use local classes.
                    _a    : Dict     					=AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""	)
                    self.assertEqual(processor.__class__.__name__     ,						"""NewProcessor"""	)
                    self.assertFalse(processor.special_attribute_present	)
                    self.assertFalse(processor.feature_extractor.special_attribute_present	)
                    self.assertFalse(processor.tokenizer.special_attribute_present	)

                    # If remote code is disabled, we load the local ones.
                    _a    : Optional[int]     					=AutoProcessor.from_pretrained(
                        """hf-internal-testing/test_dynamic_processor"""     ,						trust_remote_code=SCREAMING_SNAKE_CASE	)
                    self.assertEqual(processor.__class__.__name__     ,						"""NewProcessor"""	)
                    self.assertFalse(processor.special_attribute_present	)
                    self.assertFalse(processor.feature_extractor.special_attribute_present	)
                    self.assertFalse(processor.tokenizer.special_attribute_present	)

                    # If remote is enabled, we load from the Hub.
                    _a    : Dict     					=AutoProcessor.from_pretrained(
                        """hf-internal-testing/test_dynamic_processor"""     ,						trust_remote_code=SCREAMING_SNAKE_CASE	)
                    self.assertEqual(processor.__class__.__name__     ,						"""NewProcessor"""	)
                    self.assertTrue(processor.special_attribute_present	)
                    self.assertTrue(processor.feature_extractor.special_attribute_present	)
                    self.assertTrue(processor.tokenizer.special_attribute_present	)

             finally:
                    if "custom" in CONFIG_MAPPING._extra_content:
                           del CONFIG_MAPPING._extra_content["custom"]
                    if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                           del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
                    if CustomConfig in TOKENIZER_MAPPING._extra_content:
                           del TOKENIZER_MAPPING._extra_content[CustomConfig]
                    if CustomConfig in PROCESSOR_MAPPING._extra_content:
                           del PROCESSOR_MAPPING._extra_content[CustomConfig]






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




             '''simple docstring'''
             _a    : Dict     					=AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""	)
             self.assertEqual(processor.__class__.__name__     ,						"""BertTokenizerFast"""	)






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




             '''simple docstring'''
             _a    : Tuple     					=AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""	)
             self.assertEqual(processor.__class__.__name__     ,						"""ConvNextImageProcessor"""	)







@is_staging_test
class A__       (						unittest.TestCase      ):
      __UpperCamelCase       :  str	       =							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






      @classmethod
      def __UpperCAmelCase       ( cls			:Optional[Any]	)       ->					Tuple:




             '''simple docstring'''
             _a    : Any     					=TOKEN
             HfFolder.save_token(SCREAMING_SNAKE_CASE	)






      @classmethod
      def __UpperCAmelCase       ( cls			:Optional[int]	)       ->					Any:




             '''simple docstring'''
             try:
                    delete_repo(token=cls._token     ,						repo_id="""test-processor"""	)
             except HTTPError:
                    pass

             try:
                    delete_repo(token=cls._token     ,						repo_id="""valid_org/test-processor-org"""	)
             except HTTPError:
                    pass

             try:
                    delete_repo(token=cls._token     ,						repo_id="""test-dynamic-processor"""	)
             except HTTPError:
                    pass






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




             '''simple docstring'''
             _a    : Dict     					=WavaVecaProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)
             with tempfile.TemporaryDirectory() as tmp_dir:
                    processor.save_pretrained(
                        os.path.join(SCREAMING_SNAKE_CASE     ,						"""test-processor"""	)     ,						push_to_hub=SCREAMING_SNAKE_CASE     ,						use_auth_token=self._token	)

                    _a    : Any     					=WavaVecaProcessor.from_pretrained(f"{USER}/test-processor"	)
                    for k, v in processor.feature_extractor.__dict__.items():
                           self.assertEqual(SCREAMING_SNAKE_CASE     ,						getattr(new_processor.feature_extractor     ,						SCREAMING_SNAKE_CASE	)	)
                    self.assertDictEqual(new_processor.tokenizer.get_vocab()     ,						processor.tokenizer.get_vocab()	)






      def __UpperCAmelCase       ( self			:Tuple	)       ->					int:




             '''simple docstring'''
             _a    : Optional[int]     					=WavaVecaProcessor.from_pretrained(SCREAMING_SNAKE_CASE	)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    processor.save_pretrained(
                        os.path.join(SCREAMING_SNAKE_CASE     ,						"""test-processor-org"""	)     ,						push_to_hub=SCREAMING_SNAKE_CASE     ,						use_auth_token=self._token     ,						organization="""valid_org"""     ,						)

                    _a    : Optional[Any]     					=WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org"""	)
                    for k, v in processor.feature_extractor.__dict__.items():
                           self.assertEqual(SCREAMING_SNAKE_CASE     ,						getattr(new_processor.feature_extractor     ,						SCREAMING_SNAKE_CASE	)	)
                    self.assertDictEqual(new_processor.tokenizer.get_vocab()     ,						processor.tokenizer.get_vocab()	)






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




             '''simple docstring'''
             CustomFeatureExtractor.register_for_auto_class()
             CustomTokenizer.register_for_auto_class()
             CustomProcessor.register_for_auto_class()

             _a    : int     					=CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE	)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    _a    : str     					=os.path.join(SCREAMING_SNAKE_CASE     ,						"""vocab.txt"""	)
                    with open(SCREAMING_SNAKE_CASE     ,						"""w"""     ,						encoding="""utf-8"""	) as vocab_writer:
                           vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]	)	)
                    _a    : Optional[int]     					=CustomTokenizer(SCREAMING_SNAKE_CASE	)

             _a    : Tuple     					=CustomProcessor(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    create_repo(f"{USER}/test-dynamic-processor"     ,						token=self._token	)
                    _a    : Optional[int]     					=Repository(SCREAMING_SNAKE_CASE     ,						clone_from=f"{USER}/test-dynamic-processor"     ,						token=self._token	)
                    processor.save_pretrained(SCREAMING_SNAKE_CASE	)

                    # This has added the proper auto_map field to the feature extractor config
                    self.assertDictEqual(
                        processor.feature_extractor.auto_map     ,						{
                            """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
                            """AutoProcessor""": """custom_processing.CustomProcessor""",
                        }     ,						)

                    # This has added the proper auto_map field to the tokenizer config
                    with open(os.path.join(SCREAMING_SNAKE_CASE     ,						"""tokenizer_config.json"""	)	) as f:
                           _a    : Dict     					=json.load(SCREAMING_SNAKE_CASE	)
                    self.assertDictEqual(
                        tokenizer_config["""auto_map"""]     ,						{
                            """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
                            """AutoProcessor""": """custom_processing.CustomProcessor""",
                        }     ,						)

                    # The code has been copied from fixtures
                    self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE     ,						"""custom_feature_extraction.py"""	)	)	)
                    self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE     ,						"""custom_tokenization.py"""	)	)	)
                    self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE     ,						"""custom_processing.py"""	)	)	)

                    repo.push_to_hub()

             _a    : Dict     					=AutoProcessor.from_pretrained(f"{USER}/test-dynamic-processor"     ,						trust_remote_code=SCREAMING_SNAKE_CASE	)
             # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
             self.assertEqual(new_processor.__class__.__name__     ,						"""CustomProcessor"""	)






'''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

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


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	List[str]  )  ->			int:
       return [
           int(1000 * (box[0] / width)  ),
           int(1000 * (box[1] / height)  ),
           int(1000 * (box[2] / width)  ),
           int(1000 * (box[3] / height)  ),
       ]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	np.ndarray    ,_UpperCAmelCase	:	Optional[str]    ,_UpperCAmelCase	:	Optional[str] = None  )  ->			Optional[int]:
       _a    : Any     					=tesseract_config if tesseract_config is not None else """"""

       # apply OCR
       _a    : Optional[Any]     					=to_pil_image(_UpperCAmelCase  )
       _a					,    _a    : List[Any]     					=pil_image.size
       _a    : List[str]     					=pytesseract.image_to_data(_UpperCAmelCase    ,lang=_UpperCAmelCase    ,output_type="""dict"""    ,config=_UpperCAmelCase  )
       _a					,    _a					,    _a					,    _a					,    _a    : str     					=data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

       # filter empty words and corresponding coordinates
       _a    : Tuple     					=[idx for idx, word in enumerate(_UpperCAmelCase  ) if not word.strip()]
       _a    : List[Any]     					=[word for idx, word in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]
       _a    : Dict     					=[coord for idx, coord in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]
       _a    : List[str]     					=[coord for idx, coord in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]
       _a    : Union[str, Any]     					=[coord for idx, coord in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]
       _a    : Union[str, Any]     					=[coord for idx, coord in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]

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

       # finally, normalize the bounding boxes
       _a    : str     					=[]
       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__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  List[Any]	       =							["pixel_values"]






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




             '''simple docstring'''
             super().__init__(**SCREAMING_SNAKE_CASE	)
             _a    : List[Any]     					=size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
             _a    : Tuple     					=get_size_dict(SCREAMING_SNAKE_CASE	)

             _a    : Dict     					=do_resize
             _a    : Tuple     					=size
             _a    : str     					=resample
             _a    : Dict     					=apply_ocr
             _a    : Union[str, Any]     					=ocr_lang
             _a    : Dict     					=tesseract_config






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




             '''simple docstring'''
             _a    : int     					=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()}"	)
             _a    : Any     					=(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 __UpperCAmelCase       ( self			:Dict     ,						SCREAMING_SNAKE_CASE			:ImageInput     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE			:PILImageResampling = None     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:Optional[str] = None     ,						SCREAMING_SNAKE_CASE			:Optional[str] = None     ,						SCREAMING_SNAKE_CASE			:Optional[Union[str, TensorType]] = None     ,						SCREAMING_SNAKE_CASE			:ChannelDimension = ChannelDimension.FIRST     ,						**SCREAMING_SNAKE_CASE			:Optional[Any]     ,						)       ->					PIL.Image.Image:




             '''simple docstring'''
             _a    : Optional[int]     					=do_resize if do_resize is not None else self.do_resize
             _a    : Optional[int]     					=size if size is not None else self.size
             _a    : str     					=get_size_dict(SCREAMING_SNAKE_CASE	)
             _a    : List[str]     					=resample if resample is not None else self.resample
             _a    : int     					=apply_ocr if apply_ocr is not None else self.apply_ocr
             _a    : str     					=ocr_lang if ocr_lang is not None else self.ocr_lang
             _a    : Union[str, Any]     					=tesseract_config if tesseract_config is not None else self.tesseract_config

             _a    : List[str]     					=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.
             _a    : List[Any]     					=[to_numpy_array(SCREAMING_SNAKE_CASE	) for image in images]

             if apply_ocr:
                    requires_backends(self     ,						"""pytesseract"""	)
                    _a    : Any     					=[]
                    _a    : Any     					=[]
                    for image in images:
                           _a					,    _a    : int     					=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:
                    _a    : Union[str, Any]     					=[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)
             _a    : Dict     					=[flip_channel_order(SCREAMING_SNAKE_CASE	) for image in images]
             _a    : str     					=[to_channel_dimension_format(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	) for image in images]

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

             if apply_ocr:
                    _a    : List[Any]     					=words_batch
                    _a    : Dict     					=boxes_batch
             return data


