

__UpperCAmelCase    				=     {
    'Pillow': 'Pillow<10.0.0',
    'accelerate': 'accelerate>=0.20.3',
    'av': 'av==9.2.0',
    'beautifulsoup4': 'beautifulsoup4',
    'black': 'black~=23.1',
    'codecarbon': 'codecarbon==1.2.0',
    'cookiecutter': 'cookiecutter==1.7.3',
    'dataclasses': 'dataclasses',
    'datasets': 'datasets!=2.5.0',
    'decord': 'decord==0.6.0',
    'deepspeed': 'deepspeed>=0.9.3',
    'diffusers': 'diffusers',
    'dill': 'dill<0.3.5',
    'evaluate': 'evaluate>=0.2.0',
    'fairscale': 'fairscale>0.3',
    'faiss-cpu': 'faiss-cpu',
    'fastapi': 'fastapi',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1,<=0.7.0',
    'ftfy': 'ftfy',
    'fugashi': 'fugashi>=1.0',
    'GitPython': 'GitPython<3.1.19',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.14.1,<1.0',
    'importlib_metadata': 'importlib_metadata',
    'ipadic': 'ipadic>=1.0.0,<2.0',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2,<=0.4.13',
    'jaxlib': 'jaxlib>=0.1.65,<=0.4.13',
    'jieba': 'jieba',
    'kenlm': 'kenlm',
    'keras-nlp': 'keras-nlp>=0.3.1',
    'librosa': 'librosa',
    'nltk': 'nltk',
    'natten': 'natten>=0.14.6',
    'numpy': 'numpy>=1.17',
    'onnxconverter-common': 'onnxconverter-common',
    'onnxruntime-tools': 'onnxruntime-tools>=1.4.2',
    'onnxruntime': 'onnxruntime>=1.4.0',
    'opencv-python': 'opencv-python',
    'optuna': 'optuna',
    'optax': 'optax>=0.0.8,<=0.1.4',
    'packaging': 'packaging>=20.0',
    'parameterized': 'parameterized',
    'phonemizer': 'phonemizer',
    'protobuf': 'protobuf',
    'psutil': 'psutil',
    'pyyaml': 'pyyaml>=5.1',
    'pydantic': 'pydantic<2',
    'pytest': 'pytest>=7.2.0',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'python': 'python>=3.8.0',
    'ray[tune]': 'ray[tune]',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'rhoknp': 'rhoknp>=1.1.0,<1.3.1',
    'rjieba': 'rjieba',
    'rouge-score': 'rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1',
    'ruff': 'ruff>=0.0.241,<=0.0.259',
    'sacrebleu': 'sacrebleu>=1.4.12,<2.0.0',
    'sacremoses': 'sacremoses',
    'safetensors': 'safetensors>=0.3.1',
    'sagemaker': 'sagemaker>=2.31.0',
    'scikit-learn': 'scikit-learn',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'sigopt': 'sigopt',
    'starlette': 'starlette',
    'sudachipy': 'sudachipy>=0.6.6',
    'sudachidict_core': 'sudachidict_core>=20220729',
    'tensorflow-cpu': 'tensorflow-cpu>=2.6,<2.14',
    'tensorflow': 'tensorflow>=2.6,<2.14',
    'tensorflow-text': 'tensorflow-text<2.14',
    'tf2onnx': 'tf2onnx',
    'timeout-decorator': 'timeout-decorator',
    'timm': 'timm',
    'tokenizers': 'tokenizers>=0.11.1,!=0.11.3,<0.14',
    'torch': 'torch>=1.9,!=1.12.0',
    'torchaudio': 'torchaudio',
    'torchvision': 'torchvision',
    'pyctcdecode': 'pyctcdecode>=0.4.0',
    'tqdm': 'tqdm>=4.27',
    'unidic': 'unidic>=1.0.2',
    'unidic_lite': 'unidic_lite>=1.0.7',
    'urllib3': 'urllib3<2.0.0',
    'uvicorn': 'uvicorn',
}









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

__UpperCAmelCase    				=     logging.get_logger(__name__)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     return [
         int(1000 * (box[0] / width)				),
         int(1000 * (box[1] / height)				),
         int(1000 * (box[2] / width)				),
         int(1000 * (box[3] / height)				),
     ]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else ""

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

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

     # turn coordinates into (left, top, left+width, top+height) format
     _lowerCAmelCase   :					Union[str, Any]        =    []
     for x, y, w, h in zip(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    [x, y, x + w, y + h]
          actual_boxes.append(SCREAMING_SNAKE_CASE				)

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

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

     return words, normalized_boxes





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

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






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					int        =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Any        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Optional[int]        =    apply_ocr
      _lowerCAmelCase   :					Dict        =    ocr_lang
      _lowerCAmelCase   :					Optional[int]        =    tesseract_config

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    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()}'''       )
      _lowerCAmelCase   :					Dict        =    (size["height"], size["width"])
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Dict     ,						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_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[Any]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					Union[str, Any]        =    apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowerCAmelCase   :					Tuple        =    ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowerCAmelCase   :					Union[str, Any]        =    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.
      _lowerCAmelCase   :					Dict        =    [to_numpy_array(A_       ) for image in images]

      if apply_ocr:
           requires_backends(self     ,						"pytesseract"       )
           _lowerCAmelCase   :					Any        =    []
           _lowerCAmelCase   :					List[Any]        =    []
           for image in images:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    apply_tesseract(A_     ,						A_     ,						A_       )
                words_batch.append(A_       )
                boxes_batch.append(A_       )

      if do_resize:
           _lowerCAmelCase   :					Union[str, Any]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

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

      _lowerCAmelCase   :					Tuple        =    BatchFeature(data={"pixel_values": images}     ,						tensor_type=A_       )

      if apply_ocr:
           _lowerCAmelCase   :					Tuple        =    words_batch
           _lowerCAmelCase   :					Any        =    boxes_batch
      return data






