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

__lowerCamelCase      :			Dict							       =		logging.get_logger(__name__)



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	str     )							-> Union[str, Any]:







  """simple docstring"""





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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	np.ndarray			,					__UpperCamelCase   :	Optional[str]			,					__UpperCamelCase   :	Optional[str]     )							-> Optional[int]:







  """simple docstring"""





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

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

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

  # finally, normalize the bounding boxes
  SCREAMING_SNAKE_CASE__      							=				[]
  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 __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(   self      :    Tuple	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    bool = True	,     _lowercase      :    float = 1 / 2_55	,     _lowercase      :    bool = True	,     _lowercase      :    Union[float, Iterable[float]] = None	,     _lowercase      :    Union[float, Iterable[float]] = None	,     _lowercase      :    bool = True	,     _lowercase      :    Optional[str] = None	,     _lowercase      :    Optional[str] = ""	,     **_lowercase      :    int	,     ):



     """simple docstring"""






     super().__init__(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""height""": 2_24, """width""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase				)

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




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



     """simple docstring"""






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




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[int, float]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    List[Any]	,     ):



     """simple docstring"""






     return rescale(_lowercase	,     scale=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[float, Iterable[float]]	,     _lowercase      :    Union[float, Iterable[float]]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    List[Any]	,     ):



     """simple docstring"""






     return normalize(_lowercase	,     mean=_lowercase	,     std=_lowercase	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    Any=None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    Union[float, Iterable[float]] = None	,     _lowercase      :    Union[float, Iterable[float]] = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[str] = None	,     _lowercase      :    Optional[str] = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    ChannelDimension = ChannelDimension.FIRST	,     **_lowercase      :    List[Any]	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else self.size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE__      							=				image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE__      							=				apply_ocr if apply_ocr is not None else self.apply_ocr
     SCREAMING_SNAKE_CASE__      							=				ocr_lang if ocr_lang is not None else self.ocr_lang
     SCREAMING_SNAKE_CASE__      							=				tesseract_config if tesseract_config is not None else self.tesseract_config

     SCREAMING_SNAKE_CASE__      							=				make_list_of_images(_lowercase				)

     if not valid_images(_lowercase				):
       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.
     SCREAMING_SNAKE_CASE__      							=				[to_numpy_array(_lowercase				) for image in images]

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

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

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

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

     SCREAMING_SNAKE_CASE__      							=				[to_channel_dimension_format(_lowercase	,     _lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				BatchFeature(data={"""pixel_values""": images}	,     tensor_type=_lowercase				)

     if apply_ocr:
       SCREAMING_SNAKE_CASE__      							=				words_batch
       SCREAMING_SNAKE_CASE__      							=				boxes_batch
     return data

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__lowerCamelCase      :			int							       =		logging.get_logger(__name__)


__lowerCamelCase      :			Tuple							       =		{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Optional[Any]     )							-> Union[str, Any]:







  """simple docstring"""





  if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
    raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()     )}."""     )

  if tokenizer_name is None:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES
  else:
    SCREAMING_SNAKE_CASE__      							=				{tokenizer_name: getattr(__UpperCamelCase			,					tokenizer_name + """Fast"""     )}

  logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""     )

  for tokenizer_name in tokenizer_names:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES[tokenizer_name]

    SCREAMING_SNAKE_CASE__      							=				True
    if checkpoint_name is None:
      SCREAMING_SNAKE_CASE__      							=				list(tokenizer_class.max_model_input_sizes.keys()     )
    else:
      SCREAMING_SNAKE_CASE__      							=				[checkpoint_name]

    logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""     )

    for checkpoint in checkpoint_names:
      logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""     )

      # Load tokenizer
      SCREAMING_SNAKE_CASE__      							=				tokenizer_class.from_pretrained(__UpperCamelCase			,					force_download=__UpperCamelCase     )

      # Save fast tokenizer
      logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""     )

      # For organization names we create sub-directories
      if "/" in checkpoint:
        SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				checkpoint.split("""/"""     )
        SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
      elif add_prefix:
        SCREAMING_SNAKE_CASE__      							=				checkpoint
        SCREAMING_SNAKE_CASE__      							=				dump_path
      else:
        SCREAMING_SNAKE_CASE__      							=				None
        SCREAMING_SNAKE_CASE__      							=				dump_path

      logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""     )

      if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()     )[0]:
        SCREAMING_SNAKE_CASE__      							=				list(tokenizer.pretrained_vocab_files_map.values()     )[0][checkpoint]
        SCREAMING_SNAKE_CASE__      							=				file_path.split(__UpperCamelCase     )[-1][0]
        if next_char == "/":
          SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
          SCREAMING_SNAKE_CASE__      							=				None

        logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""     )

      SCREAMING_SNAKE_CASE__      							=				tokenizer.save_pretrained(
          __UpperCamelCase			,					legacy_format=__UpperCamelCase			,					filename_prefix=__UpperCamelCase     )
      logger.info(f"""=> File names {file_names}"""     )

      for file_name in file_names:
        if not file_name.endswith("""tokenizer.json"""     ):
          os.remove(__UpperCamelCase     )
          logger.info(f"""=> removing {file_name}"""     )


if __name__ == "__main__":
   __lowerCamelCase      :			str							       =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
   )
   parser.add_argument(
       '''--tokenizer_name''',
       default=None,
       type=str,
       help=(
           F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
           '''download and convert all the checkpoints from AWS.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_name''',
       default=None,
       type=str,
       help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
   )
   parser.add_argument(
       '''--force_download''',
       action='''store_true''',
       help='''Re-download checkpoints.''',
   )
   __lowerCamelCase      :			Any							       =		parser.parse_args()

   convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
