

'''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

_snake_case         =  logging.get_logger(__name__)







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     int:
   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  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     List[str]:
   _lowercase      :				Tuple      					=       to_pil_image(snake_case					)
   _lowercase					,				_lowercase      :				Optional[Any]      					=       pil_image.size
   _lowercase      :				Any      					=       pytesseract.image_to_data(snake_case		,							lang=snake_case		,							output_type="dict"		,							config=snake_case					)
   _lowercase					,				_lowercase					,				_lowercase					,				_lowercase					,				_lowercase      :				str      					=       data["text"], data["left"], data["top"], data["width"], data["height"]

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

   # turn coordinates into (left, top, left+width, top+height) format
   _lowercase      :				Optional[Any]      					=       []
   for x, y, w, h in zip(snake_case		,							snake_case		,							snake_case		,							snake_case					):
      _lowercase      :				List[Any]      					=       [x, y, x + w, y + h]
      actual_boxes.append(snake_case					)

   # finally, normalize the bounding boxes
   _lowercase      :				Optional[int]      					=       []
   for box in actual_boxes:
      normalized_boxes.append(normalize_box(snake_case		,							snake_case		,							snake_case					)					)

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

   return words, normalized_boxes


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							['pixel_values']


   def __init__(			self      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    _UpperCamelCase = PILImageResampling.BILINEAR      ,    _UpperCamelCase = True      ,    _UpperCamelCase = 1 / 255      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    _UpperCamelCase = ""      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(**_UpperCamelCase  )
      _lowercase      :				Optional[Any]      					=       size if size is not None else {"height": 224, "width": 224}
      _lowercase      :				Any      					=       get_size_dict(_UpperCamelCase  )

      _lowercase      :				Union[str, Any]      					=       do_resize
      _lowercase      :				str      					=       size
      _lowercase      :				Dict      					=       resample
      _lowercase      :				Any      					=       do_rescale
      _lowercase      :				Any      					=       rescale_value
      _lowercase      :				str      					=       do_normalize
      _lowercase      :				Any      					=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowercase      :				Any      					=       image_std if image_std is not None else IMAGENET_STANDARD_STD
      _lowercase      :				Optional[Any]      					=       apply_ocr
      _lowercase      :				Optional[int]      					=       ocr_lang
      _lowercase      :				str      					=       tesseract_config


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = PILImageResampling.BILINEAR      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       get_size_dict(_UpperCamelCase  )
      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()}'''  )
      _lowercase      :				Dict      					=       (size["height"], size["width"])
      return resize(_UpperCamelCase      ,    size=_UpperCamelCase      ,    resample=_UpperCamelCase      ,    data_format=_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





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






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase=None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = ChannelDimension.FIRST      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       do_resize if do_resize is not None else self.do_resize
      _lowercase      :				int      					=       size if size is not None else self.size
      _lowercase      :				List[Any]      					=       get_size_dict(_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       resample if resample is not None else self.resample
      _lowercase      :				Tuple      					=       do_rescale if do_rescale is not None else self.do_rescale
      _lowercase      :				Union[str, Any]      					=       rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowercase      :				int      					=       do_normalize if do_normalize is not None else self.do_normalize
      _lowercase      :				str      					=       image_mean if image_mean is not None else self.image_mean
      _lowercase      :				Union[str, Any]      					=       image_std if image_std is not None else self.image_std
      _lowercase      :				Optional[Any]      					=       apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowercase      :				List[Any]      					=       ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowercase      :				str      					=       tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowercase      :				Optional[Any]      					=       make_list_of_images(_UpperCamelCase  )

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

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

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

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

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

      _lowercase      :				int      					=       [to_channel_dimension_format(_UpperCamelCase      ,    _UpperCamelCase  ) for image in images]

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

      if apply_ocr:
         _lowercase      :				Optional[int]      					=       words_batch
         _lowercase      :				List[str]      					=       boxes_batch
      return data









'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


_snake_case         =  logging.getLogger()


@unittest.skip('Temporarily disable the doc tests.'  )
@require_torch
@require_tf
@slow
class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       [file for file in os.listdir(_UpperCamelCase  ) if os.path.isfile(os.path.join(_UpperCamelCase      ,    _UpperCamelCase  )  )]

      if identifier is not None:
         _lowercase      :				str      					=       [file for file in files if identifier in file]

      if n_identifier is not None:
         if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
            for n_ in n_identifier:
               _lowercase      :				Dict      					=       [file for file in files if n_ not in file]
         else:
            _lowercase      :				Optional[Any]      					=       [file for file in files if n_identifier not in file]

      _lowercase      :				Dict      					=       ignore_files or []
      ignore_files.append("__init__.py"  )
      _lowercase      :				List[str]      					=       [file for file in files if file not in ignore_files]

      for file in files:
         # Open all files
         print("Testing"      ,    _UpperCamelCase  )

         if only_modules:
            _lowercase      :				Optional[Any]      					=       file.split("."  )[0]
            try:
               _lowercase      :				Union[str, Any]      					=       getattr(_UpperCamelCase      ,    _UpperCamelCase  )
               _lowercase      :				Optional[int]      					=       doctest.DocTestSuite(_UpperCamelCase  )
               _lowercase      :				Tuple      					=       unittest.TextTestRunner().run(_UpperCamelCase  )
               self.assertIs(len(result.failures  )      ,    0  )
            except AttributeError:
               logger.info(f'''{module_identifier} is not a module.'''  )
         else:
            _lowercase      :				Optional[Any]      					=       doctest.testfile(str(".." / directory / file  )      ,    optionflags=doctest.ELLIPSIS  )
            self.assertIs(result.failed      ,    0  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       Path("src/transformers"  )
      _lowercase      :				str      					=       "modeling"
      _lowercase      :				Tuple      					=       [
          "modeling_ctrl.py",
          "modeling_tf_ctrl.py",
      ]
      self.analyze_directory(_UpperCamelCase      ,    identifier=_UpperCamelCase      ,    ignore_files=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       Path("src/transformers"  )
      _lowercase      :				Any      					=       "tokenization"
      self.analyze_directory(_UpperCamelCase      ,    identifier=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       Path("src/transformers"  )
      _lowercase      :				Optional[Any]      					=       "configuration"
      self.analyze_directory(_UpperCamelCase      ,    identifier=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       Path("src/transformers"  )
      _lowercase      :				List[Any]      					=       ["configuration", "modeling", "tokenization"]
      self.analyze_directory(_UpperCamelCase      ,    n_identifier=_UpperCamelCase  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       Path("docs/source"  )
      _lowercase      :				int      					=       ["favicon.ico"]
      self.analyze_directory(_UpperCamelCase      ,    ignore_files=_UpperCamelCase      ,    only_modules=_UpperCamelCase  )






