
"""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

UpperCamelCase			:       Any									=				logging.get_logger(__name__)






def 							A		(       snake_case   :List[Any]       ,      snake_case   :Any       ,      snake_case   :List[str]	)   ->			Tuple:
 return [
     int(1_0_0_0 * (box[0] / width)	),
     int(1_0_0_0 * (box[1] / height)	),
     int(1_0_0_0 * (box[2] / width)	),
     int(1_0_0_0 * (box[3] / height)	),
 ]






def 							A		(       snake_case   :np.ndarray       ,      snake_case   :Optional[str]       ,      snake_case   :Optional[str] = None	)   ->			str:
 __UpperCamelCase       						= tesseract_config if tesseract_config is not None else ''

 # apply OCR
 __UpperCamelCase       						= to_pil_image(snake_case	)
 __UpperCamelCase						,			__UpperCamelCase       						= pil_image.size
 __UpperCamelCase       						= pytesseract.image_to_data(snake_case       ,      lang=snake_case       ,      output_type='dict'       ,      config=snake_case	)
 __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(snake_case	) if not word.strip()]
 __UpperCamelCase       						= [word for idx, word in enumerate(snake_case	) if idx not in irrelevant_indices]
 __UpperCamelCase       						= [coord for idx, coord in enumerate(snake_case	) if idx not in irrelevant_indices]
 __UpperCamelCase       						= [coord for idx, coord in enumerate(snake_case	) if idx not in irrelevant_indices]
 __UpperCamelCase       						= [coord for idx, coord in enumerate(snake_case	) if idx not in irrelevant_indices]
 __UpperCamelCase       						= [coord for idx, coord in enumerate(snake_case	) if idx not in irrelevant_indices]

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

 # finally, normalize the bounding boxes
 __UpperCamelCase       						= []
 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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		["pixel_values"]





     def __init__(			self			,  __UpperCAmelCase = True			,  __UpperCAmelCase = None			,  __UpperCAmelCase = PILImageResampling.BILINEAR			,  __UpperCAmelCase = True			,  __UpperCAmelCase = None			,  __UpperCAmelCase = ""			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )
      __UpperCamelCase       						= size if size is not None else {'height': 224, 'width': 224}
      __UpperCamelCase       						= get_size_dict(__UpperCAmelCase   )

      __UpperCamelCase       						= do_resize
      __UpperCamelCase       						= size
      __UpperCamelCase       						= resample
      __UpperCamelCase       						= apply_ocr
      __UpperCamelCase       						= ocr_lang
      __UpperCamelCase       						= tesseract_config





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase = PILImageResampling.BILINEAR			,  __UpperCAmelCase = None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= 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()}'   )
      __UpperCamelCase       						= (size['height'], size['width'])
      return resize(__UpperCAmelCase			,  size=__UpperCAmelCase			,  resample=__UpperCAmelCase			,  data_format=__UpperCAmelCase			,  **__UpperCAmelCase   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = None			,  __UpperCAmelCase = ChannelDimension.FIRST			,  **__UpperCAmelCase			,  ):

      '''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(__UpperCAmelCase   )
      __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(__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.'   )

      # All transformations expect numpy arrays.
      __UpperCamelCase       						= [to_numpy_array(__UpperCAmelCase   ) for image in images]

      if apply_ocr:
       requires_backends(self			,  'pytesseract'   )
       __UpperCamelCase       						= []
       __UpperCamelCase       						= []
       for image in images:
        __UpperCamelCase						,			__UpperCamelCase       						= apply_tesseract(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )
        words_batch.append(__UpperCAmelCase   )
        boxes_batch.append(__UpperCAmelCase   )

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

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

      __UpperCamelCase       						= BatchFeature(data={'pixel_values': images}			,  tensor_type=__UpperCAmelCase   )

      if apply_ocr:
       __UpperCamelCase       						= words_batch
       __UpperCamelCase       						= boxes_batch
      return data





"""simple docstring"""

import argparse
from collections import defaultdict






def 							A		(       snake_case   :Tuple       ,      snake_case   :List[Any]       ,      snake_case   :List[Any]       ,      snake_case   :Union[str, Any]       ,      snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 __UpperCamelCase       						= f'{file}_{class_name}_{test_name}'
 done_test[_id] += 1

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= f'class {class_name}('
 __UpperCamelCase       						= f'{4 * " "}def {test_name}('
 __UpperCamelCase       						= f'{8 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= f'{1_6 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= 0
 __UpperCamelCase       						= 0

 __UpperCamelCase       						= []
 for line in lines:
  if line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and in_func and (line.startswith(snake_case	) or line.startswith(snake_case	)):
   __UpperCamelCase       						= len(line.split(correct_line.split()[0]	)[0]	)
   count += 1

   if count == done_test[_id]:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line:
   if ")" not in line:
    continue
   else:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line and insert_line:
   new_lines.append(f'{spaces * " "}{correct_line}'	)
   __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= False
  else:
   new_lines.append(snake_case	)

 with open(snake_case       ,      'w'	) as f:
  for line in new_lines:
   f.write(snake_case	)






def 							A		(       snake_case   :List[str]       ,      snake_case   :str=None	)   ->			Tuple:
 if fail is not None:
  with open(snake_case       ,      'r'	) as f:
   __UpperCamelCase       						= {l.strip() for l in f.readlines()}
 else:
  __UpperCamelCase       						= None

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= defaultdict(snake_case	)
 for line in correct_lines:
  __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= line.split(';'	)
  if test_failures is None or "::".join([file, class_name, test_name]	) in test_failures:
   overwrite_file(snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case	)


if __name__ == "__main__":
      UpperCamelCase			:       str									=				argparse.ArgumentParser()
      parser.add_argument("--correct_filename", help="filename of tests with expected result")
      parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
      UpperCamelCase			:       List[Any]									=				parser.parse_args()

      main(args.correct_filename, args.fail_filename)



