

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


A_	:			List[Any]  	=      logging.get_logger(__name__)



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	['''pixel_values''']






					def __init__(			self   ,	A__ = True   ,	A__ = 1 / 255   ,	A__ = True   ,	A__ = 8   ,	**A__   ,	):
						super().__init__(**A__      )

						A__							:   str					       =		do_rescale
						A__							:   Optional[Any]					       =		rescale_factor
						A__							:   Optional[int]					       =		do_pad
						A__							:   str					       =		pad_size






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = None   ,	**A__      ):
						return rescale(A__   ,	scale=A__   ,	data_format=A__   ,	**A__      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = None      ):
						A__							:   Dict					       =		get_image_size(A__      )
						A__							:   Tuple					       =		(old_height // size + 1) * size - old_height
						A__							:   Dict					       =		(old_width // size + 1) * size - old_width

						return pad(A__   ,	((0, pad_height), (0, pad_width))   ,	mode="""symmetric"""   ,	data_format=A__      )






					def   __A      (			self   ,	A__   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = ChannelDimension.FIRST   ,	**A__   ,	):
						A__							:   List[str]					       =		do_rescale if do_rescale is not None else self.do_rescale
						A__							:   str					       =		rescale_factor if rescale_factor is not None else self.rescale_factor
						A__							:   List[str]					       =		do_pad if do_pad is not None else self.do_pad
						A__							:   Any					       =		pad_size if pad_size is not None else self.pad_size

						A__							:   str					       =		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_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

						# All transformations expect numpy arrays.
						A__							:   str					       =		[to_numpy_array(A__      ) for image in images]

						if do_rescale:
							A__							:   Union[str, Any]					       =		[self.rescale(image=A__   ,	scale=A__      ) for image in images]

						if do_pad:
							A__							:   Optional[Any]					       =		[self.pad(A__   ,	size=A__      ) for image in images]

						A__							:   Optional[int]					       =		[to_channel_dimension_format(A__   ,	A__      ) for image in images]

						A__							:   List[str]					       =		{"""pixel_values""": images}
						return BatchFeature(data=A__   ,	tensor_type=A__      )






import argparse
from collections import defaultdict

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[int]   ,				lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			Any )					->				int:
	A__							:   Optional[Any]					       =		f"""{file}_{class_name}_{test_name}"""
	done_test[_id] += 1

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   Union[str, Any]					       =		f.readlines()

	A__							:   str					       =		f"""class {class_name}("""
	A__							:   Optional[Any]					       =		f"""{4 * ' '}def {test_name}("""
	A__							:   Union[str, Any]					       =		f"""{8 * ' '}{correct_line.split()[0]}"""
	A__							:   Optional[int]					       =		f"""{16 * ' '}{correct_line.split()[0]}"""
	A__							:   int					       =		False
	A__							:   str					       =		False
	A__							:   Tuple					       =		False
	A__							:   Optional[int]					       =		False
	A__							:   Optional[Any]					       =		0
	A__							:   Dict					       =		0

	A__							:   List[str]					       =		[]
	for line in lines:
		if line.startswith(lowercase_ ):
			A__							:   Dict					       =		True
		elif in_class and line.startswith(lowercase_ ):
			A__							:   Optional[Any]					       =		True
		elif in_class and in_func and (line.startswith(lowercase_ ) or line.startswith(lowercase_ )):
			A__							:   Tuple					       =		len(line.split(correct_line.split()[0] )[0] )
			count += 1

			if count == done_test[_id]:
				A__							:   Any					       =		True

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

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f"""{spaces * ' '}{correct_line}""" )
			A__							:   List[str]					       =		False
		else:
			new_lines.append(lowercase_ )

	with open(lowercase_   ,				"""w""" ) as f:
		for line in new_lines:
			f.write(lowercase_ )

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[Any]=None )					->				Any:
	if fail is not None:
		with open(lowercase_   ,				"""r""" ) as f:
			A__							:   Dict					       =		{l.strip() for l in f.readlines()}
	else:
		A__							:   List[str]					       =		None

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   int					       =		f.readlines()

	A__							:   Union[str, Any]					       =		defaultdict(lowercase_ )
	for line in correct_lines:
		A__       ,       A__       ,       A__       ,       A__							:   Optional[int]					       =		line.split(""";""" )
		if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
			overwrite_file(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	A_	:			Optional[Any]  	=      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)
	A_	:			Optional[Any]  	=      parser.parse_args()

	main(args.correct_filename, args.fail_filename)



