

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
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, logging


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



class    _a						(a__    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	["pixel_values"]






					def __init__(			self   ,	A__ = True   ,	A__ = None   ,	A__ = PILImageResampling.BILINEAR   ,	A__ = True   ,	A__ = None   ,	A__ = True   ,	A__ = 1 / 255   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						super().__init__(**SCREAMING_SNAKE_CASE_      )
						A__							:   List[Any]					       =		size if size is not None else {'shortest_edge': 256}
						A__							:   Optional[int]					       =		get_size_dict(SCREAMING_SNAKE_CASE_   ,	default_to_square=SCREAMING_SNAKE_CASE_      )
						A__							:   Optional[int]					       =		crop_size if crop_size is not None else {'height': 224, 'width': 224}
						A__							:   Dict					       =		get_size_dict(SCREAMING_SNAKE_CASE_      )
						A__							:   Tuple					       =		do_resize
						A__							:   Optional[int]					       =		size
						A__							:   List[str]					       =		resample
						A__							:   Union[str, Any]					       =		do_center_crop
						A__							:   List[str]					       =		crop_size
						A__							:   Optional[Any]					       =		do_rescale
						A__							:   str					       =		rescale_factor
						A__							:   List[str]					       =		do_normalize
						A__							:   Any					       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A__							:   int					       =		image_std if image_std is not None else IMAGENET_STANDARD_STD






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = PILImageResampling.BICUBIC   ,	A__ = None   ,	**A__   ,	):
						A__							:   List[str]					       =		get_size_dict(SCREAMING_SNAKE_CASE_   ,	default_to_square=SCREAMING_SNAKE_CASE_      )
						if "shortest_edge" not in size:
							raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""      )
						A__							:   Union[str, Any]					       =		get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,	size=size["""shortest_edge"""]   ,	default_to_square=SCREAMING_SNAKE_CASE_      )
						return resize(SCREAMING_SNAKE_CASE_   ,	size=SCREAMING_SNAKE_CASE_   ,	resample=SCREAMING_SNAKE_CASE_   ,	data_format=SCREAMING_SNAKE_CASE_   ,	**SCREAMING_SNAKE_CASE_      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = None   ,	**A__   ,	):
						A__							:   Union[str, Any]					       =		get_size_dict(SCREAMING_SNAKE_CASE_      )
						return center_crop(SCREAMING_SNAKE_CASE_   ,	size=(size["""height"""], size["""width"""])   ,	data_format=SCREAMING_SNAKE_CASE_   ,	**SCREAMING_SNAKE_CASE_      )






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






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__ = None   ,	**A__   ,	):
						return normalize(SCREAMING_SNAKE_CASE_   ,	mean=SCREAMING_SNAKE_CASE_   ,	std=SCREAMING_SNAKE_CASE_   ,	data_format=SCREAMING_SNAKE_CASE_   ,	**SCREAMING_SNAKE_CASE_      )






					def   __A      (			self   ,	A__   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = ChannelDimension.FIRST   ,	**A__   ,	):
						A__							:   List[str]					       =		do_resize if do_resize is not None else self.do_resize
						A__							:   Any					       =		size if size is not None else self.size
						A__							:   Optional[Any]					       =		get_size_dict(SCREAMING_SNAKE_CASE_   ,	default_to_square=SCREAMING_SNAKE_CASE_      )
						A__							:   Tuple					       =		resample if resample is not None else self.resample
						A__							:   Dict					       =		do_center_crop if do_center_crop is not None else self.do_center_crop
						A__							:   List[str]					       =		crop_size if crop_size is not None else self.crop_size
						A__							:   Optional[Any]					       =		get_size_dict(SCREAMING_SNAKE_CASE_      )
						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__							:   Optional[Any]					       =		do_normalize if do_normalize is not None else self.do_normalize
						A__							:   int					       =		image_mean if image_mean is not None else self.image_mean
						A__							:   List[str]					       =		image_std if image_std is not None else self.image_std

						A__							:   List[Any]					       =		make_list_of_images(SCREAMING_SNAKE_CASE_      )

						if not valid_images(SCREAMING_SNAKE_CASE_      ):
							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_center_crop and crop_size is None:
							raise ValueError("""Crop size must be specified if do_center_crop 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("""Image mean and std must be specified if do_normalize is True."""      )

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

						if do_resize:
							A__							:   Optional[int]					       =		[self.resize(image=SCREAMING_SNAKE_CASE_   ,	size=SCREAMING_SNAKE_CASE_   ,	resample=SCREAMING_SNAKE_CASE_      ) for image in images]

						if do_center_crop:
							A__							:   Any					       =		[self.center_crop(image=SCREAMING_SNAKE_CASE_   ,	size=SCREAMING_SNAKE_CASE_      ) for image in images]

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

						if do_normalize:
							A__							:   Optional[int]					       =		[self.normalize(image=SCREAMING_SNAKE_CASE_   ,	mean=SCREAMING_SNAKE_CASE_   ,	std=SCREAMING_SNAKE_CASE_      ) for image in images]

						A__							:   Union[str, Any]					       =		[to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_      ) for image in images]

						A__							:   Union[str, Any]					       =		{'pixel_values': images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,	tensor_type=SCREAMING_SNAKE_CASE_      )






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)



