




import math
from typing import Dict, Iterable, List, Optional, Tuple, 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
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


__lowerCAmelCase	:       List[str]     =	logging.get_logger(__name__)




def     __magic_name__       (				A	:      np.ndarray,						A	:      Union[int, Iterable[int]],						A	:      bool,						A	:      int     ):






			'''simple docstring'''







			def constraint_to_multiple_of(A	:      Any,						A	:      Tuple,						A	:      Optional[int]=0,						A	:      int=None     ):
						a					=    round(val / multiple     ) * multiple

						if max_val is not None and x > max_val:
									a					=    math.floor(val / multiple     ) * multiple

						if x < min_val:
									a					=    math.ceil(val / multiple     ) * multiple

						return x

			a					=    (output_size, output_size) if isinstance(A,						A     ) else output_size

			a					,	a					=    get_image_size(A     )
			a					,	a					=    output_size

			# determine new height and width
			a					=    output_height / input_height
			a					=    output_width / input_width

			if keep_aspect_ratio:
						# scale as little as possible
						if abs(1 - scale_width     ) < abs(1 - scale_height     ):
									# fit width
									a					=    scale_width
						else:
									# fit height
									a					=    scale_height

			a					=    constraint_to_multiple_of(scale_height * input_height,						multiple=A     )
			a					=    constraint_to_multiple_of(scale_width * input_width,						multiple=A     )

			return (new_height, new_width)






class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[str]											=      ["""pixel_values"""]





				def __init__(   self       :  Optional[Any]      ,   __lowerCamelCase       :  bool = True      ,   __lowerCamelCase       :  Dict[str, int] = None      ,   __lowerCamelCase       :  PILImageResampling = PILImageResampling.BILINEAR      ,   __lowerCamelCase       :  bool = False      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  bool = True      ,   __lowerCamelCase       :  Union[int, float] = 1 / 2_55      ,   __lowerCamelCase       :  bool = True      ,   __lowerCamelCase       :  Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase       :  Optional[Union[float, List[float]]] = None      ,   **__lowerCamelCase       :  Any      ,   )      ->  None:
							super().__init__(**__lowerCamelCase							)
							a					=    size if size is not None else {"height": 3_84, "width": 3_84}
							a					=    get_size_dict(__lowerCamelCase							)
							a					=    do_resize
							a					=    size
							a					=    keep_aspect_ratio
							a					=    ensure_multiple_of
							a					=    resample
							a					=    do_rescale
							a					=    rescale_factor
							a					=    do_normalize
							a					=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							a					=    image_std if image_std is not None else IMAGENET_STANDARD_STD





				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  np.ndarray      ,   __lowerCamelCase       :  Dict[str, int]      ,   __lowerCamelCase       :  bool = False      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  PILImageResampling = PILImageResampling.BICUBIC      ,   __lowerCamelCase       :  Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase       :  Any      ,   )      ->  np.ndarray:
							a					=    get_size_dict(__lowerCamelCase							)
							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()}"""							)
							a					=    get_resize_output_image_size(
							    __lowerCamelCase      ,   output_size=(size["height"], size["width"])      ,   keep_aspect_ratio=__lowerCamelCase      ,   multiple=__lowerCamelCase      ,   )
							return resize(__lowerCamelCase      ,   size=__lowerCamelCase      ,   resample=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  np.ndarray      ,   __lowerCamelCase       :  Union[int, float]      ,   __lowerCamelCase       :  Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase       :  int      ,   )      ->  Tuple:
							return rescale(__lowerCamelCase      ,   scale=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  np.ndarray      ,   __lowerCamelCase       :  Union[float, List[float]]      ,   __lowerCamelCase       :  Union[float, List[float]]      ,   __lowerCamelCase       :  Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase       :  Union[str, Any]      ,   )      ->  np.ndarray:
							return normalize(__lowerCamelCase      ,   mean=__lowerCamelCase      ,   std=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  ImageInput      ,   __lowerCamelCase       :  bool = None      ,   __lowerCamelCase       :  int = None      ,   __lowerCamelCase       :  bool = None      ,   __lowerCamelCase       :  int = None      ,   __lowerCamelCase       :  PILImageResampling = None      ,   __lowerCamelCase       :  bool = None      ,   __lowerCamelCase       :  float = None      ,   __lowerCamelCase       :  bool = None      ,   __lowerCamelCase       :  Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase       :  Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase       :  Optional[Union[str, TensorType]] = None      ,   __lowerCamelCase       :  ChannelDimension = ChannelDimension.FIRST      ,   **__lowerCamelCase       :  Optional[int]      ,   )      ->  PIL.Image.Image:
							a					=    do_resize if do_resize is not None else self.do_resize
							a					=    size if size is not None else self.size
							a					=    get_size_dict(__lowerCamelCase							)
							a					=    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							a					=    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							a					=    resample if resample is not None else self.resample
							a					=    do_rescale if do_rescale is not None else self.do_rescale
							a					=    rescale_factor if rescale_factor is not None else self.rescale_factor
							a					=    do_normalize if do_normalize is not None else self.do_normalize
							a					=    image_mean if image_mean is not None else self.image_mean
							a					=    image_std if image_std is not None else self.image_std

							a					=    make_list_of_images(__lowerCamelCase							)

							if not valid_images(__lowerCamelCase							):
										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 or resample is None:
										raise ValueError("Size and resample 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("Image mean and std must be specified if do_normalize is True."							)

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

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

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

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

							a					=    [to_channel_dimension_format(__lowerCamelCase      ,   __lowerCamelCase							) for image in images]

							a					=    {"pixel_values": images}
							return BatchFeature(data=__lowerCamelCase      ,   tensor_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  List[Tuple] = None							)      ->  Any:
							a					=    outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
										if len(__lowerCamelCase							) != len(__lowerCamelCase							):
													raise ValueError(
													    "Make sure that you pass in as many target sizes as the batch dimension of the logits"							)

										if is_torch_tensor(__lowerCamelCase							):
													a					=    target_sizes.numpy()

										a					=    []

										for idx in range(len(__lowerCamelCase							)							):
													a					=    torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0							)      ,   size=target_sizes[idx]      ,   mode="bilinear"      ,   align_corners=__lowerCamelCase							)
													a					=    resized_logits[0].argmax(dim=0							)
													semantic_segmentation.append(__lowerCamelCase							)
							else:
										a					=    logits.argmax(dim=1							)
										a					=    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

							return semantic_segmentation












import argparse
import os
import re


__lowerCAmelCase	:       Union[str, Any]     =	'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase	:       Dict     =	re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__lowerCAmelCase	:       Any     =	re.compile(r'\s*\(\s*"(\S[^"]+)"')




def     __magic_name__       (				A	:      int,						A	:      bool = False     ):






			'''simple docstring'''







			with open(A,						"r",						encoding="utf-8"     ) as f:
						a					=    f.read()

			a					=    content.split("\n"     )
			a					=    []
			a					=    0
			while line_idx < len(A     ):
						if _re_intro_mapping.search(lines[line_idx]     ) is not None:
									a					=    len(re.search(R"^(\s*)\S",						lines[line_idx]     ).groups()[0]     ) + 8
									# Start of a new mapping!
									while not lines[line_idx].startswith(" " * indent + "("     ):
												new_lines.append(lines[line_idx]     )
												line_idx += 1

									a					=    []
									while lines[line_idx].strip() != "]":
												# Blocks either fit in one line or not
												if lines[line_idx].strip() == "(":
															a					=    line_idx
															while not lines[line_idx].startswith(" " * indent + ")"     ):
																		line_idx += 1
															blocks.append("\n".join(lines[start_idx : line_idx + 1]     )     )
												else:
															blocks.append(lines[line_idx]     )
												line_idx += 1

									# Sort blocks by their identifiers
									a					=    sorted(A,						key=lambda A     : _re_identifier.search(A     ).groups()[0]     )
									new_lines += blocks
						else:
									new_lines.append(lines[line_idx]     )
									line_idx += 1

			if overwrite:
						with open(A,						"w",						encoding="utf-8"     ) as f:
									f.write("\n".join(A     )     )
			elif "\n".join(A     ) != content:
						return True




def     __magic_name__       (				A	:      bool = False     ):






			'''simple docstring'''







			a					=    [os.path.join(A,						A     ) for f in os.listdir(A     ) if f.endswith(".py"     )]
			a					=    [sort_auto_mapping(A,						overwrite=A     ) for fname in fnames]

			if not overwrite and any(A     ):
						a					=    [f for f, d in zip(A,						A     ) if d]
						raise ValueError(
						    F"""The following files have auto mappings that need sorting: {", ".join(A     )}. Run `make style` to fix"""
						    " this."     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Dict     =	argparse.ArgumentParser()
		parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
		__lowerCAmelCase	:       Optional[Any]     =	parser.parse_args()

		sort_all_auto_mappings(not args.check_only)






