




import copy
import re







class        __lowerCAmelCase					:

			"""simple docstring"""







			snake_case_  						=			'''hp'''
			snake_case_  						=			{}
			snake_case_  						=			None


			@classmethod
			def  lowercase_       ( cls     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       prefix
							__lowerCamelCase									=       defaults
							cls.build_naming_info()


			@staticmethod
			def  lowercase_       ( lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							if len(lowerCamelCase__				) == 0:
											return ""
							__lowerCamelCase									=       None
							if any(char.isdigit() for char in word				):
											raise Exception(f"""Parameters should not contain numbers: '{word}' contains a number"""				)
							if word in info["short_word"]:
											return info["short_word"][word]
							for prefix_len in range(1     ,		len(lowerCamelCase__				) + 1				):
											__lowerCamelCase									=       word[:prefix_len]
											if prefix in info["reverse_short_word"]:
															continue
											else:
															__lowerCamelCase									=       prefix
															break

							if short_word is None:
											# Paranoid fallback
											def int_to_alphabetic(lowerCamelCase__				):
															__lowerCamelCase									=       ''
															while integer != 0:
																			__lowerCamelCase									=       chr(ord('A'				) + integer % 10				) + s
																			integer //= 10
															return s

											__lowerCamelCase									=       0
											while True:
															__lowerCamelCase									=       word + '#' + int_to_alphabetic(lowerCamelCase__				)
															if sword in info["reverse_short_word"]:
																			continue
															else:
																			__lowerCamelCase									=       sword
																			break

							__lowerCamelCase									=       short_word
							__lowerCamelCase									=       word
							return short_word


			@staticmethod
			def  lowercase_       ( lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       param_name.split('_'				)

							__lowerCamelCase									=       [TrialShortNamer.shortname_for_word(lowerCamelCase__     ,		lowerCamelCase__				) for word in words]

							# We try to create a separatorless short name, but if there is a collision we have to fallback
							# to a separated short name
							__lowerCamelCase									=       ['', '_']

							for separator in separators:
											__lowerCamelCase									=       separator.join(lowerCamelCase__				)
											if shortname not in info["reverse_short_param"]:
															__lowerCamelCase									=       shortname
															__lowerCamelCase									=       param_name
															return shortname

							return param_name


			@staticmethod
			def  lowercase_       ( lowerCamelCase__     ,		lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       TrialShortNamer.shortname_for_key(lowerCamelCase__     ,		lowerCamelCase__				)
							__lowerCamelCase									=       short_name
							__lowerCamelCase									=       param_name


			@classmethod
			def  lowercase_       ( cls				)       -> Optional[Any]:



							'''simple docstring'''
							if cls.NAMING_INFO is not None:
											return

							__lowerCamelCase									=       {
							    'short_word': {},
							    'reverse_short_word': {},
							    'short_param': {},
							    'reverse_short_param': {},
							}

							__lowerCamelCase									=       list(cls.DEFAULTS.keys()				)

							for k in field_keys:
											cls.add_new_param_name(lowerCamelCase__     ,		lowerCamelCase__				)

							__lowerCamelCase									=       info


			@classmethod
			def  lowercase_       ( cls     ,		lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							cls.build_naming_info()
							assert cls.PREFIX is not None
							__lowerCamelCase									=       [copy.copy(cls.PREFIX				)]

							for k, v in params.items():
											if k not in cls.DEFAULTS:
															raise Exception(f"""You should provide a default value for the param name {k} with value {v}"""				)
											if v == cls.DEFAULTS[k]:
															# The default value is not added to the name
															continue

											__lowerCamelCase									=       cls.NAMING_INFO['short_param'][k]

											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															__lowerCamelCase									=       1 if v else 0

											__lowerCamelCase									=       '' if isinstance(lowerCamelCase__     ,		(int, float)				) else '-'
											__lowerCamelCase									=       f"""{key}{sep}{v}"""
											name.append(lowerCamelCase__				)

							return "_".join(lowerCamelCase__				)






			@classmethod
			def  lowercase_       ( cls     ,		lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       repr[len(cls.PREFIX				) + 1 :]
							if repr == "":
											__lowerCamelCase									=       []
							else:
											__lowerCamelCase									=       repr.split('_'				)

							__lowerCamelCase									=       {}

							for value in values:
											if "-" in value:
															__lowerCamelCase			,		__lowerCamelCase									=       value.split('-'				)
											else:
															__lowerCamelCase									=       re.sub('[0-9.]'     ,		''     ,		lowerCamelCase__				)
															__lowerCamelCase									=       float(re.sub('[^0-9.]'     ,		''     ,		lowerCamelCase__				)				)

											__lowerCamelCase									=       cls.NAMING_INFO['reverse_short_param'][p_k]

											__lowerCamelCase									=       p_v

							for k in cls.DEFAULTS:
											if k not in parameters:
															__lowerCamelCase									=       cls.DEFAULTS[k]

							return parameters








from typing import Dict, List, Optional, Tuple, 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, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


__A								=       logging.get_logger(__name__)







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''pixel_values''']


			def __init__( self     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = PILImageResampling.BILINEAR     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = 1 / 255     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> None:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)
							__lowerCamelCase									=       size if size is not None else {'shortest_edge': 256}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_resize
							__lowerCamelCase									=       size
							__lowerCamelCase									=       resample
							__lowerCamelCase									=       do_center_crop
							__lowerCamelCase									=       crop_size
							__lowerCamelCase									=       do_rescale
							__lowerCamelCase									=       rescale_factor
							__lowerCamelCase									=       do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							__lowerCamelCase									=       image_std if image_std is not None else IMAGENET_STANDARD_STD


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = PILImageResampling.BICUBIC     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							if "shortest_edge" not in size:
											raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
							__lowerCamelCase									=       get_resize_output_image_size(lowerCamelCase__     ,		size=size['shortest_edge']     ,		default_to_square=lowerCamelCase__				)
							return resize(lowerCamelCase__     ,		size=lowerCamelCase__     ,		resample=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__				)
							if "height" not in size or "width" not in size:
											raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
							return center_crop(lowerCamelCase__     ,		size=(size['height'], size['width'])     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__				)       -> np.ndarray:



							'''simple docstring'''
							return rescale(lowerCamelCase__     ,		scale=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							return normalize(lowerCamelCase__     ,		mean=lowerCamelCase__     ,		std=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = ChannelDimension.FIRST     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       do_resize if do_resize is not None else self.do_resize
							__lowerCamelCase									=       size if size is not None else self.size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       resample if resample is not None else self.resample
							__lowerCamelCase									=       do_center_crop if do_center_crop is not None else self.do_center_crop
							__lowerCamelCase									=       crop_size if crop_size is not None else self.crop_size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_rescale if do_rescale is not None else self.do_rescale
							__lowerCamelCase									=       rescale_factor if rescale_factor is not None else self.rescale_factor
							__lowerCamelCase									=       do_normalize if do_normalize is not None else self.do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else self.image_mean
							__lowerCamelCase									=       image_std if image_std is not None else self.image_std

							__lowerCamelCase									=       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:
											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.
							__lowerCamelCase									=       [to_numpy_array(lowerCamelCase__				) for image in images]

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

							if do_center_crop:
											__lowerCamelCase									=       [self.center_crop(image=lowerCamelCase__     ,		size=lowerCamelCase__				) for image in images]

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

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

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

							__lowerCamelCase									=       {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__     ,		tensor_type=lowerCamelCase__				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       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__				):
															__lowerCamelCase									=       target_sizes.numpy()

											__lowerCamelCase									=       []

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

							return semantic_segmentation


