
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


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


class 						lowerCamelCase       (lowerCamelCase__      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   ['pixel_values']






							def __init__( self     :					Union[str, Any],			_UpperCAmelCase     :					str = True,			_UpperCAmelCase     :					Optional[Any] = None,			_UpperCAmelCase     :					Union[str, Any] = PILImageResampling.BILINEAR,			_UpperCAmelCase     :					List[Any] = True,			_UpperCAmelCase     :					Union[str, Any] = None,			_UpperCAmelCase     :					str = True,			_UpperCAmelCase     :					Union[str, Any] = 1 / 2_5_5,			_UpperCAmelCase     :					Any = True,			_UpperCAmelCase     :					Union[str, Any] = None,			_UpperCAmelCase     :					Tuple = None,			**_UpperCAmelCase     :					str,			)				->			Tuple:





														"""simple docstring"""
														super().__init__(**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						size if size is not None else {"shortest_edge": 2_5_6}
														SCREAMING_SNAKE_CASE__       :      Any     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
														SCREAMING_SNAKE_CASE__       :      List[str]     =						get_size_dict(_UpperCAmelCase,			param_name="crop_size"						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						do_resize
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						size
														SCREAMING_SNAKE_CASE__       :      str     =						resample
														SCREAMING_SNAKE_CASE__       :      Dict     =						do_center_crop
														SCREAMING_SNAKE_CASE__       :      int     =						crop_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						do_rescale
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						rescale_factor
														SCREAMING_SNAKE_CASE__       :      List[str]     =						do_normalize
														SCREAMING_SNAKE_CASE__       :      str     =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														SCREAMING_SNAKE_CASE__       :      str     =						image_std if image_std is not None else IMAGENET_STANDARD_STD






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[Any] = PILImageResampling.BICUBIC,			_UpperCAmelCase     :					List[str] = None,			**_UpperCAmelCase     :					Optional[int],			)				->			str:





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






							def  A_     ( self     :					str,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Union[str, Any] = None,			**_UpperCAmelCase     :					Optional[int],			)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						get_size_dict(_UpperCAmelCase						)
														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(_UpperCAmelCase,			size=(size["height"], size["width"]),			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					Dict = None,			**_UpperCAmelCase     :					Any						)				->			int:





														"""simple docstring"""
														return rescale(_UpperCAmelCase,			scale=_UpperCAmelCase,			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Dict = None,			**_UpperCAmelCase     :					Tuple,			)				->			Tuple:





														"""simple docstring"""
														return normalize(_UpperCAmelCase,			mean=_UpperCAmelCase,			std=_UpperCAmelCase,			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Any = None,			_UpperCAmelCase     :					Optional[int] = None,			_UpperCAmelCase     :					Any = None,			_UpperCAmelCase     :					Union[str, Any] = None,			_UpperCAmelCase     :					Union[str, Any] = None,			_UpperCAmelCase     :					int = None,			_UpperCAmelCase     :					int = None,			_UpperCAmelCase     :					List[str] = None,			_UpperCAmelCase     :					int = None,			_UpperCAmelCase     :					Any = None,			_UpperCAmelCase     :					int = None,			_UpperCAmelCase     :					Union[str, Any] = ChannelDimension.FIRST,			**_UpperCAmelCase     :					str,			)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						do_resize if do_resize is not None else self.do_resize
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						size if size is not None else self.size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						resample if resample is not None else self.resample
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						do_center_crop if do_center_crop is not None else self.do_center_crop
														SCREAMING_SNAKE_CASE__       :      List[str]     =						crop_size if crop_size is not None else self.crop_size
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						get_size_dict(_UpperCAmelCase,			param_name="crop_size"						)
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						do_rescale if do_rescale is not None else self.do_rescale
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						rescale_factor if rescale_factor is not None else self.rescale_factor
														SCREAMING_SNAKE_CASE__       :      int     =						do_normalize if do_normalize is not None else self.do_normalize
														SCREAMING_SNAKE_CASE__       :      str     =						image_mean if image_mean is not None else self.image_mean
														SCREAMING_SNAKE_CASE__       :      str     =						image_std if image_std is not None else self.image_std

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						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."						)

														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.
														SCREAMING_SNAKE_CASE__       :      Any     =						[to_numpy_array(_UpperCAmelCase						) for image in images]

														if do_resize:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						[self.resize(image=_UpperCAmelCase,			size=_UpperCAmelCase,			resample=_UpperCAmelCase						) for image in images]

														if do_center_crop:
																					SCREAMING_SNAKE_CASE__       :      str     =						[self.center_crop(image=_UpperCAmelCase,			size=_UpperCAmelCase						) for image in images]

														if do_rescale:
																					SCREAMING_SNAKE_CASE__       :      int     =						[self.rescale(image=_UpperCAmelCase,			scale=_UpperCAmelCase						) for image in images]

														if do_normalize:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						[self.normalize(image=_UpperCAmelCase,			mean=_UpperCAmelCase,			std=_UpperCAmelCase						) for image in images]

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

														SCREAMING_SNAKE_CASE__       :      Dict     =						{"pixel_values": images}
														return BatchFeature(data=_UpperCAmelCase,			tensor_type=_UpperCAmelCase						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Dict = None						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						outputs.logits

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

																					if is_torch_tensor(_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      str     =						target_sizes.numpy()

																					SCREAMING_SNAKE_CASE__       :      Tuple     =						[]

																					for idx in range(len(_UpperCAmelCase						)						):
																												SCREAMING_SNAKE_CASE__       :      Any     =						torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0						),			size=target_sizes[idx],			mode="bilinear",			align_corners=_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						resized_logits[0].argmax(dim=0						)
																												semantic_segmentation.append(_UpperCAmelCase						)
														else:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						logits.argmax(dim=1						)
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

														return semantic_segmentation













def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str       )	->    int:

							'''simple docstring'''






							if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
														raise ValueError("String lengths must match!"       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						0

							for chara, chara in zip(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														if chara != chara:
																					count += 1

							return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()






