
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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


SCREAMING_SNAKE_CASE				:Dict									=							logging.get_logger(__name__)





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	["pixel_values"]






	def __init__(			self   :					Tuple				,A   :					bool = True				,A   :					Dict[str, int] = None				,A   :					PILImageResampling = PILImageResampling.BILINEAR				,A   :					bool = True				,A   :					Union[int, float] = 1 / 2_55				,A   :					bool = True				,A   :					Dict[str, int] = None				,A   :					bool = True				,**A   :					List[Any]				,):
					super().__init__(**A					)
					__A       =					size if size is not None else {"shortest_edge": 2_24}
					__A       =					get_size_dict(A				,default_to_square=A					)
					__A       =					crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
					__A       =					get_size_dict(A				,param_name="crop_size"					)

					__A       =					do_resize
					__A       =					size
					__A       =					resample
					__A       =					do_rescale
					__A       =					rescale_factor
					__A       =					do_center_crop
					__A       =					crop_size
					__A       =					do_flip_channel_order






	def 							UpperCamelCase_    (			self   :					List[Any]				,A   :					np.ndarray				,A   :					Dict[str, int]				,A   :					PILImageResampling = PIL.Image.BILINEAR				,A   :					Optional[Union[str, ChannelDimension]] = None				,**A   :					Optional[Any]				,):
					__A       =					get_size_dict(A				,default_to_square=A					)
					if "shortest_edge" not in size:
									raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''					)
					__A       =					get_resize_output_image_size(A				,size=size["shortest_edge"]				,default_to_square=A					)
					return resize(A				,size=A				,resample=A				,data_format=A				,**A					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					np.ndarray				,A   :					Dict[str, int]				,A   :					Optional[Union[str, ChannelDimension]] = None				,**A   :					Optional[int]				,):
					__A       =					get_size_dict(A					)
					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()}'''					)
					return center_crop(A				,size=(size["height"], size["width"])				,data_format=A				,**A					)






	def 							UpperCamelCase_    (			self   :					str				,A   :					np.ndarray				,A   :					Union[int, float]				,A   :					Optional[Union[str, ChannelDimension]] = None				,**A   :					List[Any]				,):
					return rescale(A				,scale=A				,data_format=A				,**A					)






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					np.ndarray				,A   :					Optional[Union[str, ChannelDimension]] = None					):
					return flip_channel_order(A				,data_format=A					)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					ImageInput				,A   :					bool = None				,A   :					Dict[str, int] = None				,A   :					PILImageResampling = None				,A   :					bool = None				,A   :					float = None				,A   :					bool = None				,A   :					Dict[str, int] = None				,A   :					bool = None				,A   :					Optional[Union[str, TensorType]] = None				,A   :					ChannelDimension = ChannelDimension.FIRST				,**A   :					str				,):
					__A       =					do_resize if do_resize is not None else self.do_resize
					__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_center_crop if do_center_crop is not None else self.do_center_crop
					__A       =					(
					    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
					)

					__A       =					size if size is not None else self.size
					__A       =					get_size_dict(A				,default_to_square=A					)
					__A       =					crop_size if crop_size is not None else self.crop_size
					__A       =					get_size_dict(A				,param_name="crop_size"					)

					__A       =					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_resize and size is None:
									raise ValueError("Size 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_center_crop and crop_size is None:
									raise ValueError("Crop size must be specified if do_center_crop is True."					)

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

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

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

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

					# the pretrained checkpoints assume images are BGR, not RGB
					if do_flip_channel_order:
									__A       =					[self.flip_channel_order(image=A					) for image in images]

					__A       =					[to_channel_dimension_format(A				,A					) for image in images]

					__A       =					{"pixel_values": images}
					return BatchFeature(data=A				,tensor_type=A					)







	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					Optional[int]				,A   :					List[Tuple] = None					):
					__A       =					outputs.logits

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

									if is_torch_tensor(A					):
													__A       =					target_sizes.numpy()

									__A       =					[]

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

					return semantic_segmentation




import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  parse(importlib.metadata.version('''torch'''))



def       _lowerCAmelCase    (				lowerCAmelCase_				:Union[str, Version]		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:str				)->Any:
   '''simple docstring'''






   if operation not in STR_OPERATION_TO_FUNC.keys():
      raise ValueError(F'''`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys()				)}, received {operation}'''				)
   snake_case_            =       STR_OPERATION_TO_FUNC[operation]
   if isinstance(lowerCAmelCase_		,    lowerCAmelCase_				):
      snake_case_            =       parse(importlib.metadata.version(lowerCAmelCase_				)				)
   return operation(lowerCAmelCase_		,    parse(lowerCAmelCase_				)				)



def       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:str				)->Any:
   '''simple docstring'''






   return compare_versions(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)




