




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


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=['''pixel_values''']

			def __init__( self			:   int       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   PILImageResampling = PILImageResampling.BILINEAR       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Union[int, float] = 1 / 255       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   bool = True       ,						**lowerCamelCase_			:   Tuple       ,						)						->   None:
								super().__init__(**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							size if size is not None else {'''shortest_edge''': 224}
								__magic_name__							:	List[Any]    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
								__magic_name__							:	Dict    							=							get_size_dict(lowerCamelCase_       ,						param_name='''crop_size'''  )

								__magic_name__							:	Optional[Any]    							=							do_resize
								__magic_name__							:	List[str]    							=							size
								__magic_name__							:	Union[str, Any]    							=							resample
								__magic_name__							:	List[Any]    							=							do_rescale
								__magic_name__							:	Union[str, Any]    							=							rescale_factor
								__magic_name__							:	List[str]    							=							do_center_crop
								__magic_name__							:	Optional[int]    							=							crop_size
								__magic_name__							:	List[str]    							=							do_flip_channel_order

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Dict[str, int]       ,						lowerCamelCase_			:   PILImageResampling = PIL.Image.BILINEAR       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   np.ndarray:
								__magic_name__							:	List[Any]    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								if "shortest_edge" not in size:
													raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''  )
								__magic_name__							:	List[str]    							=							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 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Dict[str, int]       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   Dict       ,						)						->   np.ndarray:
								__magic_name__							:	Union[str, Any]    							=							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()}'''  )
								return center_crop(lowerCamelCase_       ,						size=(size['''height'''], size['''width'''])       ,						data_format=lowerCamelCase_       ,						**lowerCamelCase_  )

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

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None  )						->   np.ndarray:
								return flip_channel_order(lowerCamelCase_       ,						data_format=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   ImageInput       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   PILImageResampling = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   float = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Optional[Union[str, TensorType]] = None       ,						lowerCamelCase_			:   ChannelDimension = ChannelDimension.FIRST       ,						**lowerCamelCase_			:   List[Any]       ,						)						->   PIL.Image.Image:
								__magic_name__							:	Tuple    							=							do_resize if do_resize is not None else self.do_resize
								__magic_name__							:	List[str]    							=							resample if resample is not None else self.resample
								__magic_name__							:	int    							=							do_rescale if do_rescale is not None else self.do_rescale
								__magic_name__							:	Optional[int]    							=							rescale_factor if rescale_factor is not None else self.rescale_factor
								__magic_name__							:	str    							=							do_center_crop if do_center_crop is not None else self.do_center_crop
								__magic_name__							:	Tuple    							=							(
								    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
								)

								__magic_name__							:	Tuple    							=							size if size is not None else self.size
								__magic_name__							:	Optional[int]    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								__magic_name__							:	int    							=							crop_size if crop_size is not None else self.crop_size
								__magic_name__							:	Optional[Any]    							=							get_size_dict(lowerCamelCase_       ,						param_name='''crop_size'''  )

								__magic_name__							:	Tuple    							=							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_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.
								__magic_name__							:	Tuple    							=							[to_numpy_array(lowerCamelCase_  ) for image in images]

								if do_resize:
													__magic_name__							:	int    							=							[self.resize(image=lowerCamelCase_       ,						size=lowerCamelCase_       ,						resample=lowerCamelCase_  ) for image in images]

								if do_center_crop:
													__magic_name__							:	Optional[Any]    							=							[self.center_crop(image=lowerCamelCase_       ,						size=lowerCamelCase_  ) for image in images]

								if do_rescale:
													__magic_name__							:	Optional[int]    							=							[self.rescale(image=lowerCamelCase_       ,						scale=lowerCamelCase_  ) for image in images]

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

								__magic_name__							:	Dict    							=							[to_channel_dimension_format(lowerCamelCase_       ,						lowerCamelCase_  ) for image in images]

								__magic_name__							:	List[Any]    							=							{'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase_       ,						tensor_type=lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   List[Tuple] = None  )						->   Union[str, Any]:
								__magic_name__							:	List[str]    							=							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_  ):
																		__magic_name__							:	List[Any]    							=							target_sizes.numpy()

													__magic_name__							:	Optional[int]    							=							[]

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

								return semantic_segmentation









import requests







def 		lowercase__   (		__A:	str							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							{'''Content-Type''': '''application/json'''}
					__magic_name__							:	Union[str, Any]    							=							requests.post(__A							,json={'''text''': message_body}							,headers=__A				)
					if response.status_code != 2_0_0:
										__magic_name__							:	int    							=							(
										    '''Request to slack returned an error '''
										    F'''{response.status_code}, the response is:\n{response.text}'''
										)
										raise ValueError(__A				)


if __name__ == "__main__":
	# Set the slack url to the one provided by Slack when you create the webhook at
	# https://my.slack.com/services/new/incoming-webhook/
	send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')



