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__						:						Tuple		  =			logging.get_logger(__name__)
class        snake_case (				_UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["""pixel_values"""]







					def __init__(       self     :			Any					,lowerCamelCase__     :			Any = True					,lowerCamelCase__     :			Tuple = None					,lowerCamelCase__     :			List[Any] = PILImageResampling.BILINEAR					,lowerCamelCase__     :			Optional[int] = True					,lowerCamelCase__     :			List[str] = None					,lowerCamelCase__     :			str = True					,lowerCamelCase__     :			int = 1 / 255					,lowerCamelCase__     :			Optional[int] = True					,lowerCamelCase__     :			List[str] = None					,lowerCamelCase__     :			List[str] = None					,**lowerCamelCase__     :			Union[str, Any]					,):
									super().__init__(**lowercase__    )
									UpperCAmelCase__    =							size if size is not None else {"""shortest_edge""": 256}
									UpperCAmelCase__    =							get_size_dict(lowercase__					,default_to_square=lowercase__    )
									UpperCAmelCase__    =							crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
									UpperCAmelCase__    =							get_size_dict(lowercase__					,param_name='crop_size'    )
									UpperCAmelCase__    =							do_resize
									UpperCAmelCase__    =							size
									UpperCAmelCase__    =							resample
									UpperCAmelCase__    =							do_center_crop
									UpperCAmelCase__    =							crop_size
									UpperCAmelCase__    =							do_rescale
									UpperCAmelCase__    =							rescale_factor
									UpperCAmelCase__    =							do_normalize
									UpperCAmelCase__    =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									UpperCAmelCase__    =							image_std if image_std is not None else IMAGENET_STANDARD_STD







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Any = PILImageResampling.BICUBIC					,lowerCamelCase__     :			List[Any] = None					,**lowerCamelCase__     :			str					,):
									UpperCAmelCase__    =							get_size_dict(lowercase__					,default_to_square=lowercase__    )
									if "shortest_edge" not in size:
													raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
									UpperCAmelCase__    =							get_resize_output_image_size(lowercase__					,size=size['shortest_edge']					,default_to_square=lowercase__    )
									return resize(lowercase__					,size=lowercase__					,resample=lowercase__					,data_format=lowercase__					,**lowercase__    )







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			str = None					,**lowerCamelCase__     :			Optional[Any]					,):
									UpperCAmelCase__    =							get_size_dict(lowercase__    )
									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(lowercase__					,size=(size['height'], size['width'])					,data_format=lowercase__					,**lowercase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[int] = None					,**lowerCamelCase__     :			int    ):
									return rescale(lowercase__					,scale=lowercase__					,data_format=lowercase__					,**lowercase__    )







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int = None					,**lowerCamelCase__     :			str					,):
									return normalize(lowercase__					,mean=lowercase__					,std=lowercase__					,data_format=lowercase__					,**lowercase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			int = None					,lowerCamelCase__     :			Union[str, Any] = None					,lowerCamelCase__     :			Optional[Any] = None					,lowerCamelCase__     :			str = None					,lowerCamelCase__     :			Tuple = None					,lowerCamelCase__     :			str = None					,lowerCamelCase__     :			List[str] = None					,lowerCamelCase__     :			Union[str, Any] = None					,lowerCamelCase__     :			int = None					,lowerCamelCase__     :			Union[str, Any] = None					,lowerCamelCase__     :			List[Any] = None					,lowerCamelCase__     :			List[str] = ChannelDimension.FIRST					,**lowerCamelCase__     :			Any					,):
									UpperCAmelCase__    =							do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__    =							size if size is not None else self.size
									UpperCAmelCase__    =							get_size_dict(lowercase__					,default_to_square=lowercase__    )
									UpperCAmelCase__    =							resample if resample is not None else self.resample
									UpperCAmelCase__    =							do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__    =							crop_size if crop_size is not None else self.crop_size
									UpperCAmelCase__    =							get_size_dict(lowercase__					,param_name='crop_size'    )
									UpperCAmelCase__    =							do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__    =							rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__    =							do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__    =							image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__    =							image_std if image_std is not None else self.image_std

									UpperCAmelCase__    =							make_list_of_images(lowercase__    )

									if not valid_images(lowercase__    ):
													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.
									UpperCAmelCase__    =							[to_numpy_array(lowercase__    ) for image in images]

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

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

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

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

									UpperCAmelCase__    =							[to_channel_dimension_format(lowercase__					,lowercase__    ) for image in images]

									UpperCAmelCase__    =							{"""pixel_values""": images}
									return BatchFeature(data=lowercase__					,tensor_type=lowercase__    )







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[Any] = None    ):
									UpperCAmelCase__    =							outputs.logits

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

													if is_torch_tensor(lowercase__    ):
																	UpperCAmelCase__    =							target_sizes.numpy()

													UpperCAmelCase__    =							[]

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

									return semantic_segmentation




"""simple docstring"""






import socket



def 						a_		(      ):
				UpperCAmelCase__    =							socket.socket(socket.AF_INET      ,    socket.SOCK_STREAM )
				UpperCAmelCase__    =							socket.gethostname()
				UpperCAmelCase__    =							1_2_3_1_2

				sock.connect((host, port) )
				sock.send(b'Hello server!' )

				with open('Received_file'      ,    'wb' ) as out_file:
								print('File opened' )
								print('Receiving data...' )
								while True:
												UpperCAmelCase__    =							sock.recv(1_0_2_4 )
												if not data:
																break
												out_file.write(lowerCamelCase )

				print('Successfully received the file' )
				sock.close()
				print('Connection closed' )


if __name__ == "__main__":
						main()



