






"""simple docstring"""



import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import SegformerImageProcessor





class  _UpperCAmelCase	(			unittest.TestCase							):




					def __init__(    self					:						Tuple   ,						A					:						Optional[Any]   ,						A					:						List[Any]=7   ,						A					:						Optional[Any]=3   ,						A					:						Optional[int]=30   ,						A					:						Any=4_00   ,						A					:						int=True   ,						A					:						Optional[int]=None   ,						A					:						int=True   ,						A					:						str=[0.5, 0.5, 0.5]   ,						A					:						Optional[Any]=[0.5, 0.5, 0.5]   ,						A					:						List[Any]=False   ,						)  ->  Tuple:
							lowercase_			:			Union[str, Any]								=       size if size is not None else {'''height''': 30, '''width''': 30}
							lowercase_			:			str								=       parent
							lowercase_			:			List[Any]								=       batch_size
							lowercase_			:			Any								=       num_channels
							lowercase_			:			Union[str, Any]								=       min_resolution
							lowercase_			:			str								=       max_resolution
							lowercase_			:			Tuple								=       do_resize
							lowercase_			:			List[Any]								=       size
							lowercase_			:			Dict								=       do_normalize
							lowercase_			:			Union[str, Any]								=       image_mean
							lowercase_			:			Optional[Any]								=       image_std
							lowercase_			:			List[Any]								=       do_reduce_labels






					def   A  (    self					:						Optional[int]       )  ->  List[Any]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_reduce_labels": self.do_reduce_labels,
							}



def 						lowercase   (    ):
		lowercase_			:			List[str]								=       load_dataset('''hf-internal-testing/fixtures_ade20k'''		, split='''test'''     )

		lowercase_			:			Optional[Any]								=       Image.open(dataset[0]['''file''']     )
		lowercase_			:			Any								=       Image.open(dataset[1]['''file''']     )

		return image, map



def 						lowercase   (    ):
		lowercase_			:			int								=       load_dataset('''hf-internal-testing/fixtures_ade20k'''		, split='''test'''     )

		lowercase_			:			Any								=       Image.open(dataset[0]['''file''']     )
		lowercase_			:			Tuple								=       Image.open(dataset[1]['''file''']     )
		lowercase_			:			Optional[int]								=       Image.open(dataset[2]['''file''']     )
		lowercase_			:			Tuple								=       Image.open(dataset[3]['''file''']     )

		return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class  _UpperCAmelCase	(			_A       ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:    Optional[Any]							   =       SegformerImageProcessor if is_vision_available() else None




					def   A  (    self					:						Union[str, Any]       )  ->  Tuple:
							lowercase_			:			Union[str, Any]								=       SegformerImageProcessingTester(self       )




					@property
					def   A  (    self					:						List[Any]       )  ->  Dict:
							return self.image_processor_tester.prepare_image_processor_dict()




					def   A  (    self					:						List[str]       )  ->  Dict:
							lowercase_			:			Optional[int]								=       self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(A   ,						'''do_resize'''       )       )
							self.assertTrue(hasattr(A   ,						'''size'''       )       )
							self.assertTrue(hasattr(A   ,						'''do_normalize'''       )       )
							self.assertTrue(hasattr(A   ,						'''image_mean'''       )       )
							self.assertTrue(hasattr(A   ,						'''image_std'''       )       )
							self.assertTrue(hasattr(A   ,						'''do_reduce_labels'''       )       )




					def   A  (    self					:						Optional[Any]       )  ->  Optional[Any]:
							lowercase_			:			Optional[Any]								=       self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size   ,						{'''height''': 30, '''width''': 30}       )
							self.assertEqual(image_processor.do_reduce_labels   ,						A       )

							lowercase_			:			Dict								=       self.image_processing_class.from_dict(self.image_processor_dict   ,						size=42   ,						reduce_labels=A       )
							self.assertEqual(image_processor.size   ,						{'''height''': 42, '''width''': 42}       )
							self.assertEqual(image_processor.do_reduce_labels   ,						A       )




					def   A  (    self					:						List[str]       )  ->  Optional[int]:
							pass




					def   A  (    self					:						Tuple       )  ->  Tuple:
							# Initialize image_processing
							lowercase_			:			List[str]								=       self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							lowercase_			:			Optional[int]								=       prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=A       )
							for image in image_inputs:
									self.assertIsInstance(A   ,						Image.Image       )

							# Test not batched input
							lowercase_			:			Union[str, Any]								=       image_processing(image_inputs[0]   ,						return_tensors='''pt'''       ).pixel_values
							self.assertEqual(
							    encoded_images.shape   ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)

							# Test batched
							lowercase_			:			Any								=       image_processing(A   ,						return_tensors='''pt'''       ).pixel_values
							self.assertEqual(
							    encoded_images.shape   ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)




					def   A  (    self					:						Dict       )  ->  Tuple:
							# Initialize image_processing
							lowercase_			:			Optional[Any]								=       self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							lowercase_			:			Dict								=       prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=A   ,						numpify=A       )
							for image in image_inputs:
									self.assertIsInstance(A   ,						np.ndarray       )

							# Test not batched input
							lowercase_			:			List[str]								=       image_processing(image_inputs[0]   ,						return_tensors='''pt'''       ).pixel_values
							self.assertEqual(
							    encoded_images.shape   ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)

							# Test batched
							lowercase_			:			Tuple								=       image_processing(A   ,						return_tensors='''pt'''       ).pixel_values
							self.assertEqual(
							    encoded_images.shape   ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)




					def   A  (    self					:						Tuple       )  ->  Dict:
							# Initialize image_processing
							lowercase_			:			Dict								=       self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							lowercase_			:			str								=       prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=A   ,						torchify=A       )
							for image in image_inputs:
									self.assertIsInstance(A   ,						torch.Tensor       )

							# Test not batched input
							lowercase_			:			List[Any]								=       image_processing(image_inputs[0]   ,						return_tensors='''pt'''       ).pixel_values
							self.assertEqual(
							    encoded_images.shape   ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)

							# Test batched
							lowercase_			:			Union[str, Any]								=       image_processing(A   ,						return_tensors='''pt'''       ).pixel_values
							self.assertEqual(
							    encoded_images.shape   ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)




					def   A  (    self					:						Union[str, Any]       )  ->  List[Any]:
							# Initialize image_processing
							lowercase_			:			Optional[Any]								=       self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							lowercase_			:			Optional[int]								=       prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=A   ,						torchify=A       )
							lowercase_			:			Tuple								=       []
							for image in image_inputs:
									self.assertIsInstance(A   ,						torch.Tensor       )
									maps.append(torch.zeros(image.shape[-2:]       ).long()       )

							# Test not batched input
							lowercase_			:			Tuple								=       image_processing(image_inputs[0]   ,						maps[0]   ,						return_tensors='''pt'''       )
							self.assertEqual(
							    encoding['''pixel_values'''].shape   ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)
							self.assertEqual(
							    encoding['''labels'''].shape   ,						(
							        1,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)
							self.assertEqual(encoding['''labels'''].dtype   ,						torch.long       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55       )

							# Test batched
							lowercase_			:			str								=       image_processing(A   ,						A   ,						return_tensors='''pt'''       )
							self.assertEqual(
							    encoding['''pixel_values'''].shape   ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)
							self.assertEqual(
							    encoding['''labels'''].shape   ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)
							self.assertEqual(encoding['''labels'''].dtype   ,						torch.long       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55       )

							# Test not batched input (PIL images)
							lowercase_	, lowercase_			:			Any								=       prepare_semantic_single_inputs()

							lowercase_			:			List[str]								=       image_processing(A   ,						A   ,						return_tensors='''pt'''       )
							self.assertEqual(
							    encoding['''pixel_values'''].shape   ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)
							self.assertEqual(
							    encoding['''labels'''].shape   ,						(
							        1,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)
							self.assertEqual(encoding['''labels'''].dtype   ,						torch.long       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55       )

							# Test batched input (PIL images)
							lowercase_	, lowercase_			:			Union[str, Any]								=       prepare_semantic_batch_inputs()

							lowercase_			:			int								=       image_processing(A   ,						A   ,						return_tensors='''pt'''       )
							self.assertEqual(
							    encoding['''pixel_values'''].shape   ,						(
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)
							self.assertEqual(
							    encoding['''labels'''].shape   ,						(
							        2,
							        self.image_processor_tester.size['''height'''],
							        self.image_processor_tester.size['''width'''],
							    )   ,						)
							self.assertEqual(encoding['''labels'''].dtype   ,						torch.long       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55       )






					def   A  (    self					:						Optional[int]       )  ->  List[str]:
							# Initialize image_processing
							lowercase_			:			Optional[Any]								=       self.image_processing_class(**self.image_processor_dict       )

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							lowercase_	, lowercase_			:			Any								=       prepare_semantic_single_inputs()
							lowercase_			:			Any								=       image_processing(A   ,						A   ,						return_tensors='''pt'''       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 1_50       )

							lowercase_			:			Dict								=       True
							lowercase_			:			List[str]								=       image_processing(A   ,						A   ,						return_tensors='''pt'''       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55       )











"""simple docstring"""



def 						lowercase   (    __snake_case    :			str = "mytext.txt"		, __snake_case    :			bool = False     ):
		import socket

		lowercase_			:			List[str]								=       1_2_3_1_2  # Reserve a port for your service.
		lowercase_			:			Dict								=       socket.socket()  # Create a socket object
		lowercase_			:			int								=       socket.gethostname()  # Get local machine name
		sock.bind((host, port)     )  # Bind to the port
		sock.listen(5     )  # Now wait for client connection.

		print('''Server listening....'''     )

		while True:
				lowercase_	, lowercase_			:			int								=       sock.accept()  # Establish connection with client.
				print(F'''Got connection from {addr}'''     )
				lowercase_			:			List[Any]								=       conn.recv(1_0_2_4     )
				print(F'''Server received: {data = }'''     )

				with open(__snake_case		, '''rb'''     ) as in_file:
						lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )
						while data:
								conn.send(__snake_case     )
								print(F'''Sent {data!r}'''     )
								lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )

				print('''Done sending'''     )
				conn.close()
				if testing:  # Allow the test to complete
						break

		sock.shutdown(1     )
		sock.close()


if __name__ == "__main__":
						send_file()



