
'''simple docstring'''







import math
import sys



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= ""
						try:
												with open(_SCREAMING_SNAKE_CASE	,			"rb"  ) as binary_file:
																		_SCREAMING_SNAKE_CASE				= binary_file.read()
												for dat in data:
																		_SCREAMING_SNAKE_CASE				= F'{dat:08b}'
																		result += curr_byte
												return result
						except OSError:
												print("File not accessible"  )
												sys.exit()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= {"0": "0", "1": "1"}
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= "", ""
						_SCREAMING_SNAKE_CASE				= len(_SCREAMING_SNAKE_CASE  )

						for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
												curr_string += data_bits[i]
												if curr_string not in lexicon:
																		continue

												_SCREAMING_SNAKE_CASE				= lexicon[curr_string]
												result += last_match_id
												_SCREAMING_SNAKE_CASE				= last_match_id + "0"

												if math.loga(_SCREAMING_SNAKE_CASE  ).is_integer():
																		_SCREAMING_SNAKE_CASE				= {}
																		for curr_key in list(_SCREAMING_SNAKE_CASE  ):
																								_SCREAMING_SNAKE_CASE				= lexicon.pop(_SCREAMING_SNAKE_CASE  )
																		_SCREAMING_SNAKE_CASE				= new_lex

												_SCREAMING_SNAKE_CASE				= last_match_id + "1"
												index += 1
												_SCREAMING_SNAKE_CASE				= ""
						return result



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= 8
						try:
												with open(_SCREAMING_SNAKE_CASE	,			"wb"  ) as opened_file:
																		_SCREAMING_SNAKE_CASE				= [
																		    to_write[i : i + byte_length]
																		    for i in range(0	,			len(_SCREAMING_SNAKE_CASE  )	,			_SCREAMING_SNAKE_CASE  )
																		]

																		if len(result_byte_array[-1]  ) % byte_length == 0:
																								result_byte_array.append("10000000"  )
																		else:
																								result_byte_array[-1] += "1" + "0" * (
																								    byte_length - len(result_byte_array[-1]  ) - 1
																								)

																		for elem in result_byte_array[:-1]:
																								opened_file.write(int(_SCREAMING_SNAKE_CASE	,			2  ).to_bytes(1	,			byteorder="big"  )  )
						except OSError:
												print("File not accessible"  )
												sys.exit()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= 0
						for letter in data_bits:
												if letter == "1":
																		break
												counter += 1

						_SCREAMING_SNAKE_CASE				= data_bits[counter:]
						_SCREAMING_SNAKE_CASE				= data_bits[counter + 1 :]
						return data_bits



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= read_file_binary(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= remove_prefix(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= decompress_data(_SCREAMING_SNAKE_CASE  )
						write_file_binary(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
						compress(sys.argv[1], sys.argv[2])

'''simple docstring'''







import unittest

import numpy as np

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 GLPNImageProcessor


class 			lowerCAmelCase (				unittest.TestCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=3					,						UpperCamelCase=18					,						UpperCamelCase=30					,						UpperCamelCase=400					,						UpperCamelCase=True					,						UpperCamelCase=32					,						UpperCamelCase=True					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= min_resolution
								_SCREAMING_SNAKE_CASE				= max_resolution
								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size_divisor
								_SCREAMING_SNAKE_CASE				= do_rescale




		def     lowercase       (				self      ):
								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Dict       =					GLPNImageProcessor if is_vision_available() else None




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= GLPNImageProcessingTester(self      )




		@property
		def     lowercase       (				self      ):
								return self.image_processor_tester.prepare_image_processor_dict()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_resize"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"size_divisor"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"resample"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_rescale"      )      )




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						Image.Image      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						numpify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						np.ndarray      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						torchify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						torch.Tensor      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )