





'''simple docstring'''



import numpy as np




def       _lowerCAmelCase  ( lowerCamelCase_	:     Union[str, Any]						,				lowerCamelCase_	:     Union[str, Any]="utf-8"						,				lowerCamelCase_	:     str="surrogatepass" ):
			__lowercase   =							bin(int.from_bytes(text.encode(lowerCamelCase_						,				lowerCamelCase_ )						,				'''big''' ) )[2:]
			return bits.zfill(8 * ((len(lowerCamelCase_ ) + 7) // 8) )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[Any]						,				lowerCamelCase_	:     List[Any]="utf-8"						,				lowerCamelCase_	:     List[str]="surrogatepass" ):
			__lowercase   =							int(lowerCamelCase_						,				2 )
			return n.to_bytes((n.bit_length() + 7) // 8						,				'''big''' ).decode(lowerCamelCase_						,				lowerCamelCase_ ) or "\0"




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[Any] ):
			if size_par + len(lowerCamelCase_ ) <= 2**size_par - (len(lowerCamelCase_ ) - 1):
						raise ValueError('''size of parity don\'t match with size of data''' )

			__lowercase   =							[]
			__lowercase   =							[]
			__lowercase   =							[bin(lowerCamelCase_ )[2:] for x in range(1						,				size_par + len(lowerCamelCase_ ) + 1 )]

			# sorted information data for the size of the output data
			__lowercase   =							[]
			# data position template + parity
			__lowercase   =							[]
			# parity bit counter
			__lowercase   =							0
			# counter position of data bits
			__lowercase   =							0

			for x in range(1						,				size_par + len(lowerCamelCase_ ) + 1 ):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(lowerCamelCase_ ) / np.log(2 )).is_integer():
												data_out_gab.append('''P''' )
												__lowercase   =							qtd_bp + 1
									else:
												data_out_gab.append('''D''' )
						else:
									data_out_gab.append('''D''' )

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data] )
									cont_data += 1
						else:
									data_ord.append(lowerCamelCase_ )

    # Calculates parity
			__lowercase   =							0  # parity bit counter
			for bp in range(1						,				size_par + 1 ):
						# Bit counter one for a given parity
						__lowercase   =							0
						# counter to control the loop reading
						__lowercase   =							0
						for x in data_ord:
									if x is not None:
												try:
															__lowercase   =							(bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															__lowercase   =							'''0'''
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2 )

						qtd_bp += 1

			# Mount the message
			__lowercase   =							0  # parity bit counter
			for x in range(0						,				size_par + len(lowerCamelCase_ ) ):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp] ) )
									cont_bp += 1
						else:
									data_out.append(data_ord[x] )

			return data_out




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[Any]						,				lowerCamelCase_	:     Optional[int] ):
			__lowercase   =							[]
			# Parity bit counter
			__lowercase   =							0
			# Counter p data bit reading
			__lowercase   =							0
			# list of parity received
			__lowercase   =							[]
			__lowercase   =							[]

			for x in range(1						,				len(lowerCamelCase_ ) + 1 ):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(lowerCamelCase_ ) / np.log(2 )).is_integer():
									data_out_gab.append('''P''' )
									__lowercase   =							qtd_bp + 1
						else:
									data_out_gab.append('''D''' )

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data] )
						else:
									parity_received.append(data[cont_data] )
						cont_data += 1

			# -----------calculates the parity with the data
			__lowercase   =							[]
			__lowercase   =							[]
			__lowercase   =							[bin(lowerCamelCase_ )[2:] for x in range(1						,				size_par + len(lowerCamelCase_ ) + 1 )]

			#  sorted information data for the size of the output data
			__lowercase   =							[]
			# Data position feedback + parity
			__lowercase   =							[]
			# Parity bit counter
			__lowercase   =							0
			# Counter p data bit reading
			__lowercase   =							0

			for x in range(1						,				size_par + len(lowerCamelCase_ ) + 1 ):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(lowerCamelCase_ ) / np.log(2 )).is_integer():
									data_out_gab.append('''P''' )
									__lowercase   =							qtd_bp + 1
						else:
									data_out_gab.append('''D''' )

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data] )
									cont_data += 1
						else:
									data_ord.append(lowerCamelCase_ )

    # Calculates parity
			__lowercase   =							0  # parity bit counter
			for bp in range(1						,				size_par + 1 ):
						# Bit counter one for a certain parity
						__lowercase   =							0
						# Counter to control loop reading
						__lowercase   =							0
						for x in data_ord:
									if x is not None:
												try:
															__lowercase   =							(bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															__lowercase   =							'''0'''
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2 ) )

						qtd_bp += 1

			# Mount the message
			__lowercase   =							0  # Parity bit counter
			for x in range(0						,				size_par + len(lowerCamelCase_ ) ):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp] ) )
									cont_bp += 1
						else:
									data_out.append(data_ord[x] )

			__lowercase   =							parity_received == parity
			return data_output, ack







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 ViTImageProcessor

class 				_UpperCamelCase (    unittest.TestCase     ):






			'''simple docstring'''

			def __init__(				self       :				Optional[int]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				List[Any]=7  ,		snake_case_       :				int=3  ,		snake_case_       :				Tuple=18  ,		snake_case_       :				List[Any]=30  ,		snake_case_       :				Tuple=400  ,		snake_case_       :				Dict=True  ,		snake_case_       :				List[str]=None  ,		snake_case_       :				Any=True  ,		snake_case_       :				str=[0.5, 0.5, 0.5]  ,		snake_case_       :				Optional[Any]=[0.5, 0.5, 0.5]  ,		):
							UpperCamelCase_:							int		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Any		=  parent
							UpperCamelCase_:							List[str]		=  batch_size
							UpperCamelCase_:							Union[str, Any]		=  num_channels
							UpperCamelCase_:							str		=  image_size
							UpperCamelCase_:							Optional[int]		=  min_resolution
							UpperCamelCase_:							Optional[Any]		=  max_resolution
							UpperCamelCase_:							Dict		=  do_resize
							UpperCamelCase_:							Union[str, Any]		=  size
							UpperCamelCase_:							Optional[Any]		=  do_normalize
							UpperCamelCase_:							str		=  image_mean
							UpperCamelCase_:							int		=  image_std




			def 					lowerCAmelCase__		(				self       :				Tuple					):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}




@require_torch
@require_vision
class 				_UpperCamelCase (    _A   ,				unittest.TestCase     ):






			'''simple docstring'''

			__UpperCamelCase					:  str    					=       ViTImageProcessor if is_vision_available() else None

			def 					lowerCAmelCase__		(				self       :				Any					):
							UpperCamelCase_:							Tuple		=  ViTImageProcessingTester(self					)

			@property
			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							return self.image_processor_tester.prepare_image_processor_dict()

			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_mean"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_std"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""size"""					)					)

			def 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18}					)

							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42					)
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42}					)

			def 					lowerCAmelCase__		(				self       :				int					):
							pass

			def 					lowerCAmelCase__		(				self       :				int					):
							# Initialize image_processing
							UpperCamelCase_:							List[str]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							UpperCamelCase_:							List[Any]		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		Image.Image					)

							# Test not batched input
							UpperCamelCase_:							Optional[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
							UpperCamelCase_:							List[str]		=  image_processing(snake_case_  ,		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 					lowerCAmelCase__		(				self       :				str					):
							# Initialize image_processing
							UpperCamelCase_:							List[str]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							UpperCamelCase_:							Tuple		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		numpify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		np.ndarray					)

							# Test not batched input
							UpperCamelCase_:							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
							UpperCamelCase_:							Dict		=  image_processing(snake_case_  ,		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 					lowerCAmelCase__		(				self       :				Any					):
							# Initialize image_processing
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCamelCase_:							Dict		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		torchify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		torch.Tensor					)

							# Test not batched input
							UpperCamelCase_:							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
							UpperCamelCase_:							Any		=  image_processing(snake_case_  ,		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"""],
							    )  ,		)






