
import numpy as np




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE="utf-8"      ,	_SCREAMING_SNAKE_CASE="surrogatepass"  )   ->      Optional[int]:

							"""simple docstring"""






							_UpperCAmelCase  :			Tuple	       =		bin(int.from_bytes(text.encode(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )      ,	"big"  )  )[2:]
							return bits.zfill(8 * ((len(_SCREAMING_SNAKE_CASE  ) + 7) // 8)  )




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE="utf-8"      ,	_SCREAMING_SNAKE_CASE="surrogatepass"  )   ->      Tuple:

							"""simple docstring"""






							_UpperCAmelCase  :			Tuple	       =		int(_SCREAMING_SNAKE_CASE      ,	2  )
							return n.to_bytes((n.bit_length() + 7) // 8      ,	"big"  ).decode(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  ) or "\0"




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      str:

							"""simple docstring"""






							if size_par + len(_SCREAMING_SNAKE_CASE  ) <= 2**size_par - (len(_SCREAMING_SNAKE_CASE  ) - 1):
														raise ValueError("size of parity don't match with size of data"  )

							_UpperCAmelCase  :			Union[str, Any]	       =		[]
							_UpperCAmelCase  :			Any	       =		[]
							_UpperCAmelCase  :			Tuple	       =		[bin(_SCREAMING_SNAKE_CASE  )[2:] for x in range(1      ,	size_par + len(_SCREAMING_SNAKE_CASE  ) + 1  )]

							# sorted information data for the size of the output data
							_UpperCAmelCase  :			Dict	       =		[]
							# data position template + parity
							_UpperCAmelCase  :			List[str]	       =		[]
							# parity bit counter
							_UpperCAmelCase  :			Optional[int]	       =		0
							# counter position of data bits
							_UpperCAmelCase  :			int	       =		0

							for x in range(1      ,	size_par + len(_SCREAMING_SNAKE_CASE  ) + 1  ):
														# Performs a template of bit positions - who should be given,
														# and who should be parity
														if qtd_bp < size_par:
																					if (np.log(_SCREAMING_SNAKE_CASE  ) / np.log(2  )).is_integer():
																												data_out_gab.append("P"  )
																												_UpperCAmelCase  :			List[str]	       =		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(_SCREAMING_SNAKE_CASE  )

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

														qtd_bp += 1

							# Mount the message
							_UpperCAmelCase  :			Optional[int]	       =		0  # parity bit counter
							for x in range(0      ,	size_par + len(_SCREAMING_SNAKE_CASE  )  ):
														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_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      int:

							"""simple docstring"""






							_UpperCAmelCase  :			str	       =		[]
							# Parity bit counter
							_UpperCAmelCase  :			Optional[Any]	       =		0
							# Counter p data bit reading
							_UpperCAmelCase  :			int	       =		0
							# list of parity received
							_UpperCAmelCase  :			Dict	       =		[]
							_UpperCAmelCase  :			Union[str, Any]	       =		[]

							for x in range(1      ,	len(_SCREAMING_SNAKE_CASE  ) + 1  ):
														# Performs a template of bit positions - who should be given,
														#  and who should be parity
														if qtd_bp < size_par and (np.log(_SCREAMING_SNAKE_CASE  ) / np.log(2  )).is_integer():
																					data_out_gab.append("P"  )
																					_UpperCAmelCase  :			Dict	       =		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
							_UpperCAmelCase  :			Any	       =		[]
							_UpperCAmelCase  :			List[Any]	       =		[]
							_UpperCAmelCase  :			Dict	       =		[bin(_SCREAMING_SNAKE_CASE  )[2:] for x in range(1      ,	size_par + len(_SCREAMING_SNAKE_CASE  ) + 1  )]

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

							for x in range(1      ,	size_par + len(_SCREAMING_SNAKE_CASE  ) + 1  ):
														# Performs a template position of bits - who should be given,
														# and who should be parity
														if qtd_bp < size_par and (np.log(_SCREAMING_SNAKE_CASE  ) / np.log(2  )).is_integer():
																					data_out_gab.append("P"  )
																					_UpperCAmelCase  :			List[Any]	       =		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(_SCREAMING_SNAKE_CASE  )

    # Calculates parity
							_UpperCAmelCase  :			int	       =		0  # parity bit counter
							for bp in range(1      ,	size_par + 1  ):
														# Bit counter one for a certain parity
														_UpperCAmelCase  :			Tuple	       =		0
														# Counter to control loop reading
														_UpperCAmelCase  :			Optional[Any]	       =		0
														for x in data_ord:
																					if x is not None:
																												try:
																																			_UpperCAmelCase  :			List[str]	       =		(bin_pos[cont_loop])[-1 * (bp)]
																												except IndexError:
																																			_UpperCAmelCase  :			List[str]	       =		"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
							_UpperCAmelCase  :			Optional[int]	       =		0  # Parity bit counter
							for x in range(0      ,	size_par + len(_SCREAMING_SNAKE_CASE  )  ):
														if data_ord[x] is None:
																					data_out.append(str(parity[cont_bp]  )  )
																					cont_bp += 1
														else:
																					data_out.append(data_ord[x]  )

							_UpperCAmelCase  :			Any	       =		parity_received == parity
							return data_output, ack






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						dtype=torch.float	,						)
													# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
													# camembert.eval()
													# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

													self.assertTrue(torch.allclose(output[:, :3, :3]	,						_lowerCamelCase	,						atol=1E-4			)			)




