






import numpy as np







def 						_UpperCamelCase (     UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Dict="utf-8"     ,							UpperCamelCase_							:       List[str]="surrogatepass"		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    bin(int.from_bytes(text.encode(UpperCamelCase_     ,							UpperCamelCase_		)     ,							'big'		)		)[2:]
						return bits.zfill(8 * ((len(UpperCamelCase_		) + 7) // 8)		)







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       str="utf-8"     ,							UpperCamelCase_							:       List[str]="surrogatepass"		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    int(UpperCamelCase_     ,							2		)
						return n.to_bytes((n.bit_length() + 7) // 8     ,							'big'		).decode(UpperCamelCase_     ,							UpperCamelCase_		) or "\0"







def 						_UpperCamelCase (     UpperCamelCase_							:       List[str]     ,							UpperCamelCase_							:       Any		)	-> Union[str, Any]:

						"""simple docstring"""



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

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

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

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

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

												qtd_bp += 1

						# Mount the message
						lowerCAmelCase__         =    0  # parity bit counter
						for x in range(0     ,							size_par + len(UpperCamelCase_		)		):
												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 						_UpperCamelCase (     UpperCamelCase_							:       Any     ,							UpperCamelCase_							:       List[str]		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						# Parity bit counter
						lowerCAmelCase__         =    0
						# Counter p data bit reading
						lowerCAmelCase__         =    0
						# list of parity received
						lowerCAmelCase__         =    []
						lowerCAmelCase__         =    []

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

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

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

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

						lowerCAmelCase__         =    parity_received == parity
						return data_output, ack













from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Tuple				=       DPRContextEncoderTokenizer
						_SCREAMING_SNAKE_CASE						:							Any				=       DPRContextEncoderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							str				=       True




@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       DPRQuestionEncoderTokenizer
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       DPRQuestionEncoderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       True




@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Any				=       DPRReaderTokenizer
						_SCREAMING_SNAKE_CASE						:							int				=       DPRReaderTokenizerFast
						_SCREAMING_SNAKE_CASE						:							List[str]				=       True






						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer_class.from_pretrained('bert-base-uncased' )

												lowerCAmelCase__         =    tokenizer.encode('question sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('title sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('text sequence ' * 4  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    [[1_01] + text_a + [1_02] + text_a + [1_02] + text_a]
												lowerCAmelCase__         =    BatchEncoding({'input_ids': input_ids} )

												lowerCAmelCase__         =    [[0] * len(input_ids[0] )]
												lowerCAmelCase__         =    [[0] * len(input_ids[0] )]
												lowerCAmelCase__         =    [0]
												lowerCAmelCase__         =    DPRReaderOutput(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )

												lowerCAmelCase__    ,							lowerCAmelCase__         =    8, 9
												lowerCAmelCase__         =    10
												lowerCAmelCase__         =    10
												lowerCAmelCase__         =    tokenizer.decode_best_spans(_UpperCamelCase  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].start_index  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].end_index  ,    _UpperCamelCase )
												self.assertEqual(predicted_spans[0].doc_id  ,    0 )







						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer_class.from_pretrained('bert-base-uncased' )

												lowerCAmelCase__         =    tokenizer.encode('question sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('title sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    tokenizer.encode('text sequence'  ,    add_special_tokens=_UpperCamelCase )
												lowerCAmelCase__         =    [1_01] + text_a + [1_02] + text_a + [1_02] + text_a
												lowerCAmelCase__         =    tokenizer(questions=['question sequence']  ,    titles=['title sequence']  ,    texts=['text sequence'] )
												self.assertIn('input_ids'  ,    _UpperCamelCase )
												self.assertIn('attention_mask'  ,    _UpperCamelCase )
												self.assertListEqual(encoded_input['input_ids'][0]  ,    _UpperCamelCase )





