





import numpy as np



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case="utf-8"	,      snake_case="surrogatepass"       )		->						int:
		__lowercase					=    bin(int.from_bytes(text.encode(snake_case	,      snake_case       )	,      'big'       )       )[2:]
		return bits.zfill(8 * ((len(snake_case       ) + 7) // 8)       )



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



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						int:
		if size_par + len(snake_case       ) <= 2**size_par - (len(snake_case       ) - 1):
				raise ValueError('size of parity don\'t match with size of data'       )

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

    # 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(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    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						List[str]:
		__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(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(snake_case       ) / 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(snake_case       )[2:] for x in range(1	,      size_par + len(snake_case       ) + 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(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(snake_case       ) / 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(snake_case       )

    # 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(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]       )

		__lowercase					=    parity_received == parity
		return data_output, ack











import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_torch
@require_torchaudio
@require_sentencepiece
class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    List[Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    tempfile.mkdtemp()

			__lowercase					=    ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
			__lowercase					=    dict(zip(__lowerCamelCase				,			range(len(__lowerCamelCase      )      )      )      )
			__lowercase					=    Path(self.tmpdirname      )
			save_json(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['vocab_file']      )
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
					copyfile(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['spm_file']      )

			__lowercase					=    SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
			tokenizer.save_pretrained(self.tmpdirname      )

			__lowercase					=    {
			    'feature_size': 24,
			    'num_mel_bins': 24,
			    'padding_value': 0.0,
			    'sampling_rate': 16_000,
			    'return_attention_mask': False,
			    'do_normalize': True,
			}
			save_json(__lowerCamelCase				,			save_dir / FEATURE_EXTRACTOR_NAME      )




	def       UpperCAmelCase (	self       :    Dict				,			**__lowerCamelCase       :    Dict      )					->   Union[str, Any]:

			'''simple docstring'''




			return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    str				,			**__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			shutil.rmtree(self.tmpdirname      )




	def       UpperCAmelCase (	self       :    str      )					->   Any:

			'''simple docstring'''




			__lowercase					=    self.get_tokenizer()
			__lowercase					=    self.get_feature_extractor()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			processor.save_pretrained(self.tmpdirname      )
			__lowercase					=    SpeechaTextProcessor.from_pretrained(self.tmpdirname      )

			self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer.get_vocab()      )
			self.assertIsInstance(processor.tokenizer				,			__lowerCamelCase      )

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[str]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    SpeechaTextProcessor(
			    tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()      )
			processor.save_pretrained(self.tmpdirname      )

			__lowercase					=    self.get_tokenizer(bos_token='(BOS)'				,			eos_token='(EOS)'      )
			__lowercase					=    self.get_feature_extractor(do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			__lowercase					=    SpeechaTextProcessor.from_pretrained(
			    self.tmpdirname				,			bos_token='(BOS)'				,			eos_token='(EOS)'				,			do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer_add_kwargs.get_vocab()      )
			self.assertIsInstance(processor.tokenizer				,			__lowerCamelCase      )

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor_add_kwargs.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    floats_list((3, 1_000)      )

			__lowercase					=    feature_extractor(__lowerCamelCase				,			return_tensors='np'      )
			__lowercase					=    processor(__lowerCamelCase				,			return_tensors='np'      )

			for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()				,			input_processor[key].sum()				,			delta=1E-2      )




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    'This is a test string'

			__lowercase					=    processor(text=__lowerCamelCase      )

			__lowercase					=    tokenizer(__lowerCamelCase      )

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]      )




	def       UpperCAmelCase (	self       :    str      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__lowercase					=    processor.batch_decode(__lowerCamelCase      )
			__lowercase					=    tokenizer.batch_decode(__lowerCamelCase      )

			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    Tuple      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			self.assertListEqual(
			    processor.model_input_names				,			feature_extractor.model_input_names				,			msg='`processor` and `feature_extractor` model input names do not match'				,			)




