



'''simple docstring'''

import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.array:
		_a     :      Optional[int]        =							f"""{sampling_rate}"""
		_a     :      Any        =							'1'
		_a     :      Optional[int]        =							'f32le'
		_a     :      Any        =							[
		    'ffmpeg',
		    '-i',
		    'pipe:0',
		    '-ac',
		    ac,
		    '-ar',
		    ar,
		    '-f',
		    format_for_conversion,
		    '-hide_banner',
		    '-loglevel',
		    'quiet',
		    'pipe:1',
		]

		try:
				with subprocess.Popen(lowerCAmelCase_						,			stdin=subprocess.PIPE						,			stdout=subprocess.PIPE  ) as ffmpeg_process:
						_a     :      int        =							ffmpeg_process.communicate(lowerCAmelCase_  )
		except FileNotFoundError as error:
				raise ValueError('ffmpeg was not found but is required to load audio files from filename'  ) from error
		_a     :      int        =							output_stream[0]
		_a     :      List[str]        =							np.frombuffer(lowerCAmelCase_						,			np.floataa  )
		if audio.shape[0] == 0:
				raise ValueError('Malformed soundfile'  )
		return audio



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = "f32le"						,			)			->      Union[str, Any]:
		_a     :      List[str]        =							f"""{sampling_rate}"""
		_a     :      List[str]        =							'1'
		if format_for_conversion == "s16le":
				_a     :      List[Any]        =							2
		elif format_for_conversion == "f32le":
				_a     :      Dict        =							4
		else:
				raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

		_a     :      Any        =							platform.system()
		if system == "Linux":
				_a     :      Union[str, Any]        =							'alsa'
				_a     :      Union[str, Any]        =							'default'
		elif system == "Darwin":
				_a     :      Any        =							'avfoundation'
				_a     :      Optional[int]        =							':0'
		elif system == "Windows":
				_a     :      str        =							'dshow'
				_a     :      Tuple        =							'default'

		_a     :      str        =							[
		    'ffmpeg',
		    '-f',
		    format_,
		    '-i',
		    input_,
		    '-ac',
		    ac,
		    '-ar',
		    ar,
		    '-f',
		    format_for_conversion,
		    '-fflags',
		    'nobuffer',
		    '-hide_banner',
		    '-loglevel',
		    'quiet',
		    'pipe:1',
		]
		_a     :      Any        =							int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
		_a     :      Union[str, Any]        =							_ffmpeg_stream(lowerCAmelCase_						,			lowerCAmelCase_  )
		for item in iterator:
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = "f32le"						,			)			->      str:
		if stream_chunk_s is not None:
				_a     :      str        =							stream_chunk_s
		else:
				_a     :      List[str]        =							chunk_length_s

		_a     :      int        =							ffmpeg_microphone(lowerCAmelCase_						,			lowerCAmelCase_						,			format_for_conversion=lowerCAmelCase_  )
		if format_for_conversion == "s16le":
				_a     :      Optional[Any]        =							np.intaa
				_a     :      List[Any]        =							2
		elif format_for_conversion == "f32le":
				_a     :      Tuple        =							np.floataa
				_a     :      Any        =							4
		else:
				raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

		if stride_length_s is None:
				_a     :      str        =							chunk_length_s / 6
		_a     :      Any        =							int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
		if isinstance(lowerCAmelCase_						,			(int, float)  ):
				_a     :      List[str]        =							[stride_length_s, stride_length_s]

		_a     :      str        =							int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
		_a     :      List[str]        =							int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
		_a     :      Any        =							datetime.datetime.now()
		_a     :      Dict        =							datetime.timedelta(seconds=lowerCAmelCase_  )
		for item in chunk_bytes_iter(lowerCAmelCase_						,			lowerCAmelCase_						,			stride=(stride_left, stride_right)						,			stream=lowerCAmelCase_  ):
				# Put everything back in numpy scale
				_a     :      List[Any]        =							np.frombuffer(item['raw']						,			dtype=lowerCAmelCase_  )
				_a     :      List[str]        =							(
				    item['stride'][0] // size_of_sample,
				    item['stride'][1] // size_of_sample,
				)
				_a     :      Union[str, Any]        =							sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 10 * delta:
						# We're late !! SKIP
						continue
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = False  )			->      List[Any]:
		_a     :      Tuple        =							B''
		_a       ,					_a     :      str        =							stride
		if stride_left + stride_right >= chunk_len:
				raise ValueError(
				    f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""  )
		_a     :      Optional[int]        =							0
		for raw in iterator:
				acc += raw
				if stream and len(lowerCAmelCase_  ) < chunk_len:
						_a     :      str        =							(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(lowerCAmelCase_  ) >= chunk_len:
								# We are flushing the accumulator
								_a     :      Union[str, Any]        =							(_stride_left, stride_right)
								_a     :      Dict        =							{'raw': acc[:chunk_len], 'stride': stride}
								if stream:
										_a     :      List[str]        =							False
								yield item
								_a     :      int        =							stride_left
								_a     :      List[Any]        =							acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(lowerCAmelCase_  ) > stride_left:
				_a     :      str        =							{'raw': acc, 'stride': (_stride_left, 0)}
				if stream:
						_a     :      str        =							False
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Tuple:
		_a     :      Optional[Any]        =							2**24  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase_						,			stdout=subprocess.PIPE						,			bufsize=lowerCAmelCase_  ) as ffmpeg_process:
						while True:
								_a     :      Any        =							ffmpeg_process.stdout.read(lowerCAmelCase_  )
								if raw == b"":
										break
								yield raw
		except FileNotFoundError as error:
				raise ValueError('ffmpeg was not found but is required to stream audio files from filename'  ) from error









'''simple docstring'''

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Optional[int]   				=					BarthezTokenizer
							lowerCAmelCase       :    int   				=					BarthezTokenizerFast
							lowerCAmelCase       :    Dict   				=					True
							lowerCAmelCase       :    str   				=					True







							def 							__lowercase			(							self					:       List[Any]		):
									super().setUp()

									_a     :      List[Any]        =							BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'		)
									tokenizer.save_pretrained(self.tmpdirname		)
									tokenizer.save_pretrained(self.tmpdirname	,legacy_format=_UpperCAmelCase		)
									_a     :      Union[str, Any]        =							tokenizer







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      Optional[Any]        =							'<pad>'
									_a     :      List[Any]        =							1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase		)	,_UpperCAmelCase		)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase		)	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									_a     :      Any        =							list(self.get_tokenizer().get_vocab().keys()		)

									self.assertEqual(vocab_keys[0]	,'<s>'		)
									self.assertEqual(vocab_keys[1]	,'<pad>'		)
									self.assertEqual(vocab_keys[-1]	,'<mask>'		)
									self.assertEqual(len(_UpperCAmelCase		)	,101122		)







							def 							__lowercase			(							self					:       Dict		):
									self.assertEqual(self.get_tokenizer().vocab_size	,101122		)







							@require_torch
							def 							__lowercase			(							self					:       Dict		):
									_a     :      Any        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_a     :      Dict        =							[0, 57, 3018, 70307, 91, 2]

									_a     :      Dict        =							self.tokenizer(
									    _UpperCAmelCase	,max_length=len(_UpperCAmelCase		)	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,return_tensors='pt'		)
									self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

									self.assertEqual((2, 6)	,batch.input_ids.shape		)
									self.assertEqual((2, 6)	,batch.attention_mask.shape		)
									_a     :      Tuple        =							batch.input_ids.tolist()[0]
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									if not self.test_rust_tokenizer:
											return

									_a     :      str        =							self.get_tokenizer()
									_a     :      List[str]        =							self.get_rust_tokenizer()

									_a     :      Dict        =							'I was born in 92000, and this is falsé.'

									_a     :      List[Any]        =							tokenizer.tokenize(_UpperCAmelCase		)
									_a     :      Tuple        =							rust_tokenizer.tokenize(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Union[str, Any]        =							self.get_rust_tokenizer()
									_a     :      Any        =							tokenizer.encode(_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       Optional[int]		):
									# fmt: off
									_a     :      Optional[int]        =							{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									_a     :      Optional[Any]        =							[
									    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
									    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
									    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
									    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
									    'telles que la traduction et la synthèse de texte.',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCAmelCase	,model_name='moussaKam/mbarthez'	,revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'	,sequences=_UpperCAmelCase	,)




