'''simple docstring'''

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

import numpy as np



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=F'''{sampling_rate}'''
						_lowerCAmelCase										='1'
						_lowerCAmelCase										='f32le'
						_lowerCAmelCase										=[
						    'ffmpeg',
						    '-i',
						    'pipe:0',
						    '-ac',
						    ac,
						    '-ar',
						    ar,
						    '-f',
						    format_for_conversion,
						    '-hide_banner',
						    '-loglevel',
						    'quiet',
						    'pipe:1',
						]

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__ = "f32le"			,      ):


						'''simple docstring'''






						_lowerCAmelCase										=F'''{sampling_rate}'''
						_lowerCAmelCase										='1'
						if format_for_conversion == "s16le":
												_lowerCAmelCase										=2
						elif format_for_conversion == "f32le":
												_lowerCAmelCase										=4
						else:
												raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''      )

						_lowerCAmelCase										=platform.system()
						if system == "Linux":
												_lowerCAmelCase										='alsa'
												_lowerCAmelCase										='default'
						elif system == "Darwin":
												_lowerCAmelCase										='avfoundation'
												_lowerCAmelCase										=':0'
						elif system == "Windows":
												_lowerCAmelCase										='dshow'
												_lowerCAmelCase										='default'

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__ = None			,      a__ = None			,      a__ = "f32le"			,      ):


						'''simple docstring'''






						if stream_chunk_s is not None:
												_lowerCAmelCase										=stream_chunk_s
						else:
												_lowerCAmelCase										=chunk_length_s

						_lowerCAmelCase										=ffmpeg_microphone(a__			,      a__			,      format_for_conversion=a__      )
						if format_for_conversion == "s16le":
												_lowerCAmelCase										=np.intaa
												_lowerCAmelCase										=2
						elif format_for_conversion == "f32le":
												_lowerCAmelCase										=np.floataa
												_lowerCAmelCase										=4
						else:
												raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''      )

						if stride_length_s is None:
												_lowerCAmelCase										=chunk_length_s / 6
						_lowerCAmelCase										=int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
						if isinstance(a__			,      (int, float)      ):
												_lowerCAmelCase										=[stride_length_s, stride_length_s]

						_lowerCAmelCase										=int(round(sampling_rate * stride_length_s[0]      )      ) * size_of_sample
						_lowerCAmelCase										=int(round(sampling_rate * stride_length_s[1]      )      ) * size_of_sample
						_lowerCAmelCase										=datetime.datetime.now()
						_lowerCAmelCase										=datetime.timedelta(seconds=a__      )
						for item in chunk_bytes_iter(a__			,      a__			,      stride=(stride_left, stride_right)			,      stream=a__      ):
												# Put everything back in numpy scale
												_lowerCAmelCase										=np.frombuffer(item['raw']			,      dtype=a__      )
												_lowerCAmelCase										=(
												    item['stride'][0] // size_of_sample,
												    item['stride'][1] // size_of_sample,
												)
												_lowerCAmelCase										=sampling_rate
												audio_time += delta
												if datetime.datetime.now() > audio_time + 1_0 * delta:
																		# We're late !! SKIP
																		continue
												yield item



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__ = False      ):


						'''simple docstring'''






						_lowerCAmelCase										=B''
						_lowerCAmelCase						,				_lowerCAmelCase										=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}'''      )
						_lowerCAmelCase										=0
						for raw in iterator:
												acc += raw
												if stream and len(a__      ) < chunk_len:
																		_lowerCAmelCase										=(_stride_left, 0)
																		yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
												else:
																		while len(a__      ) >= chunk_len:
																								# We are flushing the accumulator
																								_lowerCAmelCase										=(_stride_left, stride_right)
																								_lowerCAmelCase										={'raw': acc[:chunk_len], 'stride': stride}
																								if stream:
																														_lowerCAmelCase										=False
																								yield item
																								_lowerCAmelCase										=stride_left
																								_lowerCAmelCase										=acc[chunk_len - stride_left - stride_right :]
    # Last chunk
						if len(a__      ) > stride_left:
												_lowerCAmelCase										={'raw': acc, 'stride': (_stride_left, 0)}
												if stream:
																		_lowerCAmelCase										=False
												yield item



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=2**2_4  # 16Mo
						try:
												with subprocess.Popen(a__			,      stdout=subprocess.PIPE			,      bufsize=a__      ) as ffmpeg_process:
																		while True:
																								_lowerCAmelCase										=ffmpeg_process.stdout.read(a__      )
																								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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {'''vocab_file''': '''vocab.txt'''}

lowercase_     		= {
    '''vocab_file''': {
        '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
        '''YituTech/conv-bert-medium-small''': (
            '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
        ),
        '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
    }
}

lowercase_     		= {
    '''YituTech/conv-bert-base''': 512,
    '''YituTech/conv-bert-medium-small''': 512,
    '''YituTech/conv-bert-small''': 512,
}


lowercase_     		= {
    '''YituTech/conv-bert-base''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							VOCAB_FILES_NAMES
		lowercase   :      Tuple       =							PRETRAINED_VOCAB_FILES_MAP
		lowercase   :      Optional[int]       =							PRETRAINED_INIT_CONFIGURATION
		lowercase   :      int       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase   :      List[str]       =							ConvBertTokenizer






		def __init__(   self		,						__A=None		,						__A=None		,						__A=True		,						__A="[UNK]"		,						__A="[SEP]"		,						__A="[PAD]"		,						__A="[CLS]"		,						__A="[MASK]"		,						__A=True		,						__A=None		,						**__A		,						) ->     Union[str, Any]:
								super().__init__(
								    __A		,						tokenizer_file=__A		,						do_lower_case=__A		,						unk_token=__A		,						sep_token=__A		,						pad_token=__A		,						cls_token=__A		,						mask_token=__A		,						tokenize_chinese_chars=__A		,						strip_accents=__A		,						**__A		,						)

								_lowerCAmelCase										=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get('lowercase'		,						__A				) != do_lower_case
								    or normalizer_state.get('strip_accents'		,						__A				) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'		,						__A				) != tokenize_chinese_chars
								):
														_lowerCAmelCase										=getattr(__A		,						normalizer_state.pop('type'				)				)
														_lowerCAmelCase										=do_lower_case
														_lowerCAmelCase										=strip_accents
														_lowerCAmelCase										=tokenize_chinese_chars
														_lowerCAmelCase										=normalizer_class(**__A				)

								_lowerCAmelCase										=do_lower_case






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     int:
								_lowerCAmelCase										=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

								return output






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     List[int]:
								_lowerCAmelCase										=[self.sep_token_id]
								_lowerCAmelCase										=[self.cls_token_id]
								if token_ids_a is None:
														return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     Tuple[str]:
								_lowerCAmelCase										=self._tokenizer.model.save(__A		,						name=__A				)
								return tuple(__A				)





