


'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np


def      a (     __a					,   __a  )    ->      np.array:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					f'''{sampling_rate}'''
					UpperCamelCase__  :List[str]				   =					'''1'''
					UpperCamelCase__  :Optional[Any]				   =					'''f32le'''
					UpperCamelCase__  :Optional[Any]				   =					[
					    '''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:
															UpperCamelCase__  :List[str]				   =					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
					UpperCamelCase__  :int				   =					output_stream[0]
					UpperCamelCase__  :List[Any]				   =					np.frombuffer(__a					,   np.floataa  )
					if audio.shape[0] == 0:
										raise ValueError('''Malformed soundfile'''  )
					return audio


def      a (     __a					,   __a					,   __a = "f32le"					,   )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					f'''{sampling_rate}'''
					UpperCamelCase__  :int				   =					'''1'''
					if format_for_conversion == "s16le":
										UpperCamelCase__  :Union[str, Any]				   =					2
					elif format_for_conversion == "f32le":
										UpperCamelCase__  :Dict				   =					4
					else:
										raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

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

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


def      a (     __a					,   __a					,   __a = None					,   __a = None					,   __a = "f32le"					,   )    ->      str:



					'''simple docstring'''






					if stream_chunk_s is not None:
										UpperCamelCase__  :int				   =					stream_chunk_s
					else:
										UpperCamelCase__  :str				   =					chunk_length_s

					UpperCamelCase__  :Union[str, Any]				   =					ffmpeg_microphone(__a					,   __a					,   format_for_conversion=__a  )
					if format_for_conversion == "s16le":
										UpperCamelCase__  :List[str]				   =					np.intaa
										UpperCamelCase__  :Dict				   =					2
					elif format_for_conversion == "f32le":
										UpperCamelCase__  :Union[str, Any]				   =					np.floataa
										UpperCamelCase__  :List[Any]				   =					4
					else:
										raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

					if stride_length_s is None:
										UpperCamelCase__  :Optional[Any]				   =					chunk_length_s / 6
					UpperCamelCase__  :Tuple				   =					int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
					if isinstance(__a					,   (int, float)  ):
										UpperCamelCase__  :Union[str, Any]				   =					[stride_length_s, stride_length_s]

					UpperCamelCase__  :Optional[Any]				   =					int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
					UpperCamelCase__  :Tuple				   =					int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
					UpperCamelCase__  :Any				   =					datetime.datetime.now()
					UpperCamelCase__  :Any				   =					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
										UpperCamelCase__  :List[str]				   =					np.frombuffer(item['''raw''']					,   dtype=__a  )
										UpperCamelCase__  :Optional[int]				   =					(
										    item['''stride'''][0] // size_of_sample,
										    item['''stride'''][1] // size_of_sample,
										)
										UpperCamelCase__  :Tuple				   =					sampling_rate
										audio_time += delta
										if datetime.datetime.now() > audio_time + 10 * delta:
															# We're late !! SKIP
															continue
										yield item


def      a (     __a					,   __a					,   __a					,   __a = False  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					B''''''
					UpperCamelCase__   ,							UpperCamelCase__  :List[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}'''  )
					UpperCamelCase__  :str				   =					0
					for raw in iterator:
										acc += raw
										if stream and len(__a  ) < chunk_len:
															UpperCamelCase__  :Union[str, Any]				   =					(_stride_left, 0)
															yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
										else:
															while len(__a  ) >= chunk_len:
																				# We are flushing the accumulator
																				UpperCamelCase__  :Tuple				   =					(_stride_left, stride_right)
																				UpperCamelCase__  :Union[str, Any]				   =					{'''raw''': acc[:chunk_len], '''stride''': stride}
																				if stream:
																									UpperCamelCase__  :Dict				   =					False
																				yield item
																				UpperCamelCase__  :Optional[int]				   =					stride_left
																				UpperCamelCase__  :str				   =					acc[chunk_len - stride_left - stride_right :]
    # Last chunk
					if len(__a  ) > stride_left:
										UpperCamelCase__  :Tuple				   =					{'''raw''': acc, '''stride''': (_stride_left, 0)}
										if stream:
															UpperCamelCase__  :List[Any]				   =					False
										yield item







def      a (     __a					,   __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :Tuple				   =					2**24  # 16Mo
					try:
										with subprocess.Popen(__a					,   stdout=subprocess.PIPE					,   bufsize=__a  ) as ffmpeg_process:
															while True:
																				UpperCamelCase__  :Optional[Any]				   =					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 .tokenization_electra import ElectraTokenizer


__snake_case				 =	{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case				 =	{
    '''vocab_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-generator''': (
            '''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case				 =	{
    '''google/electra-small-generator''': 512,
    '''google/electra-base-generator''': 512,
    '''google/electra-large-generator''': 512,
    '''google/electra-small-discriminator''': 512,
    '''google/electra-base-discriminator''': 512,
    '''google/electra-large-discriminator''': 512,
}

__snake_case				 =	{
    '''google/electra-small-generator''': {'''do_lower_case''': True},
    '''google/electra-base-generator''': {'''do_lower_case''': True},
    '''google/electra-large-generator''': {'''do_lower_case''': True},
    '''google/electra-small-discriminator''': {'''do_lower_case''': True},
    '''google/electra-base-discriminator''': {'''do_lower_case''': True},
    '''google/electra-large-discriminator''': {'''do_lower_case''': True},
}
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							VOCAB_FILES_NAMES
		_a       					=							PRETRAINED_VOCAB_FILES_MAP
		_a       					=							PRETRAINED_INIT_CONFIGURATION
		_a       					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a       					=							ElectraTokenizer




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=True       ,  UpperCamelCase_="[UNK]"       ,  UpperCamelCase_="[SEP]"       ,  UpperCamelCase_="[PAD]"       ,  UpperCamelCase_="[CLS]"       ,  UpperCamelCase_="[MASK]"       ,  UpperCamelCase_=True       ,  UpperCamelCase_=None       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(
							    UpperCamelCase_       ,  tokenizer_file=UpperCamelCase_       ,  do_lower_case=UpperCamelCase_       ,  unk_token=UpperCamelCase_       ,  sep_token=UpperCamelCase_       ,  pad_token=UpperCamelCase_       ,  cls_token=UpperCamelCase_       ,  mask_token=UpperCamelCase_       ,  tokenize_chinese_chars=UpperCamelCase_       ,  strip_accents=UpperCamelCase_       ,  **UpperCamelCase_       ,  )

							UpperCamelCase__  :str				   =					json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
							if (
							    normalizer_state.get('''lowercase'''       ,  UpperCamelCase_				) != do_lower_case
							    or normalizer_state.get('''strip_accents'''       ,  UpperCamelCase_				) != strip_accents
							    or normalizer_state.get('''handle_chinese_chars'''       ,  UpperCamelCase_				) != tokenize_chinese_chars
							):
												UpperCamelCase__  :Any				   =					getattr(UpperCamelCase_       ,  normalizer_state.pop('''type'''				)				)
												UpperCamelCase__  :Any				   =					do_lower_case
												UpperCamelCase__  :Dict				   =					strip_accents
												UpperCamelCase__  :Optional[int]				   =					tokenize_chinese_chars
												UpperCamelCase__  :Dict				   =					normalizer_class(**UpperCamelCase_				)

							UpperCamelCase__  :str				   =					do_lower_case




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=None				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					[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        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					[self.sep_token_id]
							UpperCamelCase__  :str				   =					[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        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					self._tokenizer.model.save(UpperCamelCase_       ,  name=UpperCamelCase_				)
							return tuple(UpperCamelCase_				)