





"""simple docstring"""






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

import numpy as np

def      __A   (      a_    :bytes ,							a_    :int)		->		np.array:
				__a			:    str      							=	F"""{sampling_rate}"""
				__a			:    Tuple      							=	'''1'''
				__a			:    Optional[int]      							=	'''f32le'''
				__a			:    Union[str, 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:
												__a			:    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
				__a			:    List[Any]      							=	output_stream[0]
				__a			:    Union[str, Any]      							=	np.frombuffer(a_ ,							np.floataa)
				if audio.shape[0] == 0:
								raise ValueError('''Malformed soundfile''')
				return audio

def      __A   (      a_    :int ,							a_    :float ,							a_    :str = "f32le" ,							)		->		Tuple:
				__a			:    int      							=	F"""{sampling_rate}"""
				__a			:    List[Any]      							=	'''1'''
				if format_for_conversion == "s16le":
								__a			:    Dict      							=	2
				elif format_for_conversion == "f32le":
								__a			:    List[Any]      							=	4
				else:
								raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""")

				__a			:    List[Any]      							=	platform.system()
				if system == "Linux":
								__a			:    List[Any]      							=	'''alsa'''
								__a			:    str      							=	'''default'''
				elif system == "Darwin":
								__a			:    List[str]      							=	'''avfoundation'''
								__a			:    List[str]      							=	''':0'''
				elif system == "Windows":
								__a			:    Any      							=	'''dshow'''
								__a			:    List[str]      							=	'''default'''

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

def      __A   (      a_    :int ,							a_    :float ,							a_    :Optional[int] = None ,							a_    :Optional[Union[Tuple[float, float], float]] = None ,							a_    :str = "f32le" ,							)		->		Optional[Any]:
				if stream_chunk_s is not None:
								__a			:    Optional[int]      							=	stream_chunk_s
				else:
								__a			:    Optional[Any]      							=	chunk_length_s

				__a			:    Union[str, Any]      							=	ffmpeg_microphone(a_ ,							a_ ,							format_for_conversion=a_)
				if format_for_conversion == "s16le":
								__a			:    Tuple      							=	np.intaa
								__a			:    Optional[int]      							=	2
				elif format_for_conversion == "f32le":
								__a			:    str      							=	np.floataa
								__a			:    int      							=	4
				else:
								raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""")

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

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

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






def      __A   (      a_    :List[str] ,							a_    :int)		->		List[str]:
				__a			:    List[Any]      							=	2**24  # 16Mo
				try:
								with subprocess.Popen(a_ ,							stdout=subprocess.PIPE ,							bufsize=a_) as ffmpeg_process:
												while True:
																__a			:    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 pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


A_			      =		logging.get_logger(__name__)


A_			      =		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all MVP models at https://huggingface.co/models?filter=mvp
A_			      =		{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

A_			      =		{
    "RUCAIBox/mvp": 1024,
}






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			VOCAB_FILES_NAMES
     A_											=			PRETRAINED_VOCAB_FILES_MAP
     A_											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_											=			["input_ids", "attention_mask"]
     A_											=			MvpTokenizer





     def __init__(     self     :					Optional[int]     ,							__lowerCamelCase     :					Tuple=None     ,							__lowerCamelCase     :					Tuple=None     ,							__lowerCamelCase     :					List[str]=None     ,							__lowerCamelCase     :					Optional[int]="replace"     ,							__lowerCamelCase     :					int="<s>"     ,							__lowerCamelCase     :					Dict="</s>"     ,							__lowerCamelCase     :					int="</s>"     ,							__lowerCamelCase     :					int="<s>"     ,							__lowerCamelCase     :					int="<unk>"     ,							__lowerCamelCase     :					List[str]="<pad>"     ,							__lowerCamelCase     :					int="<mask>"     ,							__lowerCamelCase     :					int=False     ,							__lowerCamelCase     :					List[str]=True     ,							**__lowerCamelCase     :					Dict     ,							):
           super().__init__(
               __lowerCamelCase     ,							__lowerCamelCase     ,							tokenizer_file=__lowerCamelCase     ,							errors=__lowerCamelCase     ,							bos_token=__lowerCamelCase     ,							eos_token=__lowerCamelCase     ,							sep_token=__lowerCamelCase     ,							cls_token=__lowerCamelCase     ,							unk_token=__lowerCamelCase     ,							pad_token=__lowerCamelCase     ,							mask_token=__lowerCamelCase     ,							add_prefix_space=__lowerCamelCase     ,							trim_offsets=__lowerCamelCase     ,							**__lowerCamelCase     ,							)

           snake_case__       :   Any	      =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
           if pre_tok_state.get('add_prefix_space'     ,							__lowerCamelCase			) != add_prefix_space:
                 snake_case__       :   str	      =    getattr(__lowerCamelCase     ,							pre_tok_state.pop('type'			)			)
                 snake_case__       :   int	      =    add_prefix_space
                 snake_case__       :   List[str]	      =    pre_tok_class(**__lowerCamelCase			)

           snake_case__       :   Tuple	      =    add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           snake_case__       :   Dict	      =    'post_processor'
           snake_case__       :   Optional[int]	      =    getattr(self.backend_tokenizer     ,							__lowerCamelCase     ,							__lowerCamelCase			)
           if tokenizer_component_instance:
                 snake_case__       :   List[Any]	      =    json.loads(tokenizer_component_instance.__getstate__()			)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                       snake_case__       :   int	      =    tuple(state['sep']			)
                 if "cls" in state:
                       snake_case__       :   Union[str, Any]	      =    tuple(state['cls']			)

                 snake_case__       :   Dict	      =    False

                 if state.get('add_prefix_space'     ,							__lowerCamelCase			) != add_prefix_space:
                       snake_case__       :   List[Any]	      =    add_prefix_space
                       snake_case__       :   List[str]	      =    True

                 if state.get('trim_offsets'     ,							__lowerCamelCase			) != trim_offsets:
                       snake_case__       :   str	      =    trim_offsets
                       snake_case__       :   List[Any]	      =    True

                 if changes_to_apply:
                       snake_case__       :   Optional[int]	      =    getattr(__lowerCamelCase     ,							state.pop('type'			)			)
                       snake_case__       :   str	      =    component_class(**__lowerCamelCase			)
                       setattr(self.backend_tokenizer     ,							__lowerCamelCase     ,							__lowerCamelCase			)





     @property
     def 			_lowerCAmelCase      (     self     :					str			):
           if self._mask_token is None:
                 if self.verbose:
                       logger.error('Using mask_token, but it is not set yet.'			)
                 return None
           return str(self._mask_token			)





     @mask_token.setter
     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					Optional[Any]			):
           snake_case__       :   str	      =    AddedToken(__lowerCamelCase     ,							lstrip=__lowerCamelCase     ,							rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     ,							__lowerCamelCase			) else value
           snake_case__       :   Optional[int]	      =    value





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							*__lowerCamelCase     :					List[str]     ,							**__lowerCamelCase     :					List[Any]			):
           snake_case__       :   Optional[int]	      =    kwargs.get('is_split_into_words'     ,							__lowerCamelCase			)

           if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                     'to use it with pretokenized inputs.'			)

           return super()._batch_encode_plus(*__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							*__lowerCamelCase     :					Optional[Any]     ,							**__lowerCamelCase     :					Dict			):
           snake_case__       :   Tuple	      =    kwargs.get('is_split_into_words'     ,							__lowerCamelCase			)

           if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                     'to use it with pretokenized inputs.'			)

           return super()._encode_plus(*__lowerCamelCase     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[str] = None			):
           snake_case__       :   List[str]	      =    self._tokenizer.model.save(__lowerCamelCase     ,							name=__lowerCamelCase			)
           return tuple(__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[int]=None			):
           snake_case__       :   Optional[Any]	      =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                 return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					List[int]     ,							__lowerCamelCase     :					Optional[List[int]] = None			):
           snake_case__       :   Dict	      =    [self.sep_token_id]
           snake_case__       :   List[Any]	      =    [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 + sep + token_ids_a + sep			) * [0]
