



"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_big_bird import BigBirdTokenizer
else:
      A:     List[str]										=	None

A:     int										=	logging.get_logger(__name__)
A:     str										=	{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A:     Any										=	{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

A:     str										=	{
    "google/bigbird-roberta-base": 4_0_9_6,
    "google/bigbird-roberta-large": 4_0_9_6,
    "google/bigbird-base-trivia-itc": 4_0_9_6,
}


A:     Any										=	"▁"



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Union[str, Any]		=		VOCAB_FILES_NAMES
   __lowerCAmelCase						:     Any		=		PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase						:     Optional[Any]		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase						:     Union[str, Any]		=		BigBirdTokenizer
   __lowerCAmelCase						:     Optional[Any]		=		['input_ids', 'attention_mask']
   __lowerCAmelCase						:     List[int]		=		[]

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="<unk>"   ,  _SCREAMING_SNAKE_CASE="<s>"   ,  _SCREAMING_SNAKE_CASE="</s>"   ,  _SCREAMING_SNAKE_CASE="<pad>"   ,  _SCREAMING_SNAKE_CASE="[SEP]"   ,  _SCREAMING_SNAKE_CASE="[MASK]"   ,  _SCREAMING_SNAKE_CASE="[CLS]"   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else bos_token
    UpperCAmelCase			:      str			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else eos_token
    UpperCAmelCase			:      Tuple			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else unk_token
    UpperCAmelCase			:      Any			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else pad_token
    UpperCAmelCase			:      str			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else cls_token
    UpperCAmelCase			:      Optional[Any]			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else sep_token

    # Mask token behave like a normal word, i.e. include the space before it
    UpperCAmelCase			:      Any			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else mask_token

    super().__init__(
        _SCREAMING_SNAKE_CASE   ,  tokenizer_file=_SCREAMING_SNAKE_CASE   ,  bos_token=_SCREAMING_SNAKE_CASE   ,  eos_token=_SCREAMING_SNAKE_CASE   ,  unk_token=_SCREAMING_SNAKE_CASE   ,  sep_token=_SCREAMING_SNAKE_CASE   ,  pad_token=_SCREAMING_SNAKE_CASE   ,  cls_token=_SCREAMING_SNAKE_CASE   ,  mask_token=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

    UpperCAmelCase			:      str			=			vocab_file
    UpperCAmelCase			:      Optional[Any]			=			False if not self.vocab_file else True

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[int]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			[self.sep_token_id]
    UpperCAmelCase			:      Union[str, Any]			=			[self.cls_token_id]
    if token_ids_a is None:
     return cls + token_ids_a + sep
    return cls + token_ids_a + sep + token_ids_a + sep

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = False		)						->   List[int]:





    '''simple docstring'''




    if already_has_special_tokens:
     if token_ids_a is not None:
      raise ValueError(
          """You should not supply a second sequence if the provided sequence of """
          """ids is already formatted with special tokens for the model."""		)
     return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

    if token_ids_a is None:
     return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
    return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			[self.sep_token_id]
    UpperCAmelCase			:      Optional[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		) * [0] + len(token_ids_a + sep		) * [1]




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   Tuple[str]:





    '''simple docstring'''




    if not self.can_save_slow_tokenizer:
     raise ValueError(
         """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
         """tokenizer."""		)

    if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
     logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
     return
    UpperCAmelCase			:      str			=			os.path.join(
        _SCREAMING_SNAKE_CASE   ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

    if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		):
     copyfile(self.vocab_file   ,  _SCREAMING_SNAKE_CASE		)

    return (out_vocab_file,)





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

import numpy as np

def    _snake_case		(		UpperCamelCase		:	bytes    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      Tuple			=			F"{sampling_rate}"
 UpperCAmelCase			:      Optional[int]			=			"""1"""
 UpperCAmelCase			:      Dict			=			"""f32le"""
 UpperCAmelCase			:      Optional[int]			=			[
     """ffmpeg""",
     """-i""",
     """pipe:0""",
     """-ac""",
     ac,
     """-ar""",
     ar,
     """-f""",
     format_for_conversion,
     """-hide_banner""",
     """-loglevel""",
     """quiet""",
     """pipe:1""",
 ]

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

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	float    ,	UpperCamelCase		:	str = "f32le"    ,	):
 UpperCAmelCase			:      Tuple			=			F"{sampling_rate}"
 UpperCAmelCase			:      List[str]			=			"""1"""
 if format_for_conversion == "s16le":
  UpperCAmelCase			:      int			=			2
 elif format_for_conversion == "f32le":
  UpperCAmelCase			:      int			=			4
 else:
  raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"		)

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

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

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	float    ,	UpperCamelCase		:	Optional[int] = None    ,	UpperCamelCase		:	Optional[Union[Tuple[float, float], float]] = None    ,	UpperCamelCase		:	str = "f32le"    ,	):
 if stream_chunk_s is not None:
  UpperCAmelCase			:      List[Any]			=			stream_chunk_s
 else:
  UpperCAmelCase			:      List[Any]			=			chunk_length_s

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

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

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

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

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      List[Any]			=			2**24  # 16Mo
 try:
  with subprocess.Popen(UpperCamelCase    ,	stdout=subprocess.PIPE    ,	bufsize=UpperCamelCase		) as ffmpeg_process:
   while True:
    UpperCAmelCase			:      Optional[Any]			=			ffmpeg_process.stdout.read(UpperCamelCase		)
    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
