'''simple docstring'''





import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE_:	Optional[Any]    =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	List[Any]    ={'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

SCREAMING_SNAKE_CASE_:	List[Any]    ={
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

SCREAMING_SNAKE_CASE_:	Optional[Any]    ={
    'abeja/gpt-neox-japanese-2.7b': 20_48,
}





def       lowerCAmelCase_							(   snake_case_			:					Any   ,       snake_case_			:					Tuple     )       -> int:


     '''simple docstring'''




     with open(snake_case_   ,       "r"   ,       encoding="utf-8"     ) as f:
          UpperCAmelCase_			       =  json.loads(f.read()     )

     UpperCAmelCase_			       =  collections.OrderedDict()
     UpperCAmelCase_			       =  collections.OrderedDict()
     UpperCAmelCase_			       =  collections.OrderedDict()
     with open(snake_case_   ,       "r"   ,       encoding="utf-8"     ) as f:
          UpperCAmelCase_			       =  f.readlines()
     UpperCAmelCase_			       =  [[t.rstrip("\n"     )] if (t == "," or "," not in t) else t.rstrip("\n"     ).split(","     ) for t in token]
     for idx, b in enumerate(snake_case_     ):
          UpperCAmelCase_			       =  b
          UpperCAmelCase_			       =  idx
          for wd in b:
               UpperCAmelCase_			       =  idx

     return vocab, raw_vocab, ids_to_tokens, emoji




class 			__A (					UpperCamelCase__      ):
    a__			:		int			  =    VOCAB_FILES_NAMES
    a__			:		List[Any]			  =    PRETRAINED_VOCAB_FILES_MAP
    a__			:		Optional[int]			  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__			:		Tuple			  =    ["""input_ids""", """attention_mask"""]



    def __init__(self       :       int  ,     __a       :       Any  ,     __a       :       int  ,     __a       :       Optional[Any]="<|endoftext|>"  ,     __a       :       Optional[Any]="<|endoftext|>"  ,     __a       :       Optional[Any]="<|startoftext|>"  ,     __a       :       int="<|endoftext|>"  ,     __a       :       Tuple=False  ,     **__a       :       Tuple  ,     ):
         super().__init__(
             unk_token=__a  ,     pad_token=__a  ,     bos_token=__a  ,     eos_token=__a  ,     do_clean_text=__a  ,     **__a  ,     )
         if not os.path.isfile(__a					):
              raise ValueError(
                  f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
                  " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"					)
         if not os.path.isfile(__a					):
              raise ValueError(
                  f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
                  " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"					)
         UpperCAmelCase_			       =  do_clean_text
         UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  load_vocab_and_emoji(__a  ,     __a					)
         UpperCAmelCase_			       =  SubWordJapaneseTokenizer(
             vocab=self.vocab  ,     ids_to_tokens=self.ids_to_tokens  ,     emoji=self.emoji					)



    @property
    def    _lowercase		(self       :       int					):
         # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
         return len(self.raw_vocab					)



    def    _lowercase		(self       :       Union[str, Any]					):
         return dict(self.raw_vocab  ,     **self.added_tokens_encoder					)



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       List[str]					):
         return self.subword_tokenizer.tokenize(__a  ,     clean=self.do_clean_text					)



    def    _lowercase		(self       :       Any  ,     __a       :       List[Any]					):
         return self.vocab.get(__a  ,     self.vocab.get(self.unk_token					)					)



    def    _lowercase		(self       :       Tuple  ,     __a       :       Tuple					):
         return self.subword_tokenizer.convert_id_to_token(__a					)



    def    _lowercase		(self       :       Optional[Any]  ,     __a       :       Optional[int]					):
         UpperCAmelCase_			       =  "".join(__a					).strip()
         return out_string



    def    _lowercase		(self       :       Any  ,     __a       :       "Conversation"					):
         UpperCAmelCase_			       =  []
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(__a  ,     add_special_tokens=__a					) + [self.eos_token_id]					)

         if len(__a					) > self.model_max_length:
              UpperCAmelCase_			       =  input_ids[-self.model_max_length :]
         return input_ids






    def    _lowercase		(self       :       Optional[Any]  ,     __a       :       str  ,     __a       :       Optional[str] = None					):
         UpperCAmelCase_			       =  0
         if os.path.isdir(__a					):
              UpperCAmelCase_			       =  os.path.join(
                  __a  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
              UpperCAmelCase_			       =  os.path.join(
                  __a  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]					)
         else:
              UpperCAmelCase_			       =  (
                  (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
              )
              UpperCAmelCase_			       =  (
                  (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
              )
         with open(__a  ,     "w"  ,     encoding="utf-8"					) as writer:
              for token_index, token in self.ids_to_tokens.items():
                   if index != token_index:
                        logger.warning(
                            f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                            " Please check that the vocabulary is not corrupted!"					)
                        UpperCAmelCase_			       =  token_index
                   writer.write(",".join(__a					) + "\n"					)
                   index += 1
         with open(__a  ,     "w"  ,     encoding="utf-8"					) as writer:
              json.dump(self.emoji  ,     __a					)
         return vocab_file, emoji_file




class 			__A (					UpperCamelCase__      ):



    def __init__(self       :       Optional[Any]  ,     __a       :       str  ,     __a       :       Tuple  ,     __a       :       Optional[int]					):
         UpperCAmelCase_			       =  vocab  # same as swe
         UpperCAmelCase_			       =  ids_to_tokens  # same as bpe
         UpperCAmelCase_			       =  emoji
         UpperCAmelCase_			       =  np.max([len(__a					) for w in self.vocab.keys()]					)
         UpperCAmelCase_			       =  re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"					)
         UpperCAmelCase_			       =  re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"					)
         UpperCAmelCase_			       =  re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"					)
         UpperCAmelCase_			       =  re.compile(
             r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"					)
         UpperCAmelCase_			       =  re.compile(
             r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"					)
         UpperCAmelCase_			       =  re.compile(
             r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"					)
         UpperCAmelCase_			       =  "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
         UpperCAmelCase_			       =  "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
         UpperCAmelCase_			       =  str.maketrans({k: "<BLOCK>" for k in keisen + blocks}					)



    def __len__(self       :       Dict					):
         return len(self.ids_to_tokens					)



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       Optional[int]					):
         UpperCAmelCase_			       =  self.content_repattera.sub("<URL>"  ,     __a					)
         UpperCAmelCase_			       =  self.content_repattera.sub("<EMAIL>"  ,     __a					)
         UpperCAmelCase_			       =  self.content_repattera.sub("<TEL>"  ,     __a					)
         UpperCAmelCase_			       =  self.content_repattera.sub("<DATE>"  ,     __a					)
         UpperCAmelCase_			       =  self.content_repattera.sub("<DATE>"  ,     __a					)
         UpperCAmelCase_			       =  self.content_repattera.sub("<PRICE>"  ,     __a					)
         UpperCAmelCase_			       =  content.translate(self.content_transa					)
         while "<BLOCK><BLOCK>" in content:
              UpperCAmelCase_			       =  content.replace("<BLOCK><BLOCK>"  ,     "<BLOCK>"					)
         return content



    def    _lowercase		(self       :       List[str]  ,     __a       :       Union[str, Any]  ,     __a       :       str=False					):
         UpperCAmelCase_			       =  text.replace(" "  ,     "<SP>"					)
         UpperCAmelCase_			       =  text.replace("　"  ,     "<SP>"					)
         UpperCAmelCase_			       =  text.replace("\r\n"  ,     "<BR>"					)
         UpperCAmelCase_			       =  text.replace("\n"  ,     "<BR>"					)
         UpperCAmelCase_			       =  text.replace("\r"  ,     "<BR>"					)
         UpperCAmelCase_			       =  text.replace("\t"  ,     "<TAB>"					)
         UpperCAmelCase_			       =  text.replace("—"  ,     "ー"					)
         UpperCAmelCase_			       =  text.replace("−"  ,     "ー"					)
         for k, v in self.emoji["emoji"].items():
              if k in text:
                   UpperCAmelCase_			       =  text.replace(__a  ,     __a					)
         if clean:
              UpperCAmelCase_			       =  self.clean_text(__a					)

         def check_simbol(__a       :       Union[str, Any]					):
              UpperCAmelCase_			       =  x.encode()
              if len(__a					) == 1 and len(__a					) == 2:
                   UpperCAmelCase_			       =  (int(e[0]					) << 8) + int(e[1]					)
                   if (
                       (c >= 0Xc2_a1 and c <= 0Xc2_bf)
                       or (c >= 0Xc7_80 and c <= 0Xc7_83)
                       or (c >= 0Xca_b9 and c <= 0Xcb_bf)
                       or (c >= 0Xcc_80 and c <= 0Xcd_a2)
                   ):
                        return True
              return False

         def checkuae(__a       :       Dict					):
              UpperCAmelCase_			       =  x.encode()
              if len(__a					) == 1 and len(__a					) == 3:
                   UpperCAmelCase_			       =  (int(e[0]					) << 16) + (int(e[1]					) << 8) + int(e[2]					)
                   if c >= 0Xe2_80_80 and c <= 0Xe2_b0_7f:
                        return True
              return False

         UpperCAmelCase_			       =  0
         UpperCAmelCase_			       =  []
         while pos < len(__a					):
              UpperCAmelCase_			       =  min(len(__a					)  ,     pos + self.maxlen + 1					) if text[pos] == "<" else pos + 3
              UpperCAmelCase_			       =  []  # (token_id, token, pos)
              for e in range(__a  ,     __a  ,     -1					):
                   UpperCAmelCase_			       =  text[pos:e]
                   if wd in self.vocab:
                        if wd[0] == "<" and len(__a					) > 2:
                             UpperCAmelCase_			       =  [(self.vocab[wd], wd, e)]
                             break
                        else:
                             candidates.append((self.vocab[wd], wd, e)					)
              if len(__a					) > 0:
                   # the smallest token_id is adopted
                   UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  sorted(__a  ,     key=lambda __a					: x[0]					)[0]
                   result.append(__a					)
                   UpperCAmelCase_			       =  e
              else:
                   UpperCAmelCase_			       =  pos + 1
                   UpperCAmelCase_			       =  text[pos:end]
                   if check_simbol(__a					):
                        result.append("<KIGOU>"					)
                   elif checkuae(__a					):
                        result.append("<U2000U2BFF>"					)
                   else:
                        for i in wd.encode("utf-8"					):
                             result.append("<|byte%d|>" % i					)
                   UpperCAmelCase_			       =  end
         return result






    def    _lowercase		(self       :       Dict  ,     __a       :       Tuple  ,     __a       :       Optional[Any]="\n"					):
         UpperCAmelCase_			       =  []
         UpperCAmelCase_			       =  []
         UpperCAmelCase_			       =  self.ids_to_tokens[index][0]
         if word[:6] == "<|byte" and word[-2:] == "|>":
              byte_tokens.append(int(word[6:-2]					)					)
         else:
              if len(__a					) > 0:
                   words.append(bytearray(__a					).decode("utf-8"  ,     errors="replace"					)					)
                   UpperCAmelCase_			       =  []
              if word[:7] == "<|emoji" and word[-2:] == "|>":
                   words.append(self.emoji["emoji_inv"][word]					)
              elif word == "<SP>":
                   words.append(" "					)
              elif word == "<BR>":
                   words.append(__a					)
              elif word == "<TAB>":
                   words.append("\t"					)
              elif word == "<BLOCK>":
                   words.append("▀"					)
              elif word == "<KIGOU>":
                   words.append("ǀ"					)
              elif word == "<U2000U2BFF>":
                   words.append("‖"					)
              else:
                   words.append(__a					)
         if len(__a					) > 0:
              words.append(bytearray(__a					).decode("utf-8"  ,     errors="replace"					)					)
         UpperCAmelCase_			       =  "".join(__a					)
         return text












"""simple docstring"""





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

import numpy as np



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



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    str = "f32le"      ,   )    ->							Optional[int]:
						__magic_name__:	List[Any]							=       f'{sampling_rate}'
						__magic_name__:	Optional[int]							=       """1"""
						if format_for_conversion == "s16le":
												__magic_name__:	Dict							=       2
						elif format_for_conversion == "f32le":
												__magic_name__:	List[str]							=       4
						else:
												raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'  )

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

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



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    Optional[int] = None      ,   __UpperCAmelCase      :    Optional[Union[Tuple[float, float], float]] = None      ,   __UpperCAmelCase      :    str = "f32le"      ,   )    ->							List[Any]:
						if stream_chunk_s is not None:
												__magic_name__:	Optional[int]							=       stream_chunk_s
						else:
												__magic_name__:	List[str]							=       chunk_length_s

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

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

						__magic_name__:	List[str]							=       int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
						__magic_name__:	List[Any]							=       int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
						__magic_name__:	List[str]							=       datetime.datetime.now()
						__magic_name__:	Optional[Any]							=       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
												__magic_name__:	int							=       np.frombuffer(item["""raw"""]      ,   dtype=__UpperCAmelCase  )
												__magic_name__:	int							=       (
												    item["""stride"""][0] // size_of_sample,
												    item["""stride"""][1] // size_of_sample,
												)
												__magic_name__:	Union[str, Any]							=       sampling_rate
												audio_time += delta
												if datetime.datetime.now() > audio_time + 1_0 * delta:
																		# We're late !! SKIP
																		continue
												yield item



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



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    int  )    ->							List[Any]:
						__magic_name__:	str							=       2**2_4  # 16Mo
						try:
												with subprocess.Popen(__UpperCAmelCase      ,   stdout=subprocess.PIPE      ,   bufsize=__UpperCAmelCase  ) as ffmpeg_process:
																		while True:
																								__magic_name__:	Dict							=       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



