

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


a__												=						logging.get_logger(__name__)

a__												=						{'''vocab_file''': '''spiece.model'''}

a__												=						{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=False			,				_a=True			,				_a=False			,				_a="<s>"			,				_a="</s>"			,				_a="<unk>"			,				_a="<sep>"			,				_a="<pad>"			,				_a="<cls>"			,				_a="<mask>"			,				_a=["<eop>", "<eod>"]			,				_a = None			,				**_a			,				)       ->       None:
												_a      :       Optional[Any]   =						AddedToken(_a			,				lstrip=_a			,				rstrip=_a			) if isinstance(_a			,				_a			) else mask_token

												_a      :       Optional[int]   =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    do_lower_case=_a			,				remove_space=_a			,				keep_accents=_a			,				bos_token=_a			,				eos_token=_a			,				unk_token=_a			,				sep_token=_a			,				pad_token=_a			,				cls_token=_a			,				mask_token=_a			,				additional_special_tokens=_a			,				sp_model_kwargs=self.sp_model_kwargs			,				**_a			,				)

												_a      :       List[str]   =						3

												_a      :       str   =						do_lower_case
												_a      :       Optional[int]   =						remove_space
												_a      :       Optional[Any]   =						keep_accents
												_a      :       Optional[Any]   =						vocab_file

												_a      :       Optional[Any]   =						spm.SentencePieceProcessor(**self.sp_model_kwargs			)
												self.sp_model.Load(_a			)

												try:
																	import jieba
												except ModuleNotFoundError as error:
																	raise error.__class__(
																	    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
																	    '''See https://pypi.org/project/jieba/ for installation.'''			)
												_a      :       Optional[Any]   =						jieba
												_a      :       Optional[int]   =						str.maketrans(''' \n'''			,				'''\u2582\u2583'''			)


							@property
							# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
							def 							__lowercase							(     self			)       ->       int:
												return len(self.sp_model			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       List[str]   =						{self.convert_ids_to_tokens(_a			): i for i in range(self.vocab_size			)}
												vocab.update(self.added_tokens_encoder			)
												return vocab


							def __getstate__(     self			)       ->       Optional[int]:
												_a      :       Any   =						self.__dict__.copy()
												_a      :       Optional[Any]   =						None
												return state


							def __setstate__(     self			,				_a			)       ->       Union[str, Any]:
												_a      :       Union[str, Any]   =						d

												# for backward compatibility
												if not hasattr(self			,				'''sp_model_kwargs'''			):
																	_a      :       Dict   =						{}

												_a      :       List[str]   =						spm.SentencePieceProcessor(**self.sp_model_kwargs			)
												self.sp_model.Load(self.vocab_file			)


							def 							__lowercase							(     self			,				_a			)       ->       Any:
												if self.remove_space:
																	_a      :       Optional[Any]   =						''' '''.join(inputs.strip().split()			)
												else:
																	_a      :       Optional[int]   =						inputs
												_a      :       Optional[int]   =						outputs.replace('''``'''			,				'''"'''			).replace('''\'\''''			,				'''"'''			)

												if not self.keep_accents:
																	_a      :       Union[str, Any]   =						unicodedata.normalize('''NFKD'''			,				_a			)
																	_a      :       List[Any]   =						''''''.join([c for c in outputs if not unicodedata.combining(_a			)]			)
												if self.do_lower_case:
																	_a      :       Tuple   =						outputs.lower()

												return outputs


							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												_a      :       Optional[Any]   =						self.preprocess_text(_a			)
												_a      :       Optional[int]   =						self.sp_model.encode(_a			,				out_type=_a			)
												_a      :       str   =						[]
												for piece in pieces:
																	if len(_a			) > 1 and piece[-1] == str(''','''			) and piece[-2].isdigit():
																						_a      :       Tuple   =						self.sp_model.EncodeAsPieces(piece[:-1].replace(_a			,				''''''			)			)
																						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																											if len(cur_pieces[0]			) == 1:
																																_a      :       List[Any]   =						cur_pieces[1:]
																											else:
																																_a      :       Dict   =						cur_pieces[0][1:]
																						cur_pieces.append(piece[-1]			)
																						new_pieces.extend(_a			)
																	else:
																						new_pieces.append(_a			)

												return new_pieces


							def 							__lowercase							(     self			,				_a			)       ->       Dict:
												return self.sp_model.PieceToId(_a			)


							def 							__lowercase							(     self			,				_a			)       ->       str:
												return self.sp_model.IdToPiece(_a			)


							def 							__lowercase							(     self			,				_a			)       ->       List[Any]:
												_a      :       Union[str, Any]   =						''''''.join(_a			).replace(_a			,				''' '''			).strip()
												return out_string


							def 							__lowercase							(     self			,				_a			,				_a = None			)       ->       List[int]:
												_a      :       str   =						[self.sep_token_id]
												_a      :       Any   =						[self.cls_token_id]
												if token_ids_a is None:
																	return token_ids_a + sep + cls
												return token_ids_a + sep + token_ids_a + sep + cls


							def 							__lowercase							(     self			,				_a			,				_a = None			,				_a = False			)       ->       List[int]:

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_a			,				token_ids_a=_a			,				already_has_special_tokens=_a			)

												if token_ids_a is not None:
																	return ([0] * len(_a			)) + [1] + ([0] * len(_a			)) + [1, 1]
												return ([0] * len(_a			)) + [1, 1]


							def 							__lowercase							(     self			,				_a			,				_a = None			)       ->       List[int]:
												_a      :       Tuple   =						[self.sep_token_id]
												_a      :       Tuple   =						[2]

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


							def 							__lowercase							(     self			,				_a			,				_a = None			)       ->       Tuple[str]:
												if not os.path.isdir(_a			):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
																	return
												_a      :       int   =						os.path.join(
												    _a			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

												if os.path.abspath(self.vocab_file			) != os.path.abspath(_a			) and os.path.isfile(self.vocab_file			):
																	copyfile(self.vocab_file			,				_a			)
												elif not os.path.isfile(self.vocab_file			):
																	with open(_a			,				'''wb'''			) as fi:
																						_a      :       Dict   =						self.sp_model.serialized_model_proto()
																						fi.write(_a			)

												return (out_vocab_file,)




							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       Tuple:
												_a      :       Any   =						super()._decode(*_a			,				**_a			)
												_a      :       Union[str, Any]   =						text.replace(''' '''			,				''''''			).replace('''\u2582'''			,				''' '''			).replace('''\u2583'''			,				'''\n'''			)
												return text






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

import numpy as np



def   __UpperCAmelCase					(      __a :     bytes						,__a :     int      )     ->	np.array:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     str = "f32le"						,)     ->	str:

					"""simple docstring"""

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

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

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



def   __UpperCAmelCase					(      __a :     int						,__a :     float						,__a :     Optional[int] = None						,__a :     Optional[Union[Tuple[float, float], float]] = None						,__a :     str = "f32le"						,)     ->	Optional[int]:

					"""simple docstring"""

					if stream_chunk_s is not None:
										_a      :       Tuple   =						stream_chunk_s
					else:
										_a      :       Tuple   =						chunk_length_s

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

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

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



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     int						,__a :     Tuple[int, int]						,__a :     bool = False      )     ->	Optional[int]:

					"""simple docstring"""

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







def   __UpperCAmelCase					(      __a :     int						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Dict   =						2**24  # 16Mo
					try:
										with subprocess.Popen(__a						,stdout=subprocess.PIPE						,bufsize=__a      ) as ffmpeg_process:
															while True:
																				_a      :       int   =						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



