



import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
			import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{"vocab_file": "vocab.txt"}

SCREAMING_SNAKE_CASE     				=						{
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

SCREAMING_SNAKE_CASE     				=						{
    "openbmb/cpm-ant-10b": 1024,
}







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Dict:
		UpperCAmelCase_						=				collections.OrderedDict()
		with open(__SCREAMING_SNAKE_CASE			,     "r"			,     encoding="utf-8" ) as reader:
				UpperCAmelCase_						=				reader.readlines()
		for index, token in enumerate(__SCREAMING_SNAKE_CASE ):
				UpperCAmelCase_						=				token.rstrip("\n" )
				UpperCAmelCase_						=				index
		return vocab



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase="<unk>"    ,	lowerCAmelCase=200     ):
							UpperCAmelCase_						=				vocab
							UpperCAmelCase_						=				unk_token
							UpperCAmelCase_						=				max_input_chars_per_word



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				list(lowerCAmelCase     )
							if len(lowerCAmelCase     ) > self.max_input_chars_per_word:
									return [self.unk_token]

							UpperCAmelCase_						=				0
							UpperCAmelCase_						=				[]
							while start < len(lowerCAmelCase     ):
									UpperCAmelCase_						=				len(lowerCAmelCase     )
									UpperCAmelCase_						=				None
									while start < end:
											UpperCAmelCase_						=				"".join(chars[start:end]     )
											if substr in self.vocab:
													UpperCAmelCase_						=				substr
													break
											end -= 1
									if cur_substr is None:
											sub_tokens.append(self.unk_token     )
											start += 1
									else:
											sub_tokens.append(lowerCAmelCase     )
											UpperCAmelCase_						=				end

							return sub_tokens





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Union[str, Any]									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    Tuple									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    Union[str, Any]									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    str									=  ['input_ids', 'attention_mask']
					lowerCAmelCase_				:    Optional[int]									=  False



					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase="<d>"    ,	lowerCAmelCase="</d>"    ,	lowerCAmelCase="<s>"    ,	lowerCAmelCase="</s>"    ,	lowerCAmelCase="<pad>"    ,	lowerCAmelCase="<unk>"    ,	lowerCAmelCase="</n>"    ,	lowerCAmelCase="</_>"    ,	lowerCAmelCase="left"    ,	**lowerCAmelCase    ,	):
							requires_backends(self    ,	["jieba"]     )
							super().__init__(
							    bod_token=lowerCAmelCase    ,	eod_token=lowerCAmelCase    ,	bos_token=lowerCAmelCase    ,	eos_token=lowerCAmelCase    ,	pad_token=lowerCAmelCase    ,	unk_token=lowerCAmelCase    ,	line_token=lowerCAmelCase    ,	space_token=lowerCAmelCase    ,	padding_side=lowerCAmelCase    ,	**lowerCAmelCase    ,	)
							UpperCAmelCase_						=				bod_token
							UpperCAmelCase_						=				eod_token
							UpperCAmelCase_						=				load_vocab(lowerCAmelCase     )
							UpperCAmelCase_						=				self.encoder[space_token]
							UpperCAmelCase_						=				self.encoder[line_token]

							del self.encoder[space_token]
							del self.encoder[line_token]

							UpperCAmelCase_						=				collections.OrderedDict(sorted(self.encoder.items()    ,	key=lambda lowerCAmelCase     : x[1]     )     )
							UpperCAmelCase_						=				{v: k for k, v in self.encoder.items()}

							UpperCAmelCase_						=				WordpieceTokenizer(vocab=self.encoder    ,	unk_token=self.unk_token     )



					@property
					def    A__		(			self     ):
							return self.encoder[self.bod_token]



					@property
					def    A__		(			self     ):
							return self.encoder[self.eod_token]



					@property
					def    A__		(			self     ):
							return self.encoder["\n"]



					@property
					def    A__		(			self     ):
							return len(self.encoder     )



					def    A__		(			self     ):
							return dict(self.encoder    ,	**self.added_tokens_encoder     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				[]
							for x in jieba.cut(lowerCAmelCase    ,	cut_all=lowerCAmelCase     ):
									output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCAmelCase     )     )
							return output_tokens



					def    A__		(			self    ,	lowerCAmelCase    ,	**lowerCAmelCase     ):
							UpperCAmelCase_						=				[i for i in token_ids if i >= 0]
							UpperCAmelCase_						=				[
							    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
							]
							return super()._decode(lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							return token in self.encoder



					def    A__		(			self    ,	lowerCAmelCase     ):
							return "".join(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							return self.encoder.get(lowerCAmelCase    ,	self.encoder.get(self.unk_token     )     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							return self.decoder.get(lowerCAmelCase    ,	self.unk_token     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							if os.path.isdir(lowerCAmelCase     ):
									UpperCAmelCase_						=				os.path.join(
									    lowerCAmelCase    ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
							else:
									UpperCAmelCase_						=				(filename_prefix + "-" if filename_prefix else "") + save_directory
							UpperCAmelCase_						=				0
							if " " in self.encoder:
									UpperCAmelCase_						=				self.encoder[" "]
									del self.encoder[" "]
							if "\n" in self.encoder:
									UpperCAmelCase_						=				self.encoder["\n"]
									del self.encoder["\n"]
							UpperCAmelCase_						=				collections.OrderedDict(sorted(self.encoder.items()    ,	key=lambda lowerCAmelCase     : x[1]     )     )
							with open(lowerCAmelCase    ,	"w"    ,	encoding="utf-8"     ) as writer:
									for token, token_index in self.encoder.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(token + "\n"     )
											index += 1
							return (vocab_file,)



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							if token_ids_a is None:
									return [self.bos_token_id] + token_ids_a
							return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = False     ):

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

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






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

import numpy as np







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							np.array:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		UpperCAmelCase_						=				"f32le"
		UpperCAmelCase_						=				[
		    "ffmpeg",
		    "-i",
		    "pipe:0",
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]

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







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							Dict:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		UpperCAmelCase_						=				platform.system()
		if system == "Linux":
				UpperCAmelCase_						=				"alsa"
				UpperCAmelCase_						=				"default"
		elif system == "Darwin":
				UpperCAmelCase_						=				"avfoundation"
				UpperCAmelCase_						=				":0"
		elif system == "Windows":
				UpperCAmelCase_						=				"dshow"
				UpperCAmelCase_						=				"default"

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







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							int:
		if stream_chunk_s is not None:
				UpperCAmelCase_						=				stream_chunk_s
		else:
				UpperCAmelCase_						=				chunk_length_s

		UpperCAmelCase_						=				ffmpeg_microphone(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     format_for_conversion=__SCREAMING_SNAKE_CASE )
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				np.intaa
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				np.floataa
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		if stride_length_s is None:
				UpperCAmelCase_						=				chunk_length_s / 6
		UpperCAmelCase_						=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
		if isinstance(__SCREAMING_SNAKE_CASE			,     (int, float) ):
				UpperCAmelCase_						=				[stride_length_s, stride_length_s]

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







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = False )		->							Dict:
		UpperCAmelCase_						=				B""
		UpperCAmelCase_					,						UpperCAmelCase_						=				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_						=				0
		for raw in iterator:
				acc += raw
				if stream and len(__SCREAMING_SNAKE_CASE ) < chunk_len:
						UpperCAmelCase_						=				(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(__SCREAMING_SNAKE_CASE ) >= chunk_len:
								# We are flushing the accumulator
								UpperCAmelCase_						=				(_stride_left, stride_right)
								UpperCAmelCase_						=				{"raw": acc[:chunk_len], "stride": stride}
								if stream:
										UpperCAmelCase_						=				False
								yield item
								UpperCAmelCase_						=				stride_left
								UpperCAmelCase_						=				acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(__SCREAMING_SNAKE_CASE ) > stride_left:
				UpperCAmelCase_						=				{"raw": acc, "stride": (_stride_left, 0)}
				if stream:
						UpperCAmelCase_						=				False
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		UpperCAmelCase_						=				2**24  # 16Mo
		try:
				with subprocess.Popen(__SCREAMING_SNAKE_CASE			,     stdout=subprocess.PIPE			,     bufsize=__SCREAMING_SNAKE_CASE ) as ffmpeg_process:
						while True:
								UpperCAmelCase_						=				ffmpeg_process.stdout.read(__SCREAMING_SNAKE_CASE )
								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

