






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class 							__a							(  __A						):





	'''simple docstring'''



	def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False ,    UpperCamelCase__ = False ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
					super().__init__(
					    UpperCamelCase__ ,    split=UpperCamelCase__ ,    features=UpperCamelCase__ ,    cache_dir=UpperCamelCase__ ,    keep_in_memory=UpperCamelCase__ ,    streaming=UpperCamelCase__ ,    num_proc=UpperCamelCase__ ,    **UpperCamelCase__ ,    )
					SCREAMING_SNAKE_CASE_   :	Any													=   field
					SCREAMING_SNAKE_CASE_   :	str													=   path_or_paths if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ) else {self.split: path_or_paths}
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   Json(
					    cache_dir=UpperCamelCase__ ,    data_files=UpperCamelCase__ ,    features=UpperCamelCase__ ,    field=UpperCamelCase__ ,    **UpperCamelCase__ ,    )






	def   __snake_case			(						self       ):
					# Build iterable dataset
					if self.streaming:
									SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.builder.as_streaming_dataset(split=self.split       )
					# Build regular (map-style) dataset
					else:
									SCREAMING_SNAKE_CASE_   :	List[Any]													=   None
									SCREAMING_SNAKE_CASE_   :	List[str]													=   None
									SCREAMING_SNAKE_CASE_   :	int													=   None
									SCREAMING_SNAKE_CASE_   :	Tuple													=   None

									self.builder.download_and_prepare(
									    download_config=UpperCamelCase__ ,    download_mode=UpperCamelCase__ ,    verification_mode=UpperCamelCase__ ,    base_path=UpperCamelCase__ ,    num_proc=self.num_proc ,    )
									SCREAMING_SNAKE_CASE_   :	List[str]													=   self.builder.as_dataset(
									    split=self.split ,    verification_mode=UpperCamelCase__ ,    in_memory=self.keep_in_memory       )
					return dataset






class 							__a							:





	'''simple docstring'''



	def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
					if num_proc is not None and num_proc <= 0:
									raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''       )

					SCREAMING_SNAKE_CASE_   :	int													=   dataset
					SCREAMING_SNAKE_CASE_   :	Dict													=   path_or_buf
					SCREAMING_SNAKE_CASE_   :	str													=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   num_proc
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   'utf-8'
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   to_json_kwargs


	def   __snake_case			(						self       ):
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.to_json_kwargs.pop('path_or_buf' ,    UpperCamelCase__       )
					SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.to_json_kwargs.pop('orient' ,    'records'       )
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.to_json_kwargs.pop('lines' ,    True if orient == 'records' else False       )
					SCREAMING_SNAKE_CASE_   :	int													=   self.to_json_kwargs.pop('index' ,    False if orient in ['split', 'table'] else True       )
					SCREAMING_SNAKE_CASE_   :	List[str]													=   self.to_json_kwargs.pop('compression' ,    UpperCamelCase__       )

					if compression not in [None, "infer", "gzip", "bz2", "xz"]:
									raise NotImplementedError(F'''`datasets` currently does not support {compression} compression'''       )

					if isinstance(self.path_or_buf ,    (str, bytes, os.PathLike)       ):
									with fsspec.open(self.path_or_buf ,    'wb' ,    compression=UpperCamelCase__       ) as buffer:
													SCREAMING_SNAKE_CASE_   :	Dict													=   self._write(file_obj=UpperCamelCase__ ,    orient=UpperCamelCase__ ,    lines=UpperCamelCase__ ,    index=UpperCamelCase__ ,    **self.to_json_kwargs       )
					else:
									if compression:
													raise NotImplementedError(
													    F'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
													    ' was passed. Please provide a local path instead.'       )
									SCREAMING_SNAKE_CASE_   :	List[Any]													=   self._write(
									    file_obj=self.path_or_buf ,    orient=UpperCamelCase__ ,    lines=UpperCamelCase__ ,    index=UpperCamelCase__ ,    **self.to_json_kwargs       )
					return written


	def   __snake_case			(						self ,    UpperCamelCase__       ):
					SCREAMING_SNAKE_CASE_   :	Tuple													=   args

					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   query_table(
					    table=self.dataset.data ,    key=slice(UpperCamelCase__ ,    offset + self.batch_size       ) ,    indices=self.dataset._indices ,    )
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   batch.to_pandas().to_json(
					    path_or_buf=UpperCamelCase__ ,    orient=UpperCamelCase__ ,    lines=UpperCamelCase__ ,    index=UpperCamelCase__ ,    **UpperCamelCase__       )
					if not json_str.endswith('\n'       ):
									json_str += "\n"
					return json_str.encode(self.encoding       )






	def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    **UpperCamelCase__ ,    ):
					SCREAMING_SNAKE_CASE_   :	str													=   0

					if self.num_proc is None or self.num_proc == 1:
									for offset in logging.tqdm(
									    range(0 ,    len(self.dataset       ) ,    self.batch_size       ) ,    unit='ba' ,    disable=not logging.is_progress_bar_enabled() ,    desc='Creating json from Arrow format' ,    ):
													SCREAMING_SNAKE_CASE_   :	List[Any]													=   self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
													written += file_obj.write(UpperCamelCase__       )
					else:
									SCREAMING_SNAKE_CASE_   :	str													=   len(self.dataset       ), self.batch_size
									with multiprocessing.Pool(self.num_proc       ) as pool:
													for json_str in logging.tqdm(
													    pool.imap(
													        self._batch_json ,    [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 ,    UpperCamelCase__ ,    UpperCamelCase__       )] ,    ) ,    total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,    unit='ba' ,    disable=not logging.is_progress_bar_enabled() ,    desc='Creating json from Arrow format' ,    ):
																	written += file_obj.write(UpperCamelCase__       )

					return written







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

import sentencepiece as spm

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


A        =			logging.get_logger(__name__)

A        =			{'vocab_file': 'spm_char.model'}

A        =			{
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

A        =			{
    'microsoft/speecht5_asr': 1024,
    'microsoft/speecht5_tts': 1024,
    'microsoft/speecht5_vc': 1024,
}


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				List[str]				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				Optional[int]				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				Dict				    =						["""input_ids""", """attention_mask"""]


		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__="<s>" ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=UpperCamelCase__ ,    eos_token=UpperCamelCase__ ,    unk_token=UpperCamelCase__ ,    pad_token=UpperCamelCase__ ,    sp_model_kwargs=self.sp_model_kwargs ,    **UpperCamelCase__ ,    )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   vocab_file

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(UpperCamelCase__       )


		@property
		def   __snake_case			(						self       ):
						return self.sp_model.get_piece_size()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab


		def __getstate__(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.__dict__.copy()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   None
						return state


		def __setstate__(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   d

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

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


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.encode(UpperCamelCase__ ,    out_type=UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.piece_to_id(UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.sp_model.IdToPiece(UpperCamelCase__       )
						return token


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   []
						SCREAMING_SNAKE_CASE_   :	int													=   ''
						for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
														out_string += self.sp_model.decode(UpperCamelCase__       ) + token
														SCREAMING_SNAKE_CASE_   :	Any													=   []
										else:
														current_sub_tokens.append(UpperCamelCase__       )
						out_string += self.sp_model.decode(UpperCamelCase__       )
						return out_string.strip()


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=None       ):
						if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return token_ids_a + token_ids_a + [self.eos_token_id]


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False       ):
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=UpperCamelCase__ ,    token_ids_a=UpperCamelCase__ ,    already_has_special_tokens=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [1]
						if token_ids_a is None:
										return ([0] * len(UpperCamelCase__       )) + suffix_ones
						return ([0] * len(UpperCamelCase__       )) + ([0] * len(UpperCamelCase__       )) + suffix_ones






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						if not os.path.isdir(UpperCamelCase__       ):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
										return
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   os.path.join(
						    UpperCamelCase__ ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

						return (out_vocab_file,)