




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


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{"""vocab_file""": """spm_char.model"""}

UpperCamelCase__    			=			{
    """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""",
    }
}

UpperCamelCase__    			=			{
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}
class 			a__    (					snake_case__      ):
						_a							:    int					=    VOCAB_FILES_NAMES
						_a							:    Optional[int]					=    PRETRAINED_VOCAB_FILES_MAP
						_a							:    Tuple					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:    str					=    ["""input_ids""", """attention_mask"""]






						def __init__(   self		,			_A		,			_A="<s>"		,			_A="</s>"		,			_A="<unk>"		,			_A="<pad>"		,			_A = None		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {} if sp_model_kwargs is None else sp_model_kwargs

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

											__lowerCAmelCase				   =       vocab_file

											__lowerCAmelCase				   =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
											self.sp_model.Load(_A			)






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.sp_model.get_piece_size()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {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			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.__dict__.copy()
											__lowerCAmelCase				   =       None
											return state






						def __setstate__(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       d

											# for backward compatibility
											if not hasattr(self		,			"sp_model_kwargs"			):
																__lowerCAmelCase				   =       {}

											__lowerCAmelCase				   =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
											self.sp_model.Load(self.vocab_file			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return self.sp_model.encode(_A		,			out_type=_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											return self.sp_model.piece_to_id(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.sp_model.IdToPiece(_A			)
											return token






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       []
											__lowerCAmelCase				   =       ""
											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(_A			) + token
																					__lowerCAmelCase				   =       []
																else:
																					current_sub_tokens.append(_A			)
											out_string += self.sp_model.decode(_A			)
											return out_string.strip()






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=None			):




											"""simple docstring"""



											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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None		,			_A = False			):




											"""simple docstring"""



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

											__lowerCAmelCase				   =       [1]
											if token_ids_a is None:
																return ([0] * len(_A			)) + suffix_ones
											return ([0] * len(_A			)) + ([0] * len(_A			)) + suffix_ones






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											if not os.path.isdir(_A			):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
																return
											__lowerCAmelCase				   =       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:
																					__lowerCAmelCase				   =       self.sp_model.serialized_model_proto()
																					fi.write(_A			)

											return (out_vocab_file,)










import pytest

from datasets import inspect_metric, list_metrics, load_metric







@pytest.fixture
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[Any]     ):
					monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"      ,      set()     )







@pytest.fixture
def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					class 			a__    :






											def __init__(   self		,			_A			):




																"""simple docstring"""



																__lowerCAmelCase				   =       metric_id






					class 			a__    :
											_a							:    Optional[int]					=    [MetricMock(snake_case__      ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]






											def 				__SCREAMING_SNAKE_CASE(   self			):




																"""simple docstring"""



																return self._metrics






					monkeypatch.setattr("datasets.inspect.huggingface_hub"      ,      HfhMock()     )







@pytest.mark.parametrize(
    "func, args"      ,      [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]     )
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					if "tmp_path" in args:
										__lowerCAmelCase				   =       tuple(arg if arg != "tmp_path" else tmp_path for arg in args     )
					with pytest.warns(SCREAMING_SNAKE_CASE_      ,      match="https://huggingface.co/docs/evaluate"     ):
										func(*SCREAMING_SNAKE_CASE_     )




