


"""simple docstring"""




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


lowercase_	   =     logging.get_logger(__name__)


lowercase_	   =     '''▁'''

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

lowercase_	   =     {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

lowercase_	   =     {
    '''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}


class 	A_ (	lowerCamelCase__   ):




      '''simple docstring'''


      __snake_case 	=       VOCAB_FILES_NAMES
      __snake_case 	=       PRETRAINED_VOCAB_FILES_MAP
      __snake_case 	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case 	=       ["""input_ids""", """attention_mask"""]




      def __init__(    self:	int						,				a:	Any						,				a:	Optional[int]="</s>"						,				a:	Dict="<unk>"						,				a:	Tuple=[]						,				a:	Optional[Dict[str, Any]] = None						,				**a:	Any						,				):
       __lowerCamelCase		:							str		 =						{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=a						,				unk_token=a						,				additional_special_tokens=a						,				sp_model_kwargs=self.sp_model_kwargs						,				**a						,				)

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




      @property
      def 					_snake_case     (    self:	Tuple    ):
       return self.sp_model.get_piece_size()




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							int		 =						{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]    ):
       __lowerCamelCase		:							List[str]		 =						self.__dict__.copy()
       __lowerCamelCase		:							Optional[int]		 =						None
       return state




      def __setstate__(    self:	Dict						,				a:	Optional[Any]    ):
       __lowerCamelCase		:							Optional[Any]		 =						d

       # for backward compatibility
       if not hasattr(self						,				'sp_model_kwargs'    ):
        __lowerCamelCase		:							List[str]		 =						{}

       __lowerCamelCase		:							int		 =						spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(self.vocab_file    )




      def 					_snake_case     (    self:	List[str]						,				a:	str    ):
       return self.sp_model.encode(a						,				out_type=a    )




      def 					_snake_case     (    self:	Optional[int]						,				a:	Optional[int]    ):
       return self.sp_model.piece_to_id(a    )




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	Tuple    ):




       if index < self.sp_model.get_piece_size():
        __lowerCamelCase		:							Dict		 =						self.sp_model.IdToPiece(a    )
       return token




      def 					_snake_case     (    self:	int						,				a:	Dict    ):
       __lowerCamelCase		:							Optional[int]		 =						[]
       __lowerCamelCase		:							List[Any]		 =						""
       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		:							Dict		 =						[]
        else:
         current_sub_tokens.append(a    )
       out_string += self.sp_model.decode(a    )
       return out_string.strip()




      def 					_snake_case     (    self:	Tuple						,				a:	str						,				a:	Optional[str] = None    ):




       if not os.path.isdir(a    ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
        return
       __lowerCamelCase		:							List[Any]		 =						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		:							Tuple		 =						self.sp_model.serialized_model_proto()
         fi.write(a    )

       return (out_vocab_file,)





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


lowercase_	   =     logging.get_logger(__name__)


@dataclass
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       [
          """no_inference""",
          """no_cuda""",
          """no_tpu""",
          """no_speed""",
          """no_memory""",
          """no_env_print""",
          """no_multi_process""",
      ]




      def __init__(    self:	Any						,				**a:	Optional[Any]    ):
       for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
         __lowerCamelCase		:							str		 =						deprecated_arg[3:]
         setattr(self						,				a						,				not kwargs.pop(a    )    )
         logger.warning(
             F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
             F' {positive_arg}={kwargs[positive_arg]}'    )

       __lowerCamelCase		:							str		 =						kwargs.pop('torchscript'						,				self.torchscript    )
       __lowerCamelCase		:							int		 =						kwargs.pop('torch_xla_tpu_print_metrics'						,				self.torch_xla_tpu_print_metrics    )
       __lowerCamelCase		:							Dict		 =						kwargs.pop('fp16_opt_level'						,				self.fpaa_opt_level    )
       super().__init__(**a    )

      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Trace the models using torchscript"""}   )
      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}   )
      __snake_case 	=       field(
          default="""O1""" ,				metadata={
              """help""": (
                  """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
                  """See details at https://nvidia.github.io/apex/amp.html"""
              )
          } ,				)




      @cached_property
      def 					_snake_case     (    self:	Dict    ):
       requires_backends(self						,				['torch']    )
       logger.info('PyTorch: setting up devices'    )
       if not self.cuda:
        __lowerCamelCase		:							Dict		 =						torch.device('cpu'    )
        __lowerCamelCase		:							str		 =						0
       elif is_torch_tpu_available():
        __lowerCamelCase		:							Optional[int]		 =						xm.xla_device()
        __lowerCamelCase		:							Dict		 =						0
       else:
        __lowerCamelCase		:							Any		 =						torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
        __lowerCamelCase		:							Union[str, Any]		 =						torch.cuda.device_count()
       return device, n_gpu




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return is_torch_tpu_available() and self.tpu




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       # TODO(PVP): currently only single GPU is supported
       return torch.cuda.current_device()




      @property
      def 					_snake_case     (    self:	int    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[0]




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[1]




      @property
      def 					_snake_case     (    self:	List[Any]    ):
       return self.n_gpu > 0

