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

import sentencepiece as spm

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


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {'''vocab_file''': '''sentencepiece.model'''}

__magic_name__            =  {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__magic_name__            =  {
    '''google/rembert''': 256,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Optional[Any]    	=     VOCAB_FILES_NAMES
      _A						:		Optional[int]    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[UNK]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[PAD]"		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[MASK]"		,	**lowerCamelCase		,	):
          super().__init__(
              do_lower_case=lowerCamelCase		,	remove_space=lowerCamelCase		,	keep_accents=lowerCamelCase		,	bos_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	sep_token=lowerCamelCase		,	pad_token=lowerCamelCase		,	cls_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	**lowerCamelCase		,	)

          snake_case__						=				do_lower_case
          snake_case__						=				remove_space
          snake_case__						=				keep_accents
          snake_case__						=				vocab_file

          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(lowerCamelCase )
      @property
      def A_			(			self ):
          return len(self.sp_model )
      def A_			(			self ):
          snake_case__						=				{self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(			self ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          snake_case__						=				d
          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=False ):
          snake_case__						=				self.sp_model.EncodeAsPieces(lowerCamelCase )
          return pieces
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.PieceToId(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.IdToPiece(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				self.sp_model.decode_pieces(lowerCamelCase )
          return out_string
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[self.cls_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):

          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model." )
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
              return [1] + ([0] * len(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
          return [1] + ([0] * len(lowerCamelCase )) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[self.cls_token_id]

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



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not os.path.isdir(lowerCamelCase ):
              logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase ) )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)








import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return EnvironmentCommand()





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return EnvironmentCommand(args.accelerate_config_file							)





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      @staticmethod
      def A_			(			lowerCamelCase ):
          snake_case__						=				parser.add_parser("env" )
          download_parser.set_defaults(func=lowerCamelCase )
          download_parser.add_argument(
              "--accelerate-config_file"		,	default=lowerCamelCase		,	help="The accelerate config file to use for the default values in the launching script."		,	)
          download_parser.set_defaults(func=lowerCamelCase )
      def __init__(			self		,	lowerCamelCase		,	*lowerCamelCase ):
          snake_case__						=				accelerate_config_file
      def A_			(			self ):
          snake_case__						=				"not installed"
          if is_safetensors_available():
              import safetensors

              snake_case__						=				safetensors.__version__
          elif importlib.util.find_spec("safetensors" ) is not None:
              import safetensors

              snake_case__						=				F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

          snake_case__						=				"not installed"
          snake_case__						=				snake_case__						=				"not found"
          if is_accelerate_available():
              import accelerate
              from accelerate.commands.config import default_config_file, load_config_from_file

              snake_case__						=				accelerate.__version__
              # Get the default from the config file.
              if self._accelerate_config_file is not None or os.path.isfile(lowerCamelCase ):
                  snake_case__						=				load_config_from_file(self._accelerate_config_file ).to_dict()

              snake_case__						=				(
                  "\n".join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
                  if isinstance(lowerCamelCase		,	lowerCamelCase )
                  else F"""\t{accelerate_config}"""
              )

          snake_case__						=				"not installed"
          snake_case__						=				"NA"
          if is_torch_available():
              import torch

              snake_case__						=				torch.__version__
              snake_case__						=				torch.cuda.is_available()

          snake_case__						=				"not installed"
          snake_case__						=				"NA"
          if is_tf_available():
              import tensorflow as tf

              snake_case__						=				tf.__version__
              try:
                  # deprecated in v2.1
                  snake_case__						=				tf.test.is_gpu_available()
              except AttributeError:
                  # returns list of devices, convert to bool
                  snake_case__						=				bool(tf.config.list_physical_devices("GPU" ) )

          snake_case__						=				"not installed"
          snake_case__						=				"not installed"
          snake_case__						=				"not installed"
          snake_case__						=				"NA"
          if is_flax_available():
              import flax
              import jax
              import jaxlib

              snake_case__						=				flax.__version__
              snake_case__						=				jax.__version__
              snake_case__						=				jaxlib.__version__
              snake_case__						=				jax.lib.xla_bridge.get_backend().platform

          snake_case__						=				{
              "`transformers` version": version,
              "Platform": platform.platform(),
              "Python version": platform.python_version(),
              "Huggingface_hub version": huggingface_hub.__version__,
              "Safetensors version": F"""{safetensors_version}""",
              "Accelerate version": F"""{accelerate_version}""",
              "Accelerate config": F"""{accelerate_config_str}""",
              "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
              "Tensorflow version (GPU?)": F"""{tf_version} ({tf_cuda_available})""",
              "Flax version (CPU?/GPU?/TPU?)": F"""{flax_version} ({jax_backend})""",
              "Jax version": F"""{jax_version}""",
              "JaxLib version": F"""{jaxlib_version}""",
              "Using GPU in script?": "<fill in>",
              "Using distributed or parallel set-up in script?": "<fill in>",
          }

          print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
          print(self.format_dict(lowerCamelCase ) )

          return info



      @staticmethod
      def A_			(			lowerCamelCase ):
          return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"



