





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


_lowerCAmelCase 				=     logging.get_logger(__name__)


_lowerCAmelCase 				=     """▁"""

_lowerCAmelCase 				=     {"""vocab_file""": """spiece.model"""}

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

_lowerCAmelCase 				=     {
    """google/reformer-crime-and-punishment""": 524_288,
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
						a						       =							VOCAB_FILES_NAMES
						a						       =							PRETRAINED_VOCAB_FILES_MAP
						a						       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a						       =							['''input_ids''', '''attention_mask''']
						def __init__(		self      ,       a__      ,       a__="</s>"      ,       a__="<unk>"      ,       a__=[]      ,       a__ = None      ,       **a__      ,       ):
									A_     :				List[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__      ,       )

									A_     :				str			      =	vocab_file
									A_     :				Tuple			      =	spm.SentencePieceProcessor(**self.sp_model_kwargs       )
									self.sp_model.Load(a__       )
						@property
						def 		_lowerCamelCase      (		self       ):
									return self.sp_model.get_piece_size()
						def 		_lowerCamelCase      (		self       ):
									A_     :				Any			      =	{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       ):
									A_     :				str			      =	self.__dict__.copy()
									A_     :				List[Any]			      =	None
									return state
						def __setstate__(		self      ,       a__       ):
									A_     :				Any			      =	d

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

									A_     :				Optional[Any]			      =	spm.SentencePieceProcessor(**self.sp_model_kwargs       )
									self.sp_model.Load(self.vocab_file       )
						def 		_lowerCamelCase      (		self      ,       a__       ):
									return self.sp_model.encode(a__      ,       out_type=a__       )
						def 		_lowerCamelCase      (		self      ,       a__       ):
									return self.sp_model.piece_to_id(a__       )
						def 		_lowerCamelCase      (		self      ,       a__       ):
									if index < self.sp_model.get_piece_size():
												A_     :				int			      =	self.sp_model.IdToPiece(a__       )
									return token
						def 		_lowerCamelCase      (		self      ,       a__       ):
									A_     :				str			      =	[]
									A_     :				Tuple			      =	""""""
									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
															A_     :				Union[str, Any]			      =	[]
												else:
															current_sub_tokens.append(a__       )
									out_string += self.sp_model.decode(a__       )
									return out_string.strip()






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
									if not os.path.isdir(a__       ):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
												return
									A_     :				Union[str, 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:
															A_     :				List[Any]			      =	self.sp_model.serialized_model_proto()
															fi.write(a__       )

									return (out_vocab_file,)













# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


_lowerCAmelCase 				=     """3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
      import torch

      print("""Torch version:""", torch.__version__)
      print("""Cuda available:""", torch.cuda.is_available())
      print("""Cuda version:""", torch.version.cuda)
      print("""CuDNN version:""", torch.backends.cudnn.version())
      print("""Number of GPUs available:""", torch.cuda.device_count())
      print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
      print("""Torch version:""", None)

try:
      import deepspeed

      print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
      print("""DeepSpeed version:""", None)

try:
      import tensorflow as tf

      print("""TensorFlow version:""", tf.__version__)
      print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
      print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
      print("""TensorFlow version:""", None)






