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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase    :				str							     =	logging.get_logger(__name__)

__lowerCamelCase    :				str							     =	{'''vocab_file''': '''spiece.model'''}

__lowerCamelCase    :				str							     =	{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

__lowerCamelCase    :				List[str]							     =	{
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}







class __snake_case       (		lowerCamelCase_	):
     lowerCAmelCase_														=							VOCAB_FILES_NAMES
     lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_														=							["input_ids", "attention_mask"]
     lowerCAmelCase_														=							[]




     def __init__(					self    :       Union[str, Any] ,			_lowercase    :       Any ,			_lowercase    :       Optional[int]="<unk>" ,			_lowercase    :       List[Any]="<s>" ,			_lowercase    :       Dict="</s>" ,			_lowercase    :       int="<pad>" ,			_lowercase    :       str="[SEP]" ,			_lowercase    :       List[str]="[MASK]" ,			_lowercase    :       Dict="[CLS]" ,			_lowercase    :       Optional[Dict[str, Any]] = None ,			**_lowercase    :       Optional[int] ,			):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else bos_token
       SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else eos_token
       SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else unk_token
       SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else pad_token
       SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else cls_token
       SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE__    							=   AddedToken(_lowercase ,			lstrip=_lowercase ,			rstrip=_lowercase   ) if isinstance(_lowercase ,			_lowercase   ) else mask_token

       SCREAMING_SNAKE_CASE__    							=   {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=_lowercase ,			eos_token=_lowercase ,			unk_token=_lowercase ,			pad_token=_lowercase ,			sep_token=_lowercase ,			mask_token=_lowercase ,			cls_token=_lowercase ,			sp_model_kwargs=self.sp_model_kwargs ,			**_lowercase ,			)

       SCREAMING_SNAKE_CASE__    							=   vocab_file

       SCREAMING_SNAKE_CASE__    							=   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(_lowercase   )




     @property
     def 			__a		(					self    :       Tuple   ):



       """simple docstring"""






       return self.sp_model.get_piece_size()




     def 			__a		(					self    :       int   ):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__    							=   {self.convert_ids_to_tokens(_lowercase   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab




     def __getstate__(					self    :       Dict   ):



       """simple docstring"""






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




     def __setstate__(					self    :       List[str] ,			_lowercase    :       Optional[int]   ):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__    							=   d

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

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




     def 			__a		(					self    :       List[Any] ,			_lowercase    :       str   ):



       """simple docstring"""






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




     def 			__a		(					self    :       Any ,			_lowercase    :       str   ):



       """simple docstring"""






       return self.sp_model.piece_to_id(_lowercase   )




     def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str]   ):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__    							=   self.sp_model.IdToPiece(_lowercase   )
       return token




     def 			__a		(					self    :       List[Any] ,			_lowercase    :       Tuple   ):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__    							=   []
       SCREAMING_SNAKE_CASE__    							=   """"""
       SCREAMING_SNAKE_CASE__    							=   False
       for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
           if not prev_is_special:
             out_string += " "
           out_string += self.sp_model.decode(_lowercase   ) + token
           SCREAMING_SNAKE_CASE__    							=   True
           SCREAMING_SNAKE_CASE__    							=   []
         else:
           current_sub_tokens.append(_lowercase   )
           SCREAMING_SNAKE_CASE__    							=   False
       out_string += self.sp_model.decode(_lowercase   )
       return out_string.strip()




     def 			__a		(					self    :       Dict ,			_lowercase    :       List[int] ,			_lowercase    :       bool = False ,			_lowercase    :       bool = None ,			_lowercase    :       bool = True ,			**_lowercase    :       Optional[Any] ,			):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""use_source_tokenizer""" ,			_lowercase   )

       SCREAMING_SNAKE_CASE__    							=   self.convert_ids_to_tokens(_lowercase ,			skip_special_tokens=_lowercase   )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       SCREAMING_SNAKE_CASE__    							=   []
       SCREAMING_SNAKE_CASE__    							=   []
       for token in filtered_tokens:
         if skip_special_tokens and token in self.all_special_ids:
           continue
         if token in self.added_tokens_encoder:
           if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(_lowercase   )   )
             SCREAMING_SNAKE_CASE__    							=   []
           sub_texts.append(_lowercase   )
         else:
           current_sub_text.append(_lowercase   )
       if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(_lowercase   )   )

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
         SCREAMING_SNAKE_CASE__    							=   re.sub(R""" (\[(MASK|SEP)\])""" ,			R"""\1""" ,			""" """.join(_lowercase   )   )
       else:
         SCREAMING_SNAKE_CASE__    							=   """""".join(_lowercase   )

       SCREAMING_SNAKE_CASE__    							=   (
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
         SCREAMING_SNAKE_CASE__    							=   self.clean_up_tokenization(_lowercase   )
         return clean_text
       else:
         return text




     def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



       """simple docstring"""






       if not os.path.isdir(_lowercase   ):
         logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
       SCREAMING_SNAKE_CASE__    							=   os.path.join(
           _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(_lowercase   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file ,			_lowercase   )
       elif not os.path.isfile(self.vocab_file   ):
         with open(_lowercase ,			"""wb"""   ) as fi:
           SCREAMING_SNAKE_CASE__    							=   self.sp_model.serialized_model_proto()
           fi.write(_lowercase   )

       return (out_vocab_file,)




     def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None   ):



       """simple docstring"""






       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       SCREAMING_SNAKE_CASE__    							=   [self.cls_token_id]
       SCREAMING_SNAKE_CASE__    							=   [self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep




     def 			__a		(					self    :       List[str] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None ,			_lowercase    :       bool = False   ):



       """simple docstring"""






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

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




     def 			__a		(					self    :       List[str] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None   ):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__    							=   [self.sep_token_id]
       SCREAMING_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]

# 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    :				Tuple							     =	'''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)
