

'''simple docstring'''







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						    =      logging.get_logger(__name__)

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

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


class    A__ ( _snake_case     ):
    lowercase		   =						VOCAB_FILES_NAMES
    lowercase		   =						PRETRAINED_VOCAB_FILES_MAP
    lowercase		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase		   =						["input_ids", "attention_mask"]
    lowercase		   =						[]



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__="<unk>"						,			UpperCamelCase__="<s>"						,			UpperCamelCase__="</s>"						,			UpperCamelCase__="<pad>"						,			UpperCamelCase__="[SEP]"						,			UpperCamelCase__="[MASK]"						,			UpperCamelCase__="[CLS]"						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> None:





           '''simple docstring'''
           A_		   = AddedToken(UpperCamelCase__						,			lstrip=UpperCamelCase__						,			rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__						,			UpperCamelCase__    ) else bos_token
           A_		   = AddedToken(UpperCamelCase__						,			lstrip=UpperCamelCase__						,			rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__						,			UpperCamelCase__    ) else eos_token
           A_		   = AddedToken(UpperCamelCase__						,			lstrip=UpperCamelCase__						,			rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__						,			UpperCamelCase__    ) else unk_token
           A_		   = AddedToken(UpperCamelCase__						,			lstrip=UpperCamelCase__						,			rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__						,			UpperCamelCase__    ) else pad_token
           A_		   = AddedToken(UpperCamelCase__						,			lstrip=UpperCamelCase__						,			rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__						,			UpperCamelCase__    ) else cls_token
           A_		   = AddedToken(UpperCamelCase__						,			lstrip=UpperCamelCase__						,			rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__						,			UpperCamelCase__    ) else sep_token

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

           A_		   = {} if sp_model_kwargs is None else sp_model_kwargs

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

           A_		   = vocab_file

           A_		   = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(UpperCamelCase__    )



    @property
    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           return self.sp_model.get_piece_size()



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = {self.convert_ids_to_tokens(UpperCamelCase__    ): i for i in range(self.vocab_size    )}
           vocab.update(self.added_tokens_encoder    )
           return vocab



    def __getstate__(   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = self.__dict__.copy()
           A_		   = None
           return state



    def __setstate__(   self						,			UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = d

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

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



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> List[str]:





           '''simple docstring'''
           return self.sp_model.encode(UpperCamelCase__						,			out_type=UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           return self.sp_model.piece_to_id(UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           A_		   = self.sp_model.IdToPiece(UpperCamelCase__    )
           return token



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = []
           A_		   = """"""
           A_		   = 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(UpperCamelCase__    ) + token
                         A_		   = True
                         A_		   = []
                  else:
                         current_sub_tokens.append(UpperCamelCase__    )
                         A_		   = False
           out_string += self.sp_model.decode(UpperCamelCase__    )
           return out_string.strip()



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = False						,			UpperCamelCase__ = None						,			UpperCamelCase__ = True						,			**UpperCamelCase__						,			)  -> str:





           '''simple docstring'''
           A_		   = kwargs.pop("""use_source_tokenizer"""						,			UpperCamelCase__    )

           A_		   = self.convert_ids_to_tokens(UpperCamelCase__						,			skip_special_tokens=UpperCamelCase__    )

           # 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
           A_		   = []
           A_		   = []
           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(UpperCamelCase__    )    )
                                A_		   = []
                         sub_texts.append(UpperCamelCase__    )
                  else:
                         current_sub_text.append(UpperCamelCase__    )
           if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(UpperCamelCase__    )    )

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

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



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> Tuple[str]:





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

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

           return (out_vocab_file,)



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> List[int]:





           '''simple docstring'''
           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           A_		   = [self.cls_token_id]
           A_		   = [self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = False    )  -> List[int]:





           '''simple docstring'''
           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCamelCase__						,			token_ids_a=UpperCamelCase__						,			already_has_special_tokens=UpperCamelCase__    )

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







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> List[int]:





           '''simple docstring'''
           A_		   = [self.sep_token_id]
           A_		   = [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]



'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
__lowerCamelCase						    =      logging.getLogger(__name__)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = git.Repo(search_parent_directories=UpperCAmelCase__			)
       A_		   = {
           """repo_id""": str(UpperCAmelCase__			),
           """repo_sha""": str(repo.head.object.hexsha			),
           """repo_branch""": str(repo.active_branch			),
       }

       with open(os.path.join(UpperCAmelCase__,    """git_log.json"""			),    """w"""			) as f:
              json.dump(UpperCAmelCase__,    UpperCAmelCase__,    indent=4			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Dict:
       if params.n_gpu <= 0:
              A_		   = 0
              A_		   = -1
              A_		   = True
              A_		   = False
              return

       assert torch.cuda.is_available()

       logger.info("""Initializing GPUs"""			)
       if params.n_gpu > 1:
              assert params.local_rank != -1

              A_		   = int(os.environ["""WORLD_SIZE"""]			)
              A_		   = int(os.environ["""N_GPU_NODE"""]			)
              A_		   = int(os.environ["""RANK"""]			)

              # number of nodes / node ID
              A_		   = params.world_size // params.n_gpu_per_node
              A_		   = params.global_rank // params.n_gpu_per_node
              A_		   = True

              assert params.n_nodes == int(os.environ["""N_NODES"""]			)
              assert params.node_id == int(os.environ["""NODE_RANK"""]			)

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              A_		   = 1
              A_		   = 0
              A_		   = 0
              A_		   = 0
              A_		   = 1
              A_		   = 1
              A_		   = False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       A_		   = params.node_id == 0 and params.local_rank == 0
       A_		   = params.n_nodes > 1

       # summary
       A_		   = F'''--- Global rank: {params.global_rank} - '''
       logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes			)
       logger.info(PREFIX + """Node ID        : %i""" % params.node_id			)
       logger.info(PREFIX + """Local rank     : %i""" % params.local_rank			)
       logger.info(PREFIX + """World size     : %i""" % params.world_size			)
       logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node			)
       logger.info(PREFIX + """Master         : %s""" % str(params.is_master			)			)
       logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node			)			)
       logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu			)			)
       logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()			)

       # set GPU device
       torch.cuda.set_device(params.local_rank			)

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info("""Initializing PyTorch distributed"""			)
              torch.distributed.init_process_group(
                  init_method="""env://""",    backend="""nccl""",    )







def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       np.random.seed(args.seed			)
       torch.manual_seed(args.seed			)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed			)
