





"""simple docstring"""




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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_pegasus import PegasusTokenizer
else:
    lowercase				:	Dict      						=  None


lowercase				:	Dict      						=  logging.get_logger(__name__)


lowercase				:	Optional[Any]      						=  "▁"

lowercase				:	List[Any]      						=  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowercase				:	Optional[Any]      						=  {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

lowercase				:	Any      						=  {
    "google/pegasus-xsum": 5_1_2,
}





class 			A__   (							SCREAMING_SNAKE_CASE__     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  VOCAB_FILES_NAMES
   __A       :    List[Any]	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    int	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    Optional[int]	  =  PegasusTokenizer
   __A       :    Any	  =  ['input_ids', 'attention_mask']

   def __init__( self ,  lowercase=None ,  lowercase=None ,  lowercase="<pad>" ,  lowercase="</s>" ,  lowercase="<unk>" ,  lowercase="<mask_2>" ,  lowercase="<mask_1>" ,  lowercase=None ,  lowercase=103 ,  **lowercase ,  )      ->						List[Any]:







          '''simple docstring'''

          a__						:     int			=    offset

          if additional_special_tokens is not None:
                 if not isinstance(a_ ,  a_):
                        raise TypeError(
                            F'additional_special_tokens should be of type {type(a_)}, but is'
                            F' {type(a_)}')

                 a__						:     Any			=    (
                     ([mask_token_sent] + additional_special_tokens)
                     if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                     else additional_special_tokens
                 )
                 # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                 additional_special_tokens_extended += [
                     F'<unk_{i}>' for i in range(len(a_) ,  self.offset - 1)
                 ]

                 if len(set(a_)) != len(a_):
                        raise ValueError(
                            'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
                            F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
                 a__						:     List[Any]			=    additional_special_tokens_extended
          else:
                 a__						:     Dict			=    [mask_token_sent] if mask_token_sent is not None else []
                 additional_special_tokens += [F'<unk_{i}>' for i in range(2 ,  self.offset)]

          super().__init__(
              a_ ,  tokenizer_file=a_ ,  pad_token=a_ ,  eos_token=a_ ,  unk_token=a_ ,  mask_token=a_ ,  mask_token_sent=a_ ,  offset=a_ ,  additional_special_tokens=a_ ,  **a_ ,  )
          a__						:     List[Any]			=    vocab_file
          a__						:     str			=    False if not self.vocab_file else True

   def 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

          a__						:     str			=    set(self.all_special_ids)  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
                 raise ValueError(
                     'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
                     F' {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}')

          return [1 if x in all_special_ids else 0 for x in seq]

   def 							__lowercase		( self ,  lowercase ,  lowercase = None ,  lowercase = False)      ->						Union[str, Any]:







          '''simple docstring'''

          if already_has_special_tokens:
                 return self._special_token_mask(a_)
          elif token_ids_a is None:
                 return self._special_token_mask(a_) + [1]
          else:
                 return self._special_token_mask(token_ids_a + token_ids_a) + [1]

   def 							__lowercase		( self ,  lowercase ,  lowercase=None)      ->						List[Any]:







          '''simple docstring'''

          if token_ids_a is None:
                 return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						List[str]:







          '''simple docstring'''

          if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                     'tokenizer.')

          if not os.path.isdir(a_):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory')
                 return
          a__						:     Optional[int]			=    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_):
                 copyfile(self.vocab_file ,  a_)

          return (out_vocab_file,)









import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
    from deepspeed import DeepSpeedEngine

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



def A_     (		A__    )  ->		Optional[int]:
       if is_torch_version('<'		,				'2.0.0'    ) or not hasattr(A__		,				'_dynamo'    ):
              return False
       return isinstance(A__		,				torch._dynamo.eval_frame.OptimizedModule    )



def A_     (		A__		,				A__ = True    )  ->		int:
       a__						:     Optional[Any]			=    (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

       a__						:     Union[str, Any]			=    is_compiled_module(A__    )
       if is_compiled:
              a__						:     List[str]			=    model
              a__						:     Dict			=    model._orig_mod

       if is_deepspeed_available():
              options += (DeepSpeedEngine,)

       while isinstance(A__		,				A__    ):
              a__						:     str			=    model.module

       if not keep_fpaa_wrapper:
              a__						:     Union[str, Any]			=    getattr(A__		,				'forward'    )
              a__						:     List[Any]			=    model.__dict__.pop('_original_forward'		,				A__    )
              if original_forward is not None:
                     while hasattr(A__		,				'__wrapped__'    ):
                            a__						:     int			=    forward.__wrapped__
                            if forward == original_forward:
                                   break
                     a__						:     List[Any]			=    forward
              if getattr(A__		,				'_converted_to_transformer_engine'		,				A__    ):
                     convert_model(A__		,				to_transformer_engine=A__    )

       if is_compiled:
              a__						:     List[str]			=    model
              a__						:     Any			=    compiled_model

       return model



def A_     (		)  ->		int:
       PartialState().wait_for_everyone()



def A_     (		A__		,				A__    )  ->		Dict:
       if PartialState().distributed_type == DistributedType.TPU:
              xm.save(A__		,				A__    )
       elif PartialState().local_process_index == 0:
              torch.save(A__		,				A__    )



@contextmanager
def A_     (		**A__    )  ->		Any:
       for key, value in kwargs.items():
              a__						:     Optional[int]			=    str(A__    )

       yield

       for key in kwargs:
              if key.upper() in os.environ:
                     del os.environ[key.upper()]



def A_     (		A__    )  ->		List[str]:
       if not hasattr(A__		,				'__qualname__'    ) and not hasattr(A__		,				'__name__'    ):
              a__						:     Dict			=    getattr(A__		,				'__class__'		,				A__    )
       if hasattr(A__		,				'__qualname__'    ):
              return obj.__qualname__
       if hasattr(A__		,				'__name__'    ):
              return obj.__name__
       return str(A__    )



def A_     (		A__		,				A__    )  ->		Dict:
       for key, value in source.items():
              if isinstance(A__		,				A__    ):
                     a__						:     Optional[Any]			=    destination.setdefault(A__		,				{}    )
                     merge_dicts(A__		,				A__    )
              else:
                     a__						:     Optional[int]			=    value

       return destination



def A_     (		A__ = None    )  ->		bool:
       if port is None:
              a__						:     List[Any]			=    2_9500
       with socket.socket(socket.AF_INET		,				socket.SOCK_STREAM    ) as s:
              return s.connect_ex(('localhost', port)    ) == 0


