


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											=       '▁'

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

lowerCamelCase											=       {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

lowerCamelCase											=       {
    'google/pegasus-xsum': 5_12,
}


lowerCamelCase											=       logging.get_logger(__name__)




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  List[str]      =VOCAB_FILES_NAMES

      UpperCamelCase__							:  List[str]      =VOCAB_FILES_NAMES
      UpperCamelCase__							:  Any      =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__							:  str      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__							:  str      =['input_ids', 'attention_mask']






      def __init__(					self						:     Dict			,   lowercase_						:     Any			,   lowercase_						:     int="<pad>"			,   lowercase_						:     List[Any]="</s>"			,   lowercase_						:     List[Any]="<unk>"			,   lowercase_						:     int="<mask_2>"			,   lowercase_						:     List[Any]="<mask_1>"			,   lowercase_						:     Tuple=None			,   lowercase_						:     Optional[Any]=103			,   lowercase_						:     Optional[Dict[str, Any]] = None			,   **lowercase_						:     List[Any]			,   )					->						None:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=offset
             if additional_special_tokens is not None:
                    if not isinstance(lowercase_			,   lowercase_ ):
                           raise TypeError(
                               F'''additional_special_tokens should be of type {type(lowercase_ )}, but is'''
                               F''' {type(lowercase_ )}''' )

                    _lowerCamelCase       :		Dict		=(
                        ([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(lowercase_ )			,   self.offset - 1 )
                    ]

                    if len(set(lowercase_ ) ) != len(lowercase_ ):
                           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}.''' )
                    _lowerCamelCase       :		List[Any]		=additional_special_tokens_extended
             else:
                    _lowerCamelCase       :		int		=[mask_token_sent] if mask_token_sent is not None else []
                    additional_special_tokens += [F'''<unk_{i}>''' for i in range(2			,   self.offset )]

             _lowerCamelCase       :		List[Any]		={} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 eos_token=lowercase_			,   unk_token=lowercase_			,   mask_token=lowercase_			,   pad_token=lowercase_			,   mask_token_sent=lowercase_			,   offset=lowercase_			,   additional_special_tokens=lowercase_			,   sp_model_kwargs=self.sp_model_kwargs			,   **lowercase_			,   )
             _lowerCamelCase       :		List[str]		=mask_token_sent
             _lowerCamelCase       :		Union[str, Any]		=vocab_file
             _lowerCamelCase       :		List[str]		=spm.SentencePieceProcessor(**self.sp_model_kwargs )
             self.sp_model.Load(lowercase_ )

             # add special tokens to encoder dict
             _lowerCamelCase       :		Dict[int, str]		={
                 0: self.pad_token,
                 1: self.eos_token,
             }

             if self.mask_token_sent is not None:
                    self.encoder.update(
                        {
                            2: self.mask_token_sent,
                            3: self.mask_token,
                        } )

             if self.offset > 0:
                    # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                    # mask_token_sent is already added to list -> so start at 1
                    self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,   self.offset - 1 )} )

             _lowerCamelCase       :		Dict[str, int]		={v: k for k, v in self.encoder.items()}






      @property
      def        lowerCamelCase       (					self						:     List[str] )					->						int:
             """simple docstring"""




             return len(self.sp_model ) + self.offset






      def        lowerCamelCase       (					self						:     str )					->						Dict[str, int]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		={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						:     Optional[int] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.__dict__.copy()
             _lowerCamelCase       :		List[Any]		=None
             return state






      def __setstate__(					self						:     Union[str, Any]			,   lowercase_						:     Tuple )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=d

             # for backward compatibility
             if not hasattr(self			,   'sp_model_kwargs' ):
                    _lowerCamelCase       :		Dict		={}

             _lowerCamelCase       :		List[str]		=spm.SentencePieceProcessor(**self.sp_model_kwargs )
             self.sp_model.Load(self.vocab_file )






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     str )					->						List[str]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     str )					->						int:
             """simple docstring"""




             if token in self.decoder:
                    return self.decoder[token]
             elif token in self.added_tokens_decoder:
                    return self.added_tokens_decoder[token]
             _lowerCamelCase       :		Union[str, Any]		=self.sp_model.piece_to_id(lowercase_ )
             return sp_id + self.offset






      def        lowerCamelCase       (					self						:     List[str]			,   lowercase_						:     int )					->						str:
             """simple docstring"""




             if index in self.encoder:
                    return self.encoder[index]
             elif index in self.added_tokens_encoder:
                    return self.added_tokens_encoder[index]
             else:
                    _lowerCamelCase       :		List[str]		=self.sp_model.IdToPiece(index - self.offset )
             return token






      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=[]
             _lowerCamelCase       :		Any		=''
             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(lowercase_ ) + token
                           _lowerCamelCase       :		Tuple		=[]
                    else:
                           current_sub_tokens.append(lowercase_ )
             out_string += self.sp_model.decode(lowercase_ )
             return out_string.strip()






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Tuple=False )					->						List[str]:
             """simple docstring"""




             return 1






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     Optional[int] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=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

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






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     List			,   lowercase_						:     Optional[List] = None			,   lowercase_						:     bool = False )					->						List[int]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     Tuple			,   lowercase_						:     Optional[Any]=None )					->						List[int]:
             """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        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     str			,   lowercase_						:     Optional[str] = None )					->						Tuple[str]:
             """simple docstring"""




             if not os.path.isdir(lowercase_ ):
                    logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                    return
             _lowerCamelCase       :		Union[str, Any]		=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:
                           _lowerCamelCase       :		Any		=self.sp_model.serialized_model_proto()
                           fi.write(lowercase_ )

             return (out_vocab_file,)






import os

import pytest
from attr import dataclass


lowerCamelCase											=       'us-east-1'  # defaults region




@dataclass
class A  :
      UpperCamelCase__							:  str
      UpperCamelCase__							:  Dict      ='arn:aws:iam::558105141721:role/sagemaker_execution_role'
      UpperCamelCase__							:  int      ={
          'task_name': 'mnli',
          'per_device_train_batch_size': 16,
          'per_device_eval_batch_size': 16,
          'do_train': True,
          'do_eval': True,
          'do_predict': True,
          'output_dir': '/opt/ml/model',
          'overwrite_output_dir': True,
          'max_steps': 500,
          'save_steps': 5500,
      }
      UpperCamelCase__							:  Optional[Any]      ={**hyperparameters, 'max_steps': 1000}






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             if self.framework == "pytorch":
                    return [
                        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                        {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                        {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
                    ]
             else:
                    return [
                        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                        {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                        {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
                    ]






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             return F'''{self.framework}-transfromers-test'''






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             return F'''./tests/sagemaker/scripts/{self.framework}'''






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             if self.framework == "pytorch":
                    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
             else:
                    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope='class'     )
def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[Any]		=SageMakerTestEnvironment(framework=request.cls.framework     )


