
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__magic_name__            =  logging.get_logger(__name__)




class 					_SCREAMING_SNAKE_CASE  :
      _A						:		str
      _A						:		str    	=     None
      @staticmethod
      def A_			(			):
          raise NotImplementedError
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase ):
          raise NotImplementedError
      def A_			(			self		,	lowerCamelCase ):
          raise NotImplementedError
      def A_			(			self ):
          if not self.is_available():
              raise RuntimeError(
                  F"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" )



      @classmethod
      def A_			(			cls ):
          return F"""`pip install {cls.pip_package or cls.name}`"""





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Dict    	=     'optuna'
      @staticmethod
      def A_			(			):
          return is_optuna_available()
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase ):
          return run_hp_search_optuna(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase )



      def A_			(			self		,	lowerCamelCase ):
          return default_hp_space_optuna(lowerCamelCase )





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		str    	=     'ray'
      _A						:		Dict    	=     '\'ray[tune]\''
      @staticmethod
      def A_			(			):
          return is_ray_available()
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase ):
          return run_hp_search_ray(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase )



      def A_			(			self		,	lowerCamelCase ):
          return default_hp_space_ray(lowerCamelCase )





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		List[Any]    	=     'sigopt'
      @staticmethod
      def A_			(			):
          return is_sigopt_available()
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase ):
          return run_hp_search_sigopt(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase )



      def A_			(			self		,	lowerCamelCase ):
          return default_hp_space_sigopt(lowerCamelCase )





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		List[str]    	=     'wandb'
      @staticmethod
      def A_			(			):
          return is_wandb_available()
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase ):
          return run_hp_search_wandb(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase )



      def A_			(			self		,	lowerCamelCase ):
          return default_hp_space_wandb(lowerCamelCase )





__magic_name__            =  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__						=				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
    if len(__lowerCAmelCase							) > 0:
        snake_case__						=				available_backends[0].name
        if len(__lowerCAmelCase							) > 1:
            logger.info(
                F"""{len(__lowerCAmelCase							)} hyperparameter search backends available. Using {name} as the default."""							)
        return name
    raise RuntimeError(
        "No hyperparameter search backend available.\n"
        + "\n".join(
            F""" - To install {backend.name} run {backend.pip_install()}"""
            for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()							)							)





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:
     __magic_name__            =  None


__magic_name__            =  logging.get_logger(__name__)


__magic_name__            =  '''▁'''

__magic_name__            =  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__            =  {
    '''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'''
    },
}

__magic_name__            =  {
    '''google/pegasus-xsum''': 512,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		int    	=     VOCAB_FILES_NAMES
      _A						:		Dict    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A						:		Tuple    	=     PegasusTokenizer
      _A						:		str    	=     ['input_ids', 'attention_mask']
      def __init__(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase="<pad>"		,	lowerCamelCase="</s>"		,	lowerCamelCase="<unk>"		,	lowerCamelCase="<mask_2>"		,	lowerCamelCase="<mask_1>"		,	lowerCamelCase=None		,	lowerCamelCase=1_03		,	**lowerCamelCase		,	):
          snake_case__						=				offset

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

              snake_case__						=				(
                  ([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(lowerCamelCase )		,	self.offset - 1 )
              ]

              if len(set(lowerCamelCase ) ) != len(lowerCamelCase ):
                  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}.""" )
              snake_case__						=				additional_special_tokens_extended
          else:
              snake_case__						=				[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__(
              lowerCamelCase		,	tokenizer_file=lowerCamelCase		,	pad_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	mask_token_sent=lowerCamelCase		,	offset=lowerCamelCase		,	additional_special_tokens=lowerCamelCase		,	**lowerCamelCase		,	)
          snake_case__						=				vocab_file
          snake_case__						=				False if not self.vocab_file else True
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):
          if already_has_special_tokens:
              return self._special_token_mask(lowerCamelCase )
          elif token_ids_a is None:
              return self._special_token_mask(lowerCamelCase ) + [1]
          else:
              return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=None ):
          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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          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(lowerCamelCase ):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)



