



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

lowerCAmelCase__						:	int					=logging.get_logger(__name__)


lowerCAmelCase__						:	Union[str, Any]					={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase__						:	Optional[int]					={
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

lowerCAmelCase__						:	Dict					={'''facebook/blenderbot-3B''': 128}

class   UpperCAmelCase_    (  __A			):

						'''simple docstring'''




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






						def __init__(       self ,				_A=None ,				_A=None ,				_A=None ,				_A="replace" ,				_A="<s>" ,				_A="</s>" ,				_A="</s>" ,				_A="<s>" ,				_A="<unk>" ,				_A="<pad>" ,				_A="<mask>" ,				_A=False ,				_A=True ,				**_A ,				):







													'''simple docstring'''

													super().__init__(
													    lowerCAmelCase_ ,				lowerCAmelCase_ ,				tokenizer_file=lowerCAmelCase_ ,				errors=lowerCAmelCase_ ,				bos_token=lowerCAmelCase_ ,				eos_token=lowerCAmelCase_ ,				sep_token=lowerCAmelCase_ ,				cls_token=lowerCAmelCase_ ,				unk_token=lowerCAmelCase_ ,				pad_token=lowerCAmelCase_ ,				mask_token=lowerCAmelCase_ ,				add_prefix_space=lowerCAmelCase_ ,				trim_offsets=lowerCAmelCase_ ,				**lowerCAmelCase_ ,				)

													__SCREAMING_SNAKE_CASE										=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
													if pre_tok_state.get('add_prefix_space' ,				lowerCAmelCase_						) != add_prefix_space:
																				__SCREAMING_SNAKE_CASE										=   getattr(lowerCAmelCase_ ,				pre_tok_state.pop('type'						)						)
																				__SCREAMING_SNAKE_CASE										=   add_prefix_space
																				__SCREAMING_SNAKE_CASE										=   pre_tok_class(**lowerCAmelCase_						)

													__SCREAMING_SNAKE_CASE										=   add_prefix_space

													__SCREAMING_SNAKE_CASE										=   "post_processor"
													__SCREAMING_SNAKE_CASE										=   getattr(self.backend_tokenizer ,				lowerCAmelCase_ ,				lowerCAmelCase_						)
													if tokenizer_component_instance:
																				__SCREAMING_SNAKE_CASE										=   json.loads(tokenizer_component_instance.__getstate__()						)

																				# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																				if "sep" in state:
																											__SCREAMING_SNAKE_CASE										=   tuple(state['sep']						)
																				if "cls" in state:
																											__SCREAMING_SNAKE_CASE										=   tuple(state['cls']						)

																				__SCREAMING_SNAKE_CASE										=   False

																				if state.get('add_prefix_space' ,				lowerCAmelCase_						) != add_prefix_space:
																											__SCREAMING_SNAKE_CASE										=   add_prefix_space
																											__SCREAMING_SNAKE_CASE										=   True

																				if state.get('trim_offsets' ,				lowerCAmelCase_						) != trim_offsets:
																											__SCREAMING_SNAKE_CASE										=   trim_offsets
																											__SCREAMING_SNAKE_CASE										=   True

																				if changes_to_apply:
																											__SCREAMING_SNAKE_CASE										=   getattr(lowerCAmelCase_ ,				state.pop('type'						)						)
																											__SCREAMING_SNAKE_CASE										=   component_class(**lowerCAmelCase_						)
																											setattr(self.backend_tokenizer ,				lowerCAmelCase_ ,				lowerCAmelCase_						)






						@property
						# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
						def 			_A      (       self						):







													'''simple docstring'''

													if self._mask_token is None:
																				if self.verbose:
																											logger.error('Using mask_token, but it is not set yet.'						)
																				return None
													return str(self._mask_token						)






						@mask_token.setter
						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   AddedToken(lowerCAmelCase_ ,				lstrip=lowerCAmelCase_ ,				rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_ ,				lowerCAmelCase_						) else value
													__SCREAMING_SNAKE_CASE										=   value






						def 			_A      (       self ,				*_A ,				**_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   kwargs.get('is_split_into_words' ,				lowerCAmelCase_						)
													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*lowerCAmelCase_ ,				**lowerCAmelCase_						)






						def 			_A      (       self ,				*_A ,				**_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   kwargs.get('is_split_into_words' ,				lowerCAmelCase_						)

													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*lowerCAmelCase_ ,				**lowerCAmelCase_						)






						def 			_A      (       self ,				_A ,				_A = None						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self._tokenizer.model.save(lowerCAmelCase_ ,				name=lowerCAmelCase_						)
													return tuple(lowerCAmelCase_						)






						def 			_A      (       self ,				_A ,				_A = 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 + sep + token_ids_a + sep						) * [0]






						def 			_A      (       self ,				_A ,				_A = None						):







													'''simple docstring'''

													return token_ids_a + [self.eos_token_id]






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   []
													for is_user, text in conversation.iter_texts():
																				if is_user:
																											# We need to space prefix as it's being done within blenderbot
																											inputs.append(' ' + text						)
																				else:
																											# Generated responses should contain them already.
																											inputs.append(lowerCAmelCase_						)

													__SCREAMING_SNAKE_CASE										=   "  ".join(lowerCAmelCase_						)
													__SCREAMING_SNAKE_CASE										=   self.encode(lowerCAmelCase_						)
													if len(lowerCAmelCase_						) > self.model_max_length:
																				__SCREAMING_SNAKE_CASE										=   input_ids[-self.model_max_length :]
																				logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""						)
													return input_ids














"""simple docstring"""
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


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			:
	__magic_name__       =							42
	__magic_name__       =							None





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     Optional[Any]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]		)		->     str:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				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 	_SCREAMING_SNAKE_CASE ( cls   :					Tuple		)		->     List[str]:
				return f"""`pip install {cls.pip_package or cls.name}`"""







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''optuna'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[str]:
				return is_optuna_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     int:
				return run_hp_search_optuna(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Dict		)		->     Optional[Any]:
				return default_hp_space_optuna(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''ray'''
	__magic_name__       =							'''\'ray[tune]\''''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				return is_ray_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     List[str]:
				return run_hp_search_ray(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Tuple		)		->     Optional[int]:
				return default_hp_space_ray(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''sigopt'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[Any]:
				return is_sigopt_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					str		)		->     str:
				return run_hp_search_sigopt(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Any		)		->     Any:
				return default_hp_space_sigopt(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''wandb'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     int:
				return is_wandb_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Optional[Any]		)		->     Optional[Any]:
				return run_hp_search_wandb(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]		)		->     Any:
				return default_hp_space_wandb(lowerCAmelCase_		)





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

def 							snake_case (  ):
			UpperCAmelCase_	:					List[str]     	=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
			if len(A__		) > 0:
						UpperCAmelCase_	:					Optional[Any]     	=   available_backends[0].name
						if len(A__		) > 1:
									logger.info(
									    F"""{len(A__		)} 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()		)		)



