


'''simple docstring'''





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

UpperCAmelCase__  			=				logging.get_logger(__name__)


UpperCAmelCase__  			=				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

UpperCAmelCase__  			=				{
    '''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'''
    },
}

UpperCAmelCase__  			=				{'''facebook/blenderbot-3B''': 1_2_8}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Dict					      =			VOCAB_FILES_NAMES
			A						:				Optional[int]					      =			PRETRAINED_VOCAB_FILES_MAP
			A						:				List[str]					      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A						:				Tuple					      =			['''input_ids''', '''attention_mask''']
			A						:				Tuple					      =			BlenderbotTokenizer
			def __init__(  self							:			Optional[Any]			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			Any=None			,    lowerCAmelCase_							:			Tuple=None			,    lowerCAmelCase_							:			Any="replace"			,    lowerCAmelCase_							:			List[Any]="<s>"			,    lowerCAmelCase_							:			str="</s>"			,    lowerCAmelCase_							:			Optional[int]="</s>"			,    lowerCAmelCase_							:			Optional[Any]="<s>"			,    lowerCAmelCase_							:			Tuple="<unk>"			,    lowerCAmelCase_							:			Union[str, Any]="<pad>"			,    lowerCAmelCase_							:			Union[str, Any]="<mask>"			,    lowerCAmelCase_							:			List[str]=False			,    lowerCAmelCase_							:			Dict=True			,    **lowerCAmelCase_							:			Dict			,    )      ->		Union[str, Any]:
						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_			,    )

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

						__A=	add_prefix_space

						__A=	'post_processor'
						__A=	getattr(self.backend_tokenizer			,    lowerCAmelCase_			,    lowerCAmelCase_	)
						if tokenizer_component_instance:
									__A=	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:
												__A=	tuple(state['sep']	)
									if "cls" in state:
												__A=	tuple(state['cls']	)

									__A=	False

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

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

									if changes_to_apply:
												__A=	getattr(lowerCAmelCase_			,    state.pop('type'	)	)
												__A=	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 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		str:
						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 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Optional[int]:
						__A=	AddedToken(lowerCAmelCase_			,    lstrip=lowerCAmelCase_			,    rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_			,    lowerCAmelCase_	) else value
						__A=	value
			def 					lowerCAmelCase     (  self							:			Dict			,    *lowerCAmelCase_							:			int			,    **lowerCAmelCase_							:			Tuple	)      ->		BatchEncoding:
						__A=	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 					lowerCAmelCase     (  self							:			Union[str, Any]			,    *lowerCAmelCase_							:			Dict			,    **lowerCAmelCase_							:			int	)      ->		BatchEncoding:
						__A=	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 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Optional[str] = None	)      ->		Tuple[str]:
						__A=	self._tokenizer.model.save(lowerCAmelCase_			,    name=lowerCAmelCase_	)
						return tuple(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			List[int]			,    lowerCAmelCase_							:			Optional[List[int]] = None	)      ->		List[int]:
						__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 + sep + token_ids_a + sep	) * [0]
			def 					lowerCAmelCase     (  self							:			int			,    lowerCAmelCase_							:			List[int]			,    lowerCAmelCase_							:			Optional[List[int]] = None	)      ->		Optional[Any]:
						return token_ids_a + [self.eos_token_id]

			def 					lowerCAmelCase     (  self							:			int			,    lowerCAmelCase_							:			"Conversation"	)      ->		List[int]:
						__A=	[]
						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_	)

						__A=	'  '.join(lowerCAmelCase_	)
						__A=	self.encode(lowerCAmelCase_	)
						if len(lowerCAmelCase_	) > self.model_max_length:
									__A=	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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__  			=				{
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TrOCRForCausalLM''',
					    '''TrOCRPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
					from .processing_trocr import TrOCRProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
					import sys

					UpperCAmelCase__  			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



