
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""": 128}







class 			A_  (     lowercase_  ):




	'''simple docstring'''


	_UpperCamelCase	:	Dict									=			VOCAB_FILES_NAMES
	_UpperCamelCase	:	List[str]									=			PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase	:	List[Any]									=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCamelCase	:	Dict									=			['''input_ids''', '''attention_mask''']
	_UpperCamelCase	:	str									=			BlenderbotTokenizer





	def __init__(			self						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       snake_case="replace"						,       snake_case="<s>"						,       snake_case="</s>"						,       snake_case="</s>"						,       snake_case="<s>"						,       snake_case="<unk>"						,       snake_case="<pad>"						,       snake_case="<mask>"						,       snake_case=False						,       snake_case=True						,       **snake_case						,       ):
		super().__init__(
		    snake_case						,       snake_case						,       tokenizer_file=snake_case						,       errors=snake_case						,       bos_token=snake_case						,       eos_token=snake_case						,       sep_token=snake_case						,       cls_token=snake_case						,       unk_token=snake_case						,       pad_token=snake_case						,       mask_token=snake_case						,       add_prefix_space=snake_case						,       trim_offsets=snake_case						,       **snake_case						,       )

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

		lowercase	    =       add_prefix_space

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

			lowercase	    =       False

			if state.get('add_prefix_space'						,       snake_case			) != add_prefix_space:
				lowercase	    =       add_prefix_space
				lowercase	    =       True

			if state.get('trim_offsets'						,       snake_case			) != trim_offsets:
				lowercase	    =       trim_offsets
				lowercase	    =       True

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





	@property
	# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
	def       SCREAMING_SNAKE_CASE__		(			self			):
		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       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       AddedToken(snake_case						,       lstrip=snake_case						,       rstrip=snake_case			) if isinstance(snake_case						,       snake_case			) else value
		lowercase	    =       value





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		lowercase	    =       kwargs.get('is_split_into_words'						,       snake_case			)
		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(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		lowercase	    =       kwargs.get('is_split_into_words'						,       snake_case			)

		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(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		lowercase	    =       self._tokenizer.model.save(snake_case						,       name=snake_case			)
		return tuple(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		lowercase	    =       [self.sep_token_id]
		lowercase	    =       [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       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		return token_ids_a + [self.eos_token_id]





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       []
		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(snake_case			)

		lowercase	    =       '  '.join(snake_case			)
		lowercase	    =       self.encode(snake_case			)
		if len(snake_case			) > self.model_max_length:
			lowercase	    =       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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




