


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

__snake_case				      =   logging.get_logger(__name__)


__snake_case				      =   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__snake_case				      =   {
    "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"
    },
}

__snake_case				      =   {"facebook/blenderbot-3B": 128}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= PRETRAINED_VOCAB_FILES_MAP
			lowercase						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= ["""input_ids""", """attention_mask"""]
			lowercase						= BlenderbotTokenizer




			def __init__(						self							:		List[Any]       ,       __magic_name__							:		int=None       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		List[str]=None       ,       __magic_name__							:		Any="replace"       ,       __magic_name__							:		Union[str, Any]="<s>"       ,       __magic_name__							:		Tuple="</s>"       ,       __magic_name__							:		Optional[Any]="</s>"       ,       __magic_name__							:		Optional[int]="<s>"       ,       __magic_name__							:		str="<unk>"       ,       __magic_name__							:		Tuple="<pad>"       ,       __magic_name__							:		Union[str, Any]="<mask>"       ,       __magic_name__							:		Union[str, Any]=False       ,       __magic_name__							:		Tuple=True       ,       **__magic_name__							:		str       ,       ):







						"""simple docstring"""







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

						UpperCamelCase         =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
						if pre_tok_state.get("""add_prefix_space"""       ,       __magic_name__						) != add_prefix_space:
									UpperCamelCase         =					getattr(__magic_name__       ,       pre_tok_state.pop("""type"""						)						)
									UpperCamelCase         =					add_prefix_space
									UpperCamelCase         =					pre_tok_class(**__magic_name__						)

						UpperCamelCase         =					add_prefix_space

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

									UpperCamelCase         =					False

									if state.get("""add_prefix_space"""       ,       __magic_name__						) != add_prefix_space:
												UpperCamelCase         =					add_prefix_space
												UpperCamelCase         =					True

									if state.get("""trim_offsets"""       ,       __magic_name__						) != trim_offsets:
												UpperCamelCase         =					trim_offsets
												UpperCamelCase         =					True

									if changes_to_apply:
												UpperCamelCase         =					getattr(__magic_name__       ,       state.pop("""type"""						)						)
												UpperCamelCase         =					component_class(**__magic_name__						)
												setattr(self.backend_tokenizer       ,       __magic_name__       ,       __magic_name__						)




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







						"""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   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					AddedToken(__magic_name__       ,       lstrip=__magic_name__       ,       rstrip=__magic_name__						) if isinstance(__magic_name__       ,       __magic_name__						) else value
						UpperCamelCase         =					value




			def   lowerCamelCase_					(						self							:		List[Any]       ,       *__magic_name__							:		int       ,       **__magic_name__							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					kwargs.get("""is_split_into_words"""       ,       __magic_name__						)
						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(*__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Any       ,       *__magic_name__							:		int       ,       **__magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					kwargs.get("""is_split_into_words"""       ,       __magic_name__						)

						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(*__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str] = None						):







						"""simple docstring"""







						UpperCamelCase         =					self._tokenizer.model.save(__magic_name__       ,       name=__magic_name__						)
						return tuple(__magic_name__						)




			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						UpperCamelCase         =					[self.sep_token_id]
						UpperCamelCase         =					[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							:		List[Any]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						return token_ids_a + [self.eos_token_id]





			def   lowerCamelCase_					(						self							:		str       ,       __magic_name__							:		"Conversation"						):







						"""simple docstring"""







						UpperCamelCase         =					[]
						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(__magic_name__						)

						UpperCamelCase         =					"""  """.join(__magic_name__						)
						UpperCamelCase         =					self.encode(__magic_name__						)
						if len(__magic_name__						) > self.model_max_length:
									UpperCamelCase         =					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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case            =   logging.get_logger(__name__)

__snake_case            =   {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """lilt"""




			def __init__(						self							:		Tuple       ,       __magic_name__							:		Tuple=3_0_5_2_2       ,       __magic_name__							:		List[str]=7_6_8       ,       __magic_name__							:		Optional[Any]=1_2       ,       __magic_name__							:		str=1_2       ,       __magic_name__							:		Tuple=3_0_7_2       ,       __magic_name__							:		List[Any]="gelu"       ,       __magic_name__							:		Dict=0.1       ,       __magic_name__							:		str=0.1       ,       __magic_name__							:		Dict=5_1_2       ,       __magic_name__							:		Any=2       ,       __magic_name__							:		Optional[int]=0.02       ,       __magic_name__							:		Optional[int]=1e-12       ,       __magic_name__							:		Optional[int]=0       ,       __magic_name__							:		List[str]="absolute"       ,       __magic_name__							:		List[Any]=None       ,       __magic_name__							:		str=4       ,       __magic_name__							:		Any=1_0_2_4       ,       **__magic_name__							:		List[str]       ,       ):







						"""simple docstring"""







						super().__init__(pad_token_id=__magic_name__       ,       **__magic_name__						)

						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					type_vocab_size
						UpperCamelCase         =					initializer_range
						UpperCamelCase         =					layer_norm_eps
						UpperCamelCase         =					position_embedding_type
						UpperCamelCase         =					classifier_dropout
						UpperCamelCase         =					channel_shrink_ratio
						UpperCamelCase         =					max_ad_position_embeddings




