

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__	   = logging.get_logger(__name__)


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

lowerCAmelCase__	   = {
    """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__	   = {"""facebook/blenderbot-3B""": 1_2_8}

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       VOCAB_FILES_NAMES
			__lowerCamelCase        =       PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase        =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase        =       ['input_ids', 'attention_mask']
			__lowerCamelCase        =       BlenderbotTokenizer


			def __init__(    self      ,      lowercase=None      ,      lowercase=None      ,      lowercase=None      ,      lowercase="replace"      ,      lowercase="<s>"      ,      lowercase="</s>"      ,      lowercase="</s>"      ,      lowercase="<s>"      ,      lowercase="<unk>"      ,      lowercase="<pad>"      ,      lowercase="<mask>"      ,      lowercase=False      ,      lowercase=True      ,      **lowercase      ,      )  ->						Optional[int]:
							'''simple docstring'''






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

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

							A__        =			add_prefix_space

							A__        =			"post_processor"
							A__        =			getattr(self.backend_tokenizer      ,      lowercase      ,      lowercase       )
							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"      ,      lowercase       ) != add_prefix_space:
															A__        =			add_prefix_space
															A__        =			True

											if state.get("trim_offsets"      ,      lowercase       ) != trim_offsets:
															A__        =			trim_offsets
															A__        =			True

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


			@property
			# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
			def      UpperCamelCase		(    self       )  ->						str:
							'''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      UpperCamelCase		(    self      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			AddedToken(lowercase      ,      lstrip=lowercase      ,      rstrip=lowercase       ) if isinstance(lowercase      ,      lowercase       ) else value
							A__        =			value


			def      UpperCamelCase		(    self      ,      *lowercase      ,      **lowercase       )  ->						BatchEncoding:
							'''simple docstring'''






							A__        =			kwargs.get("is_split_into_words"      ,      lowercase       )
							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(*lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      *lowercase      ,      **lowercase       )  ->						BatchEncoding:
							'''simple docstring'''






							A__        =			kwargs.get("is_split_into_words"      ,      lowercase       )

							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(*lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						Tuple[str]:
							'''simple docstring'''






							A__        =			self._tokenizer.model.save(lowercase      ,      name=lowercase       )
							return tuple(lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						List[int]:
							'''simple docstring'''






							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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						Optional[Any]:
							'''simple docstring'''






							return token_ids_a + [self.eos_token_id]


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						List[int]:
							'''simple docstring'''






							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(lowercase       )

							A__        =			"  ".join(lowercase       )
							A__        =			self.encode(lowercase       )
							if len(lowercase       ) > 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




from typing import Union

import fire
import torch
from tqdm import tqdm



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str = "cpu"	,       SCREAMING_SNAKE_CASE_:							Union[str, None] = None      )			->      None:




				'''simple docstring'''





				A__        =			torch.load(SCREAMING_SNAKE_CASE_	,       map_location=SCREAMING_SNAKE_CASE_      )
				for k, v in tqdm(state_dict.items()      ):
								if not isinstance(SCREAMING_SNAKE_CASE_	,       torch.Tensor      ):
												raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
								A__        =			v.half()
				if save_path is None:  # overwrite src_path
								A__        =			src_path
				torch.save(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	fire.Fire(convert)

