


'''simple docstring'''





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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


_lowercase       :      Union[str, Any]	=	logging.get_logger(__name__)

_lowercase       :      Union[str, Any]	=	{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase       :      List[Any]	=	{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

_lowercase       :      Union[str, Any]	=	{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= VOCAB_FILES_NAMES
				lowerCAmelCase 		= PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase 		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase 		= ['input_ids', 'attention_mask']
				lowerCAmelCase 		= GPTaTokenizer

				def __init__(						self				,   a__=None				,   a__=None				,   a__=None				,   a__="<|endoftext|>"				,   a__="<|endoftext|>"				,   a__="<|endoftext|>"				,   a__=False				,   **a__				,   )	->				List[str]:
					super().__init__(
					    a__				,   a__				,   tokenizer_file=a__				,   unk_token=a__				,   bos_token=a__				,   eos_token=a__				,   add_prefix_space=a__				,   **a__				,   )

					A								=				kwargs.pop("""add_bos_token"""				,   a__   )

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

					A								=				add_prefix_space

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				BatchEncoding:
					A								=				kwargs.get("""is_split_into_words"""				,   a__   )
					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(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				BatchEncoding:
					A								=				kwargs.get("""is_split_into_words"""				,   a__   )

					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(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				Tuple[str]:
					A								=				self._tokenizer.model.save(a__				,   name=a__   )
					return tuple(a__   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[int]:
					A								=				[]
					for is_user, text in conversation.iter_texts():
						input_ids.extend(self.encode(a__				,   add_special_tokens=a__   ) + [self.eos_token_id]   )

					if len(a__   ) > self.model_max_length:
						A								=				input_ids[-self.model_max_length :]
					return input_ids







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







	assert masked_input.count("""<mask>"""						) == 1
	A								=				torch.tensor(tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
	A								=				model(UpperCamelCase__						)[0]  # The last hidden-state is the first element of the output tuple
	A								=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	A								=				logits[0, masked_index, :]
	A								=				logits.softmax(dim=0						)
	A					,			A								=				prob.topk(k=UpperCamelCase__							,					dim=0						)
	A								=				""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
	A								=				tokenizer.mask_token
	A								=				[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """						)						):
		A								=				predicted_token_bpe.replace("""\u2581"""							,					""" """						)
		if " {0}".format(UpperCamelCase__						) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(UpperCamelCase__						)							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(UpperCamelCase__							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
	return topk_filled_outputs


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



