


'''simple docstring'''





from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class       _UpperCamelCase    (					__a				):

				"""simple docstring"""







				lowerCAmelCase 		= ['''image_processor''', '''tokenizer''']
				lowerCAmelCase 		= '''BlipImageProcessor'''
				lowerCAmelCase 		= '''AutoTokenizer'''

				def __init__(						self				,   a__				,   a__   )	->				Union[str, Any]:
					A								=				False
					super().__init__(snake_case__				,   snake_case__   )
					A								=				self.image_processor

				def __call__(						self				,   a__ = None				,   a__ = None				,   a__ = True				,   a__ = False				,   a__ = None				,   a__ = None				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = False				,   a__ = True				,   a__ = None				,   **a__				,   )	->				Tuple:
					if images is None and text is None:
						raise ValueError("""You have to specify either images or text."""   )

					# Get only text
					if images is None:
						A								=				self.tokenizer
						A								=				self.tokenizer(
						    text=snake_case__				,   add_special_tokens=snake_case__				,   padding=snake_case__				,   truncation=snake_case__				,   max_length=snake_case__				,   stride=snake_case__				,   pad_to_multiple_of=snake_case__				,   return_attention_mask=snake_case__				,   return_overflowing_tokens=snake_case__				,   return_special_tokens_mask=snake_case__				,   return_offsets_mapping=snake_case__				,   return_token_type_ids=snake_case__				,   return_length=snake_case__				,   verbose=snake_case__				,   return_tensors=snake_case__				,   **snake_case__				,   )
						return text_encoding

					# add pixel_values
					A								=				self.image_processor(snake_case__				,   return_tensors=snake_case__   )

					if text is not None:
						A								=				self.tokenizer(
						    text=snake_case__				,   add_special_tokens=snake_case__				,   padding=snake_case__				,   truncation=snake_case__				,   max_length=snake_case__				,   stride=snake_case__				,   pad_to_multiple_of=snake_case__				,   return_attention_mask=snake_case__				,   return_overflowing_tokens=snake_case__				,   return_special_tokens_mask=snake_case__				,   return_offsets_mapping=snake_case__				,   return_token_type_ids=snake_case__				,   return_length=snake_case__				,   verbose=snake_case__				,   return_tensors=snake_case__				,   **snake_case__				,   )
					else:
						A								=				None

					if text_encoding is not None:
						encoding_image_processor.update(snake_case__   )

					return encoding_image_processor

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Dict:
					return self.tokenizer.batch_decode(*snake_case__				,   **snake_case__   )

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				Union[str, Any]:
					return self.tokenizer.decode(*snake_case__				,   **snake_case__   )

				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.tokenizer.model_input_names
					A								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







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))



