



"""simple docstring"""




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase=5 )   ->	Any:






      '''simple docstring'''


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


_UpperCamelCase: Union[str, Any]										=     CamembertTokenizer.from_pretrained('camembert-base')
_UpperCamelCase: List[str]										=     CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_UpperCamelCase: List[Any]										=     'Le camembert est <mask> :)'





print(fill_mask(masked_input, model, tokenizer, topk=3))





'''simple docstring'''

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Any   				=					'conditional_detr'
							lowerCAmelCase       :    List[str]   				=					['past_key_values']
							lowerCAmelCase       :    Optional[int]   				=					{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Union[str, Any]=None	,_UpperCAmelCase					:       List[Any]=3	,_UpperCAmelCase					:       List[Any]=300	,_UpperCAmelCase					:       Dict=6	,_UpperCAmelCase					:       List[str]=2048	,_UpperCAmelCase					:       Optional[int]=8	,_UpperCAmelCase					:       List[Any]=6	,_UpperCAmelCase					:       Optional[int]=2048	,_UpperCAmelCase					:       Dict=8	,_UpperCAmelCase					:       int=0.0	,_UpperCAmelCase					:       Optional[Any]=0.0	,_UpperCAmelCase					:       Optional[Any]=True	,_UpperCAmelCase					:       str="relu"	,_UpperCAmelCase					:       Tuple=256	,_UpperCAmelCase					:       Optional[int]=0.1	,_UpperCAmelCase					:       str=0.0	,_UpperCAmelCase					:       Optional[int]=0.0	,_UpperCAmelCase					:       Union[str, Any]=0.02	,_UpperCAmelCase					:       List[str]=1.0	,_UpperCAmelCase					:       Any=False	,_UpperCAmelCase					:       int="sine"	,_UpperCAmelCase					:       List[str]="resnet50"	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       str=False	,_UpperCAmelCase					:       str=2	,_UpperCAmelCase					:       int=5	,_UpperCAmelCase					:       Optional[int]=2	,_UpperCAmelCase					:       str=1	,_UpperCAmelCase					:       Union[str, Any]=1	,_UpperCAmelCase					:       List[str]=2	,_UpperCAmelCase					:       Union[str, Any]=5	,_UpperCAmelCase					:       List[Any]=2	,_UpperCAmelCase					:       Optional[int]=0.25	,**_UpperCAmelCase					:       Tuple	,):
									if backbone_config is not None and use_timm_backbone:
											raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'		)

									if not use_timm_backbone:
											if backbone_config is None:
													logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
													_a     :      Optional[Any]        =							CONFIG_MAPPING['resnet'](out_features=['stage4']		)
											elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													_a     :      str        =							backbone_config.get('model_type'		)
													_a     :      Union[str, Any]        =							CONFIG_MAPPING[backbone_model_type]
													_a     :      List[Any]        =							config_class.from_dict(_UpperCAmelCase		)

									_a     :      Tuple        =							use_timm_backbone
									_a     :      Union[str, Any]        =							backbone_config
									_a     :      List[Any]        =							num_channels
									_a     :      Union[str, Any]        =							num_queries
									_a     :      Optional[Any]        =							d_model
									_a     :      Tuple        =							encoder_ffn_dim
									_a     :      Dict        =							encoder_layers
									_a     :      List[str]        =							encoder_attention_heads
									_a     :      Union[str, Any]        =							decoder_ffn_dim
									_a     :      Optional[int]        =							decoder_layers
									_a     :      int        =							decoder_attention_heads
									_a     :      Optional[int]        =							dropout
									_a     :      Tuple        =							attention_dropout
									_a     :      List[Any]        =							activation_dropout
									_a     :      str        =							activation_function
									_a     :      Optional[Any]        =							init_std
									_a     :      Union[str, Any]        =							init_xavier_std
									_a     :      List[Any]        =							encoder_layerdrop
									_a     :      List[Any]        =							decoder_layerdrop
									_a     :      Dict        =							encoder_layers
									_a     :      List[Any]        =							auxiliary_loss
									_a     :      Optional[int]        =							position_embedding_type
									_a     :      List[Any]        =							backbone
									_a     :      Optional[int]        =							use_pretrained_backbone
									_a     :      Optional[int]        =							dilation
									# Hungarian matcher
									_a     :      Tuple        =							class_cost
									_a     :      str        =							bbox_cost
									_a     :      Any        =							giou_cost
									# Loss coefficients
									_a     :      Tuple        =							mask_loss_coefficient
									_a     :      Dict        =							dice_loss_coefficient
									_a     :      Tuple        =							cls_loss_coefficient
									_a     :      Any        =							bbox_loss_coefficient
									_a     :      Dict        =							giou_loss_coefficient
									_a     :      Union[str, Any]        =							focal_alpha
									super().__init__(is_encoder_decoder=_UpperCAmelCase	,**_UpperCAmelCase		)







							@property
							def 							__lowercase			(							self					:       Dict		):
									return self.encoder_attention_heads







							@property
							def 							__lowercase			(							self					:       str		):
									return self.d_model







							def 							__lowercase			(							self					:       int		):
									_a     :      List[str]        =							copy.deepcopy(self.__dict__		)
									if self.backbone_config is not None:
											_a     :      Dict        =							self.backbone_config.to_dict()
									_a     :      Union[str, Any]        =							self.__class__.model_type
									return output

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					version.parse('1.11'   )







							@property
							def 							__lowercase			(							self					:       Dict		):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									        ('pixel_mask', {0: 'batch'}),
									    ]		)







							@property
							def 							__lowercase			(							self					:       Any		):
									return 1E-5







							@property
							def 							__lowercase			(							self					:       List[Any]		):
									return 12




