



import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__=5   ) ->      Any:
						# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
						assert masked_input.count("""<mask>"""   ) == 1
						__lowercase					=				torch.tensor(tokenizer.encode(lowercase__						,       add_special_tokens=lowercase__   )   ).unsqueeze(0   )  # Batch size 1
						__lowercase					=				model(lowercase__   )[0]  # The last hidden-state is the first element of the output tuple
						__lowercase					=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
						__lowercase					=				logits[0, masked_index, :]
						__lowercase					=				logits.softmax(dim=0   )
						__lowercase	,			__lowercase					=				prob.topk(k=lowercase__						,       dim=0   )
						__lowercase					=				""" """.join(
						    [tokenizer.convert_ids_to_tokens(indices[i].item()   ) for i in range(len(lowercase__   )   )]   )
						__lowercase					=				tokenizer.mask_token
						__lowercase					=				[]
						for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """   )   ):
												__lowercase					=				predicted_token_bpe.replace("""\u2581"""						,       """ """   )
												if " {0}".format(lowercase__   ) in masked_input:
																		topk_filled_outputs.append(
																		    (
																		        masked_input.replace(""" {0}""".format(lowercase__   )						,       lowercase__   ),
																		        values[index].item(),
																		        predicted_token,
																		    )   )
												else:
																		topk_filled_outputs.append(
																		    (
																		        masked_input.replace(lowercase__						,       lowercase__   ),
																		        values[index].item(),
																		        predicted_token,
																		    )   )
						return topk_filled_outputs


UpperCamelCase__ =						CamembertTokenizer.from_pretrained("camembert-base")
UpperCamelCase__ =						CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

UpperCamelCase__ =						"Le camembert est <mask> :)"







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










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


UpperCamelCase__ =						logging.get_logger(__name__)

UpperCamelCase__ =						{
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Union[str, Any]      =			"""yolos"""





			def __init__(				self	:       Optional[int]			,     lowercase	:       Any=768			,     lowercase	:       Tuple=12			,     lowercase	:       Tuple=12			,     lowercase	:       str=3_072			,     lowercase	:       Optional[Any]="gelu"			,     lowercase	:       Union[str, Any]=0.0			,     lowercase	:       Dict=0.0			,     lowercase	:       Optional[int]=0.02			,     lowercase	:       Optional[Any]=1E-1_2			,     lowercase	:       Tuple=[512, 864]			,     lowercase	:       Optional[int]=16			,     lowercase	:       Dict=3			,     lowercase	:       Optional[Any]=True			,     lowercase	:       Optional[int]=100			,     lowercase	:       Optional[int]=True			,     lowercase	:       Any=False			,     lowercase	:       Any=1			,     lowercase	:       Any=5			,     lowercase	:       List[str]=2			,     lowercase	:       Union[str, Any]=5			,     lowercase	:       str=2			,     lowercase	:       Tuple=0.1			,     **lowercase	:       str			,     )						->					Optional[Any]:
									"""simple docstring"""







									super().__init__(**lowercase   )

									__lowercase					=				hidden_size
									__lowercase					=				num_hidden_layers
									__lowercase					=				num_attention_heads
									__lowercase					=				intermediate_size
									__lowercase					=				hidden_act
									__lowercase					=				hidden_dropout_prob
									__lowercase					=				attention_probs_dropout_prob
									__lowercase					=				initializer_range
									__lowercase					=				layer_norm_eps
									__lowercase					=				image_size
									__lowercase					=				patch_size
									__lowercase					=				num_channels
									__lowercase					=				qkv_bias
									__lowercase					=				num_detection_tokens
									__lowercase					=				use_mid_position_embeddings
									__lowercase					=				auxiliary_loss
									# Hungarian matcher
									__lowercase					=				class_cost
									__lowercase					=				bbox_cost
									__lowercase					=				giou_cost
									# Loss coefficients
									__lowercase					=				bbox_loss_coefficient
									__lowercase					=				giou_loss_coefficient
									__lowercase					=				eos_coefficient





class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Dict      =			version.parse("""1.11"""			)





			@property
			def 		snake_case__			(				self	:       List[str]   )						->					Mapping[str, Mapping[int, str]]:
									"""simple docstring"""







									return OrderedDict(
									    [
									        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
									    ]   )





			@property
			def 		snake_case__			(				self	:       int   )						->					float:
									"""simple docstring"""







									return 1E-4







			@property
			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									return 12





