
import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def    lowerCamelCase__				(				__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  Optional[int]=5				):




			"""simple docstring"""


			assert masked_input.count("<mask>"				) == 1
			lowerCAmelCase_						=	torch.tensor(tokenizer.encode(__UpperCAmelCase    ,							add_special_tokens=__UpperCAmelCase				)				).unsqueeze(0				)  # Batch size 1
			lowerCAmelCase_						=	model(__UpperCAmelCase				)[0]  # The last hidden-state is the first element of the output tuple
			lowerCAmelCase_						=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
			lowerCAmelCase_						=	logits[0, masked_index, :]
			lowerCAmelCase_						=	logits.softmax(dim=0				)
			lowerCAmelCase_						=	prob.topk(k=__UpperCAmelCase    ,							dim=0				)
			lowerCAmelCase_						=	''' '''.join(
			    [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCAmelCase				)				)]				)
			lowerCAmelCase_						=	tokenizer.mask_token
			lowerCAmelCase_						=	[]
			for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "				)				):
						lowerCAmelCase_						=	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


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

_A   =     "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))




"""simple docstring"""






from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A			   = logging.get_logger(__name__)

__A			   = {
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[Any]						    = "efficientformer"






    def __init__(   self    ,			_UpperCAmelCase = [3, 2, 6, 4]    ,			_UpperCAmelCase = [48, 96, 224, 448]    ,			_UpperCAmelCase = [True, True, True, True]    ,			_UpperCAmelCase = 448    ,			_UpperCAmelCase = 32    ,			_UpperCAmelCase = 4    ,			_UpperCAmelCase = 7    ,			_UpperCAmelCase = 5    ,			_UpperCAmelCase = 8    ,			_UpperCAmelCase = 4    ,			_UpperCAmelCase = 0.0    ,			_UpperCAmelCase = 16    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 2    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = 0.0    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = 1e-5    ,			_UpperCAmelCase = "gelu"    ,			_UpperCAmelCase = 0.02    ,			_UpperCAmelCase = 1e-1_2    ,			_UpperCAmelCase = 224    ,			_UpperCAmelCase = 1e-0_5    ,			**_UpperCAmelCase    ,			):
       super().__init__(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      hidden_act
       lowercase__:   Union[str, Any]					=      hidden_dropout_prob
       lowercase__:   Union[str, Any]					=      hidden_sizes
       lowercase__:   Any					=      num_hidden_layers
       lowercase__:   int					=      num_attention_heads
       lowercase__:   Dict					=      initializer_range
       lowercase__:   Optional[int]					=      layer_norm_eps
       lowercase__:   int					=      patch_size
       lowercase__:   int					=      num_channels
       lowercase__:   str					=      depths
       lowercase__:   List[str]					=      mlp_expansion_ratio
       lowercase__:   List[str]					=      downsamples
       lowercase__:   List[str]					=      dim
       lowercase__:   Optional[Any]					=      key_dim
       lowercase__:   Union[str, Any]					=      attention_ratio
       lowercase__:   Any					=      resolution
       lowercase__:   Any					=      pool_size
       lowercase__:   List[Any]					=      downsample_patch_size
       lowercase__:   Optional[int]					=      downsample_stride
       lowercase__:   Union[str, Any]					=      downsample_pad
       lowercase__:   List[Any]					=      drop_path_rate
       lowercase__:   Optional[Any]					=      num_metaad_blocks
       lowercase__:   Any					=      distillation
       lowercase__:   Optional[int]					=      use_layer_scale
       lowercase__:   List[str]					=      layer_scale_init_value
       lowercase__:   Dict					=      image_size
       lowercase__:   List[str]					=      batch_norm_eps
