






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  Any    ,			UpperCAmelCase							:  Optional[int]    ,			UpperCAmelCase							:  Optional[int]=5       ):
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count("<mask>"       ) == 1
    lowerCAmelCase__   :List[str]      							=				torch.tensor(tokenizer.encode(UpperCAmelCase    ,			add_special_tokens=UpperCAmelCase       )       ).unsqueeze(0       )  # Batch size 1
    lowerCAmelCase__   :List[str]      							=				model(UpperCAmelCase       )[0]  # The last hidden-state is the first element of the output tuple
    lowerCAmelCase__   :Optional[Any]      							=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    lowerCAmelCase__   :Dict      							=				logits[0, masked_index, :]
    lowerCAmelCase__   :str      							=				logits.softmax(dim=0       )
    lowerCAmelCase__							,lowerCAmelCase__   :int      							=				prob.topk(k=UpperCAmelCase    ,			dim=0       )
    lowerCAmelCase__   :int      							=				" ".join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(UpperCAmelCase       )       )]       )
    lowerCAmelCase__   :Any      							=				tokenizer.mask_token
    lowerCAmelCase__   :Optional[int]      							=				[]
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
        lowerCAmelCase__   :List[Any]      							=				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    :   Any        =			CamembertTokenizer.from_pretrained("""camembert-base""")
_a    :   Optional[Any]        =			CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

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











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


_a    :   Dict        =			logging.get_logger(__name__)

_a    :   Optional[Any]        =			{
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    '''swin2sr'''

      A          =    {
          '''hidden_size''': '''embed_dim''',
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
      }
      def __init__(   self    ,	_lowerCAmelCase=64    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=180    ,	_lowerCAmelCase=[6, 6, 6, 6, 6, 6]    ,	_lowerCAmelCase=[6, 6, 6, 6, 6, 6]    ,	_lowerCAmelCase=8    ,	_lowerCAmelCase=2.0    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=0.0    ,	_lowerCAmelCase=0.0    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase="gelu"    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=0.02    ,	_lowerCAmelCase=1e-5    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=1.0    ,	_lowerCAmelCase="1conv"    ,	_lowerCAmelCase="pixelshuffle"    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__(**_lowerCAmelCase		)

          lowerCAmelCase__   :Union[str, Any]      							=				image_size
          lowerCAmelCase__   :List[Any]      							=				patch_size
          lowerCAmelCase__   :Any      							=				num_channels
          lowerCAmelCase__   :Union[str, Any]      							=				embed_dim
          lowerCAmelCase__   :Optional[int]      							=				depths
          lowerCAmelCase__   :int      							=				len(_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				num_heads
          lowerCAmelCase__   :str      							=				window_size
          lowerCAmelCase__   :List[str]      							=				mlp_ratio
          lowerCAmelCase__   :List[Any]      							=				qkv_bias
          lowerCAmelCase__   :Union[str, Any]      							=				hidden_dropout_prob
          lowerCAmelCase__   :Optional[int]      							=				attention_probs_dropout_prob
          lowerCAmelCase__   :Dict      							=				drop_path_rate
          lowerCAmelCase__   :Tuple      							=				hidden_act
          lowerCAmelCase__   :Dict      							=				use_absolute_embeddings
          lowerCAmelCase__   :Tuple      							=				layer_norm_eps
          lowerCAmelCase__   :Dict      							=				initializer_range
          lowerCAmelCase__   :Optional[int]      							=				upscale
          lowerCAmelCase__   :Optional[Any]      							=				img_range
          lowerCAmelCase__   :List[str]      							=				resi_connection
          lowerCAmelCase__   :Union[str, Any]      							=				upsampler



