


"""simple docstring"""







import functools
import operator

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


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Union[str, Any]     =	"""unispeech-sat"""






    def __init__( self    :   Dict    ,			__UpperCAmelCase    :   List[str]=32    ,			__UpperCAmelCase    :   Dict=768    ,			__UpperCAmelCase    :   Union[str, Any]=12    ,			__UpperCAmelCase    :   List[str]=12    ,			__UpperCAmelCase    :   str=3072    ,			__UpperCAmelCase    :   str="gelu"    ,			__UpperCAmelCase    :   int=0.1    ,			__UpperCAmelCase    :   int=0.1    ,			__UpperCAmelCase    :   Union[str, Any]=0.1    ,			__UpperCAmelCase    :   List[str]=0.0    ,			__UpperCAmelCase    :   List[str]=0.0    ,			__UpperCAmelCase    :   str=0.1    ,			__UpperCAmelCase    :   int=0.1    ,			__UpperCAmelCase    :   List[Any]=0.02    ,			__UpperCAmelCase    :   Tuple=1e-5    ,			__UpperCAmelCase    :   Any="group"    ,			__UpperCAmelCase    :   str="gelu"    ,			__UpperCAmelCase    :   Any=(512, 512, 512, 512, 512, 512, 512)    ,			__UpperCAmelCase    :   Dict=(5, 2, 2, 2, 2, 2, 2)    ,			__UpperCAmelCase    :   int=(10, 3, 3, 3, 3, 2, 2)    ,			__UpperCAmelCase    :   Tuple=False    ,			__UpperCAmelCase    :   List[Any]=128    ,			__UpperCAmelCase    :   str=16    ,			__UpperCAmelCase    :   Any=False    ,			__UpperCAmelCase    :   Union[str, Any]=True    ,			__UpperCAmelCase    :   Optional[Any]=0.05    ,			__UpperCAmelCase    :   Optional[Any]=10    ,			__UpperCAmelCase    :   str=2    ,			__UpperCAmelCase    :   List[str]=0.0    ,			__UpperCAmelCase    :   Optional[int]=10    ,			__UpperCAmelCase    :   int=0    ,			__UpperCAmelCase    :   Tuple=320    ,			__UpperCAmelCase    :   Optional[Any]=2    ,			__UpperCAmelCase    :   List[Any]=0.1    ,			__UpperCAmelCase    :   Dict=100    ,			__UpperCAmelCase    :   List[str]=256    ,			__UpperCAmelCase    :   str=256    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   Dict="mean"    ,			__UpperCAmelCase    :   Any=False    ,			__UpperCAmelCase    :   Tuple=False    ,			__UpperCAmelCase    :   Optional[int]=256    ,			__UpperCAmelCase    :   Union[str, Any]=(512, 512, 512, 512, 1500)    ,			__UpperCAmelCase    :   Dict=(5, 3, 3, 1, 1)    ,			__UpperCAmelCase    :   Optional[Any]=(1, 2, 3, 1, 1)    ,			__UpperCAmelCase    :   Union[str, Any]=512    ,			__UpperCAmelCase    :   Union[str, Any]=0    ,			__UpperCAmelCase    :   List[Any]=1    ,			__UpperCAmelCase    :   str=2    ,			__UpperCAmelCase    :   str=504    ,			**__UpperCAmelCase    :   Dict    ,			):
      super().__init__(**__UpperCAmelCase    ,			pad_token_id=__UpperCAmelCase    ,			bos_token_id=__UpperCAmelCase    ,			eos_token_id=__UpperCAmelCase)
      a						:		Optional[int]											=      hidden_size
      a						:		List[str]											=      feat_extract_norm
      a						:		str											=      feat_extract_activation
      a						:		Optional[Any]											=      list(__UpperCAmelCase)
      a						:		Dict											=      list(__UpperCAmelCase)
      a						:		str											=      list(__UpperCAmelCase)
      a						:		Optional[int]											=      conv_bias
      a						:		List[str]											=      num_conv_pos_embeddings
      a						:		List[str]											=      num_conv_pos_embedding_groups
      a						:		int											=      len(self.conv_dim)
      a						:		Union[str, Any]											=      num_hidden_layers
      a						:		str											=      intermediate_size
      a						:		Union[str, Any]											=      hidden_act
      a						:		List[str]											=      num_attention_heads
      a						:		Optional[Any]											=      hidden_dropout
      a						:		Any											=      attention_dropout
      a						:		Optional[Any]											=      activation_dropout
      a						:		Optional[Any]											=      feat_proj_dropout
      a						:		Dict											=      final_dropout
      a						:		Dict											=      layerdrop
      a						:		int											=      layer_norm_eps
      a						:		Any											=      initializer_range
      a						:		Union[str, Any]											=      vocab_size
      a						:		Any											=      num_clusters
      a						:		int											=      do_stable_layer_norm
      a						:		Dict											=      use_weighted_layer_sum

      if (
          (len(self.conv_stride) != self.num_feat_extract_layers)
          or (len(self.conv_kernel) != self.num_feat_extract_layers)
          or (len(self.conv_dim) != self.num_feat_extract_layers)
      ):
        raise ValueError(
            "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
            " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
            f''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
            f''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      a						:		str											=      apply_spec_augment
      a						:		List[str]											=      mask_time_prob
      a						:		Any											=      mask_time_length
      a						:		Tuple											=      mask_time_min_masks
      a						:		Any											=      mask_feature_prob
      a						:		List[str]											=      mask_feature_length
      a						:		Dict											=      mask_feature_min_masks

      # parameters for pretraining with codevector quantized representations
      a						:		int											=      num_codevectors_per_group
      a						:		int											=      num_codevector_groups
      a						:		str											=      contrastive_logits_temperature
      a						:		Tuple											=      feat_quantizer_dropout
      a						:		int											=      num_negatives
      a						:		Any											=      codevector_dim
      a						:		Dict											=      proj_codevector_dim
      a						:		Union[str, Any]											=      diversity_loss_weight

      # ctc loss
      a						:		Tuple											=      ctc_loss_reduction
      a						:		List[Any]											=      ctc_zero_infinity

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      a						:		Optional[Any]											=      classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      a						:		int											=      list(__UpperCAmelCase)
      a						:		List[str]											=      list(__UpperCAmelCase)
      a						:		List[str]											=      list(__UpperCAmelCase)
      a						:		int											=      xvector_output_dim






    @property
    def      __snake_case							( self    :   List[Any]):
      return functools.reduce(operator.mul    ,			self.conv_stride    ,			1)










"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







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


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

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







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






