



'''simple docstring'''





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def   UpperCAmelCase			(  a_	,						a_	,						a_	,						a_=5						)					->    Any:



   """simple docstring"""






   assert masked_input.count("""<mask>"""						) == 1
   A_   :						Dict		= torch.tensor(tokenizer.encode(a_	,						add_special_tokens=a_						)						).unsqueeze(0						)  # Batch size 1
   A_   :						Tuple		= model(a_						)[0]  # The last hidden-state is the first element of the output tuple
   A_   :						Optional[int]		= (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
   A_   :						Tuple		= logits[0, masked_index, :]
   A_   :						Any		= logits.softmax(dim=0						)
   A_		,   A_   :						Union[str, 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_   :						Tuple		= tokenizer.mask_token
   A_   :						int		= []
   for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """						)						):
      A_   :						int		= 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


UpperCamelCase__  :			List[str]          =					CamembertTokenizer.from_pretrained('camembert-base')
UpperCamelCase__  :			List[str]          =					CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

UpperCamelCase__  :			List[str]          =					"""Le camembert est <mask> :)"""







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

import functools
import operator

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


__a  :     str       =  logging.get_logger(__name__)

__a  :     int       =  {
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Any			 =						'''wavlm'''





			def __init__(   self      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-5      ,   lowerCAmelCase__="group"      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)      ,   lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)      ,   lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2)      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=1_28      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=8_00      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=0.05      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=1_00      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__="mean"      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 15_00)      ,   lowerCAmelCase__=(5, 3, 3, 1, 1)      ,   lowerCAmelCase__=(1, 2, 3, 1, 1)      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=80      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Tuple:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__      ,   pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__						)
							__lowercase 			=							hidden_size
							__lowercase 			=							feat_extract_norm
							__lowercase 			=							feat_extract_activation
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							conv_bias
							__lowercase 			=							num_buckets
							__lowercase 			=							max_bucket_distance
							__lowercase 			=							num_conv_pos_embeddings
							__lowercase 			=							num_conv_pos_embedding_groups
							__lowercase 			=							len(self.conv_dim						)
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_dropout
							__lowercase 			=							attention_dropout
							__lowercase 			=							activation_dropout
							__lowercase 			=							feat_proj_dropout
							__lowercase 			=							final_dropout
							__lowercase 			=							layerdrop
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							initializer_range
							__lowercase 			=							num_ctc_classes
							__lowercase 			=							vocab_size
							__lowercase 			=							do_stable_layer_norm
							__lowercase 			=							use_weighted_layer_sum
							__lowercase 			=							classifier_proj_size

							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
							__lowercase 			=							apply_spec_augment
							__lowercase 			=							mask_time_prob
							__lowercase 			=							mask_time_length
							__lowercase 			=							mask_time_min_masks
							__lowercase 			=							mask_feature_prob
							__lowercase 			=							mask_feature_length

							# parameters for pretraining with codevector quantized representations
							__lowercase 			=							num_codevectors_per_group
							__lowercase 			=							num_codevector_groups
							__lowercase 			=							contrastive_logits_temperature
							__lowercase 			=							num_negatives
							__lowercase 			=							codevector_dim
							__lowercase 			=							proj_codevector_dim
							__lowercase 			=							diversity_loss_weight

							# ctc loss
							__lowercase 			=							ctc_loss_reduction
							__lowercase 			=							ctc_zero_infinity

							# adapter
							__lowercase 			=							add_adapter
							__lowercase 			=							adapter_kernel_size
							__lowercase 			=							adapter_stride
							__lowercase 			=							num_adapter_layers
							__lowercase 			=							output_hidden_size or hidden_size

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							__lowercase 			=							classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							xvector_output_dim





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return functools.reduce(operator.mul      ,   self.conv_stride      ,   1						)