





import functools
import operator

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


_snake_case  :   List[str]						   =  logging.get_logger(__name__)

_snake_case  :   List[Any]						   =  {
    '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_      =			"""wavlm"""






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str]=3_2 ,  lowerCAmelCase_			:		Union[str, Any]=7_6_8 ,  lowerCAmelCase_			:		str=1_2 ,  lowerCAmelCase_			:		Optional[int]=1_2 ,  lowerCAmelCase_			:		Optional[Any]=3_0_7_2 ,  lowerCAmelCase_			:		Optional[int]="gelu" ,  lowerCAmelCase_			:		str=0.1 ,  lowerCAmelCase_			:		Dict=0.1 ,  lowerCAmelCase_			:		int=0.1 ,  lowerCAmelCase_			:		Any=0.0 ,  lowerCAmelCase_			:		Dict=0.1 ,  lowerCAmelCase_			:		str=0.1 ,  lowerCAmelCase_			:		str=0.02 ,  lowerCAmelCase_			:		Tuple=1e-5 ,  lowerCAmelCase_			:		str="group" ,  lowerCAmelCase_			:		str="gelu" ,  lowerCAmelCase_			:		List[str]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) ,  lowerCAmelCase_			:		Tuple=(5, 2, 2, 2, 2, 2, 2) ,  lowerCAmelCase_			:		Tuple=(1_0, 3, 3, 3, 3, 2, 2) ,  lowerCAmelCase_			:		List[str]=False ,  lowerCAmelCase_			:		str=1_2_8 ,  lowerCAmelCase_			:		str=1_6 ,  lowerCAmelCase_			:		Tuple=3_2_0 ,  lowerCAmelCase_			:		Dict=8_0_0 ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		List[str]=True ,  lowerCAmelCase_			:		str=0.05 ,  lowerCAmelCase_			:		Union[str, Any]=1_0 ,  lowerCAmelCase_			:		Optional[int]=2 ,  lowerCAmelCase_			:		List[Any]=0.0 ,  lowerCAmelCase_			:		Optional[Any]=1_0 ,  lowerCAmelCase_			:		int=3_2_0 ,  lowerCAmelCase_			:		Dict=2 ,  lowerCAmelCase_			:		Optional[Any]=0.1 ,  lowerCAmelCase_			:		int=1_0_0 ,  lowerCAmelCase_			:		str=2_5_6 ,  lowerCAmelCase_			:		Union[str, Any]=2_5_6 ,  lowerCAmelCase_			:		Union[str, Any]=0.1 ,  lowerCAmelCase_			:		Any="mean" ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		str=2_5_6 ,  lowerCAmelCase_			:		Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) ,  lowerCAmelCase_			:		List[Any]=(5, 3, 3, 1, 1) ,  lowerCAmelCase_			:		Optional[int]=(1, 2, 3, 1, 1) ,  lowerCAmelCase_			:		Optional[Any]=5_1_2 ,  lowerCAmelCase_			:		Dict=8_0 ,  lowerCAmelCase_			:		List[Any]=0 ,  lowerCAmelCase_			:		str=1 ,  lowerCAmelCase_			:		Dict=2 ,  lowerCAmelCase_			:		Dict=False ,  lowerCAmelCase_			:		Optional[int]=3 ,  lowerCAmelCase_			:		List[str]=2 ,  lowerCAmelCase_			:		int=3 ,  lowerCAmelCase_			:		Dict=None ,  **lowerCAmelCase_			:		str ,  )							->						List[str]:
     super().__init__(**lowerCAmelCase_ ,  pad_token_id=lowerCAmelCase_ ,  bos_token_id=lowerCAmelCase_ ,  eos_token_id=lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     feat_extract_norm
     __lowerCAmelCase							     =     feat_extract_activation
     __lowerCAmelCase							     =     list(lowerCAmelCase_					)
     __lowerCAmelCase							     =     list(lowerCAmelCase_					)
     __lowerCAmelCase							     =     list(lowerCAmelCase_					)
     __lowerCAmelCase							     =     conv_bias
     __lowerCAmelCase							     =     num_buckets
     __lowerCAmelCase							     =     max_bucket_distance
     __lowerCAmelCase							     =     num_conv_pos_embeddings
     __lowerCAmelCase							     =     num_conv_pos_embedding_groups
     __lowerCAmelCase							     =     len(self.conv_dim					)
     __lowerCAmelCase							     =     num_hidden_layers
     __lowerCAmelCase							     =     intermediate_size
     __lowerCAmelCase							     =     hidden_act
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     hidden_dropout
     __lowerCAmelCase							     =     attention_dropout
     __lowerCAmelCase							     =     activation_dropout
     __lowerCAmelCase							     =     feat_proj_dropout
     __lowerCAmelCase							     =     final_dropout
     __lowerCAmelCase							     =     layerdrop
     __lowerCAmelCase							     =     layer_norm_eps
     __lowerCAmelCase							     =     initializer_range
     __lowerCAmelCase							     =     num_ctc_classes
     __lowerCAmelCase							     =     vocab_size
     __lowerCAmelCase							     =     do_stable_layer_norm
     __lowerCAmelCase							     =     use_weighted_layer_sum
     __lowerCAmelCase							     =     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
     __lowerCAmelCase							     =     apply_spec_augment
     __lowerCAmelCase							     =     mask_time_prob
     __lowerCAmelCase							     =     mask_time_length
     __lowerCAmelCase							     =     mask_time_min_masks
     __lowerCAmelCase							     =     mask_feature_prob
     __lowerCAmelCase							     =     mask_feature_length

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

     # ctc loss
     __lowerCAmelCase							     =     ctc_loss_reduction
     __lowerCAmelCase							     =     ctc_zero_infinity

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

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

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






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						Tuple:
     return functools.reduce(operator.mul ,  self.conv_stride ,  1					)








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
   # Initialise PyTorch model.
   # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
   # TapasConfig to False.

   # initialize configuration from json file
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WTQ":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     True
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     0.66_4694
      __lowerCAmelCase							     =     0.20_7951
      __lowerCAmelCase							     =     0.12_1194
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     False
      __lowerCAmelCase							     =     0.035_2513

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     False
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     36.4519
      __lowerCAmelCase							     =     0.90_3421
      __lowerCAmelCase							     =     222.088
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     0.76_3141

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "TABFACT":
      __lowerCAmelCase							     =     TapasForSequenceClassification(config=lowerCAmelCase_     )
   elif task == "MLM":
      __lowerCAmelCase							     =     TapasForMaskedLM(config=lowerCAmelCase_     )
   elif task == "INTERMEDIATE_PRETRAINING":
      __lowerCAmelCase							     =     TapasModel(config=lowerCAmelCase_     )
   else:
      raise ValueError(F"""Task {task} not supported."""     )

   print(F"""Building PyTorch model from configuration: {config}"""     )
   # Load weights from tf checkpoint
   load_tf_weights_in_tapas(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

   # Save pytorch-model (weights and configuration)
   print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
   model.save_pretrained(lowerCAmelCase_     )

   # Save tokenizer files
   print(F"""Save tokenizer files to {pytorch_dump_path}"""     )
   __lowerCAmelCase							     =     TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',     model_max_length=512     )
   tokenizer.save_pretrained(lowerCAmelCase_     )

   print('Used relative position embeddings:',     model.config.reset_position_index_per_cell     )


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
     )
     parser.add_argument(
         '--reset_position_index_per_cell',
         default=False,
         action='store_true',
         help='Whether to use relative position embeddings or not. Defaults to True.',
     )
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--tapas_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained TAPAS model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     _snake_case  :   Optional[Any]						   =  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.task,
         args.reset_position_index_per_cell,
         args.tf_checkpoint_path,
         args.tapas_config_file,
         args.pytorch_dump_path,
     )

