
'''simple docstring'''







import math

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


__lowerCAmelCase         =    logging.get_logger(__name__)

__lowerCAmelCase         =    {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Tuple   =					'''data2vec-audio'''


  def __init__(		self      :     int							,_a      :     int=32							,_a      :     str=768							,_a      :     List[str]=12							,_a      :     List[str]=12							,_a      :     Dict=3072							,_a      :     str="gelu"							,_a      :     List[str]=0.1							,_a      :     Union[str, Any]=0.1							,_a      :     Dict=0.1							,_a      :     Optional[int]=0.0							,_a      :     Optional[Any]=0.1							,_a      :     Optional[Any]=0.1							,_a      :     int=0.02							,_a      :     Union[str, Any]=1E-5							,_a      :     str="gelu"							,_a      :     List[str]=(512, 512, 512, 512, 512, 512, 512)							,_a      :     int=(5, 2, 2, 2, 2, 2, 2)							,_a      :     Optional[Any]=(10, 3, 3, 3, 3, 2, 2)							,_a      :     List[Any]=False							,_a      :     str=16							,_a      :     Optional[Any]=19							,_a      :     str=5							,_a      :     Dict=0.05							,_a      :     Optional[Any]=10							,_a      :     Union[str, Any]=2							,_a      :     str=0.0							,_a      :     str=10							,_a      :     List[str]=0							,_a      :     str="sum"							,_a      :     Union[str, Any]=False							,_a      :     Tuple=False							,_a      :     Any=256							,_a      :     Tuple=(512, 512, 512, 512, 1500)							,_a      :     Tuple=(5, 3, 3, 1, 1)							,_a      :     int=(1, 2, 3, 1, 1)							,_a      :     Optional[int]=512							,_a      :     Optional[int]=0							,_a      :     List[Any]=1							,_a      :     Tuple=2							,_a      :     int=False							,_a      :     int=3							,_a      :     Optional[Any]=2							,_a      :     str=3							,_a      :     Union[str, Any]=None							,**_a      :     int							,):


     '''simple docstring'''






     super().__init__(**_a							,pad_token_id=_a							,bos_token_id=_a							,eos_token_id=_a       )
     _a				:	str			=   hidden_size
     _a				:	Dict			=   feat_extract_activation
     _a				:	str			=   list(_a       )
     _a				:	Any			=   list(_a       )
     _a				:	Any			=   list(_a       )
     _a				:	Union[str, Any]			=   conv_bias
     _a				:	List[str]			=   num_conv_pos_embeddings
     _a				:	Any			=   num_conv_pos_embedding_groups
     _a				:	Dict			=   conv_pos_kernel_size
     _a				:	Optional[Any]			=   len(self.conv_dim       )
     _a				:	Optional[int]			=   num_hidden_layers
     _a				:	Union[str, Any]			=   intermediate_size
     _a				:	Union[str, Any]			=   hidden_act
     _a				:	Dict			=   num_attention_heads
     _a				:	List[Any]			=   hidden_dropout
     _a				:	Optional[int]			=   attention_dropout
     _a				:	Optional[Any]			=   activation_dropout
     _a				:	Any			=   feat_proj_dropout
     _a				:	List[str]			=   final_dropout
     _a				:	List[Any]			=   layerdrop
     _a				:	List[str]			=   layer_norm_eps
     _a				:	Any			=   initializer_range
     _a				:	List[str]			=   vocab_size
     _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				:	Any			=   mask_time_prob
     _a				:	Any			=   mask_time_length
     _a				:	Optional[int]			=   mask_time_min_masks
     _a				:	List[Any]			=   mask_feature_prob
     _a				:	Optional[Any]			=   mask_feature_length
     _a				:	Union[str, Any]			=   mask_feature_min_masks

     # ctc loss
     _a				:	List[str]			=   ctc_loss_reduction
     _a				:	Any			=   ctc_zero_infinity

     # adapter
     _a				:	Union[str, Any]			=   add_adapter
     _a				:	int			=   adapter_kernel_size
     _a				:	Any			=   adapter_stride
     _a				:	Optional[int]			=   num_adapter_layers
     _a				:	Optional[Any]			=   output_hidden_size or hidden_size

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     _a				:	Dict			=   classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     _a				:	Optional[int]			=   list(_a       )
     _a				:	Dict			=   list(_a       )
     _a				:	Optional[Any]			=   list(_a       )
     _a				:	Any			=   xvector_output_dim


  @property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     return math.prod(self.conv_stride       )





'''simple docstring'''







import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    str ,	__a      :    Optional[Any]=None ):






   """simple docstring"""

   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   _a				:	str			=   nn.Parameter(__a )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      _a				:	Any			=   nn.Parameter(__a )





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Optional[Any] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Tuple			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	Dict			=   np.asarray(weights[2] )

   set_param(
       torch_layer.self_attention.query_key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    Optional[int] ,	__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	str			=   np.asarray(weights[2] )
   _a				:	int			=   np.asarray(weights[3] )

   set_param(
       torch_layer.self_attention.query ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Any ,	__a      :    Optional[Any] ):






   """simple docstring"""

   _a				:	List[str]			=   weights[0][0][0]
   _a				:	List[Any]			=   np.asarray(layer_norm_a[0] )
   _a				:	List[str]			=   np.asarray(layer_norm_a[1] )
   set_param(
       torch_block.attention.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # lsh weights + output
   _a				:	List[str]			=   weights[0][1]
   if len(__a ) < 4:
      set_layer_weights_in_torch_lsh(__a ,	torch_block.attention ,	__a )
   else:
      set_layer_weights_in_torch_local(__a ,	torch_block.attention ,	__a )

   # intermediate weighs
   _a				:	Optional[Any]			=   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(__a ) == 4:
      _a				:	Union[str, Any]			=   intermediate_weights[2]

   # layernorm 2
   _a				:	Any			=   np.asarray(intermediate_weights[0][0] )
   _a				:	List[Any]			=   np.asarray(intermediate_weights[0][1] )
   set_param(
       torch_block.feed_forward.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # intermediate dense
   _a				:	Any			=   np.asarray(intermediate_weights[1][0] )
   _a				:	Any			=   np.asarray(intermediate_weights[1][1] )
   set_param(
       torch_block.feed_forward.dense.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)

   # intermediate out
   _a				:	Optional[int]			=   np.asarray(intermediate_weights[4][0] )
   _a				:	int			=   np.asarray(intermediate_weights[4][1] )
   set_param(
       torch_block.feed_forward.output.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Dict ,	__a      :    Dict ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Optional[int]			=   torch_model.reformer

   # word embeds
   _a				:	Tuple			=   np.asarray(weights[1] )
   set_param(
       torch_model_reformer.embeddings.word_embeddings ,	torch.tensor(__a ) ,	)

   if isinstance(weights[3] ,	__a ):
      _a				:	Any			=   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights ) ):
         _a				:	List[Any]			=   np.asarray(weights[3][emb_idx][0] )
         assert (
             position_embeddings.weights[emb_idx].shape == emb_weights.shape
         ), f"""{position_embeddings[emb_idx]} emb does not match"""
         _a				:	Any			=   nn.Parameter(torch.tensor(__a ) )

   _a				:	List[str]			=   weights[5]
   assert len(torch_model_reformer.encoder.layers ) * 4 == len(
       __a ), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
      _a				:	Tuple			=   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__a ,	__a ,	__a )

   # output layer norm
   _a				:	Optional[Any]			=   np.asarray(weights[7][0] )
   _a				:	int			=   np.asarray(weights[7][1] )
   set_param(
       torch_model_reformer.encoder.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # output embeddings
   _a				:	List[str]			=   np.asarray(weights[9][0] )
   _a				:	int			=   np.asarray(weights[9][1] )
   set_param(
       torch_model.lm_head.decoder ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Tuple ,	__a      :    Optional[Any] ,	__a      :    Dict ):






   """simple docstring"""

   _a				:	List[Any]			=   ReformerConfig.from_json_file(__a )
   print(f"""Building PyTorch model from configuration: {config}""" )
   _a				:	int			=   ReformerModelWithLMHead(__a )

   with open(__a ,	'rb' ) as f:
      _a				:	Optional[Any]			=   pickle.load(__a )['weights']

   set_model_weights_in_torch(__a ,	__a ,	config.hidden_size )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict() ,	__a )


if __name__ == "__main__":
  __lowerCAmelCase         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained Reformer 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."""
  )
  __lowerCAmelCase         =    parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



