"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase__     						=  {
    """text_branch""": """text_model""",
    """audio_branch""": """audio_model.audio_encoder""",
    """attn""": """attention.self""",
    """self.proj""": """output.dense""",
    """attention.self_mask""": """attn_mask""",
    """mlp.fc1""": """intermediate.dense""",
    """mlp.fc2""": """output.dense""",
    """norm1""": """layernorm_before""",
    """norm2""": """layernorm_after""",
    """bn0""": """batch_norm""",
}

UpperCAmelCase__     						=  AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")






def 			__UpperCAmelCase    (	lowercase      ,lowercase=False						):







  """simple docstring"""
  _UpperCAmelCase       , _UpperCAmelCase         =					create_model(
      """HTSAT-tiny"""      ,"""roberta"""      ,lowercase      ,precision="""fp32"""      ,device="""cuda:0""" if torch.cuda.is_available() else """cpu"""      ,enable_fusion=lowercase      ,fusion_type="""aff_2d""" if enable_fusion else None      ,)
  return model, model_cfg






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					{}

  _UpperCAmelCase         =					R""".*sequential.(\d+).*"""
  _UpperCAmelCase         =					R""".*_projection.(\d+).*"""

  for key, value in state_dict.items():
    # check if any key needs to be modified
    for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
      if key_to_modify in key:
        _UpperCAmelCase         =					key.replace(lowercase      ,lowercase						)

    if re.match(lowercase      ,lowercase						):
      # replace sequential layers with list
      _UpperCAmelCase         =					re.match(lowercase      ,lowercase						).group(1						)

      _UpperCAmelCase         =					key.replace(f'''sequential.{sequential_layer}.'''      ,f'''layers.{int(lowercase						)//3}.linear.'''						)
    elif re.match(lowercase      ,lowercase						):
      _UpperCAmelCase         =					int(re.match(lowercase      ,lowercase						).group(1						)						)

      # Because in CLAP they use `nn.Sequential`...
      _UpperCAmelCase         =					1 if projecton_layer == 0 else 2

      _UpperCAmelCase         =					key.replace(f'''_projection.{projecton_layer}.'''      ,f'''_projection.linear{transformers_projection_layer}.'''						)

    if "audio" and "qkv" in key:
      # split qkv into query key and value
      _UpperCAmelCase         =					value
      _UpperCAmelCase         =					mixed_qkv.size(0						) // 3

      _UpperCAmelCase         =					mixed_qkv[:qkv_dim]
      _UpperCAmelCase         =					mixed_qkv[qkv_dim : qkv_dim * 2]
      _UpperCAmelCase         =					mixed_qkv[qkv_dim * 2 :]

      _UpperCAmelCase         =					query_layer
      _UpperCAmelCase         =					key_layer
      _UpperCAmelCase         =					value_layer
    else:
      _UpperCAmelCase         =					value

  return model_state_dict






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase=False						):







  """simple docstring"""
  _UpperCAmelCase       , _UpperCAmelCase         =					init_clap(lowercase      ,enable_fusion=lowercase						)

  clap_model.eval()
  _UpperCAmelCase         =					clap_model.state_dict()
  _UpperCAmelCase         =					rename_state_dict(lowercase						)

  _UpperCAmelCase         =					ClapConfig()
  _UpperCAmelCase         =					enable_fusion
  _UpperCAmelCase         =					ClapModel(lowercase						)

  # ignore the spectrogram embedding layer
  model.load_state_dict(lowercase      ,strict=lowercase						)

  model.save_pretrained(lowercase						)
  transformers_config.save_pretrained(lowercase						)


if __name__ == "__main__":
    UpperCAmelCase__     						=  argparse.ArgumentParser()
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
    UpperCAmelCase__     						=  parser.parse_args()

    convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)

"""simple docstring"""


import math


class 				a :


    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     list[list[float]]      , __lowerCAmelCase		:     list[int]    ):
      _UpperCAmelCase         =					0.0
      _UpperCAmelCase         =					0.0
      for i in range(len(__lowerCAmelCase    )    ):
        da += math.pow((sample[i] - weights[0][i])      , 2    )
        da += math.pow((sample[i] - weights[1][i])      , 2    )
        return 0 if da > da else 1
      return 0


    def    lowerCAmelCase_     (    self		:     Union[str, Any]      , __lowerCAmelCase		:     list[list[int | float]]      , __lowerCAmelCase		:     list[int]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     float    ):
      for i in range(len(__lowerCAmelCase    )    ):
        weights[j][i] += alpha * (sample[i] - weights[j][i])
      return weights






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  # Training Examples ( m, n )
  _UpperCAmelCase         =					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  _UpperCAmelCase         =					[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  _UpperCAmelCase         =					SelfOrganizingMap()
  _UpperCAmelCase         =					3
  _UpperCAmelCase         =					0.5

  for _ in range(lowercase						):
    for j in range(len(lowercase						)						):
      # training sample
      _UpperCAmelCase         =					training_samples[j]

      # Compute the winning vector
      _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

      # Update the winning vector
      _UpperCAmelCase         =					self_organizing_map.update(lowercase      ,lowercase      ,lowercase      ,lowercase						)

    # classify test sample
  _UpperCAmelCase         =					[0, 0, 0, 1]
  _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

  # results
  print(f'''Clusters that the test sample belongs to : {winner}'''						)
  print(f'''Weights that have been trained : {weights}'''						)


# running the main() function
if __name__ == "__main__":
    main()
