



'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__lowerCAmelCase									=   {
    '''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''',
}

__lowerCAmelCase									=   AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Any:
		_a       ,					_a     :      Union[str, Any]        =							create_model(
		    'HTSAT-tiny'						,			'roberta'						,			lowerCAmelCase_						,			precision='fp32'						,			device='cuda:0' if torch.cuda.is_available() else 'cpu'						,			enable_fusion=lowerCAmelCase_						,			fusion_type='aff_2d' if enable_fusion else None						,			)
		return model, model_cfg



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      int:
		_a     :      Optional[int]        =							{}

		_a     :      Tuple        =							r'.*sequential.(\d+).*'
		_a     :      int        =							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:
								_a     :      Union[str, Any]        =							key.replace(lowerCAmelCase_						,			lowerCAmelCase_  )

				if re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						# replace sequential layers with list
						_a     :      List[str]        =							re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )

						_a     :      Optional[Any]        =							key.replace(f"""sequential.{sequential_layer}."""						,			f"""layers.{int(lowerCAmelCase_  )//3}.linear."""  )
				elif re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						_a     :      str        =							int(re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )  )

						# Because in CLAP they use `nn.Sequential`...
						_a     :      Optional[Any]        =							1 if projecton_layer == 0 else 2

						_a     :      int        =							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
						_a     :      str        =							value
						_a     :      List[str]        =							mixed_qkv.size(0  ) // 3

						_a     :      str        =							mixed_qkv[:qkv_dim]
						_a     :      int        =							mixed_qkv[qkv_dim : qkv_dim * 2]
						_a     :      Any        =							mixed_qkv[qkv_dim * 2 :]

						_a     :      List[Any]        =							query_layer
						_a     :      Union[str, Any]        =							key_layer
						_a     :      Tuple        =							value_layer
				else:
						_a     :      Dict        =							value

		return model_state_dict



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      int:
		_a       ,					_a     :      Optional[Any]        =							init_clap(lowerCAmelCase_						,			enable_fusion=lowerCAmelCase_  )

		clap_model.eval()
		_a     :      Tuple        =							clap_model.state_dict()
		_a     :      Optional[int]        =							rename_state_dict(lowerCAmelCase_  )

		_a     :      List[str]        =							ClapConfig()
		_a     :      Tuple        =							enable_fusion
		_a     :      int        =							ClapModel(lowerCAmelCase_  )

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

		model.save_pretrained(lowerCAmelCase_  )
		transformers_config.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase									=   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''')
							__lowerCAmelCase									=   parser.parse_args()

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





'''simple docstring'''

import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return input_array.reshape((input_array.size, 1)     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					np.nan
 for i in range(UpperCAmelCase     ):
  lowercase__				:    Optional[Any]  =					features[:, labels == i]
  lowercase__				:    Optional[Any]  =					data.mean(1     )
  # Centralize the data of class i
  lowercase__				:    Dict  =					data - column_reshape(UpperCAmelCase     )
  if i > 0:
   # If covariance_sum is not None
   covariance_sum += np.dot(UpperCAmelCase		,     centered_data.T     )
  else:
   # If covariance_sum is np.nan (i.e. first loop)
   lowercase__				:    List[str]  =					np.dot(UpperCAmelCase		,     centered_data.T     )

 return covariance_sum / features.shape[1]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Tuple  =					features.mean(1     )
 lowercase__				:    Dict  =					np.nan
 for i in range(UpperCAmelCase     ):
  lowercase__				:    List[str]  =					features[:, labels == i]
  lowercase__				:    int  =					data.shape[1]
  lowercase__				:    Optional[int]  =					data.mean(1     )
  if i > 0:
   # If covariance_sum is not None
   covariance_sum += device_data * np.dot(
       column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )		,     (column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )).T		,     )
  else:
   # If covariance_sum is np.nan (i.e. first loop)
   lowercase__				:    Optional[int]  =					device_data * np.dot(
       column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )		,     (column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )).T		,     )

 return covariance_sum / features.shape[1]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):

 # Check if the features have been loaded
 if features.any():
  lowercase__				:    Optional[Any]  =					features.mean(1     )
  # Center the dataset
  lowercase__				:    List[str]  =					features - np.reshape(UpperCAmelCase		,     (data_mean.size, 1)     )
  lowercase__				:    Optional[Any]  =					np.dot(UpperCAmelCase		,     centered_data.T     ) / features.shape[1]
  lowercase__ ,							lowercase__				:    Tuple  =					np.linalg.eigh(UpperCAmelCase     )
  # Take all the columns in the reverse order (-1), and then takes only the first
  lowercase__				:    str  =					eigenvectors[:, ::-1][:, 0:dimensions]
  # Project the database on the new space
  lowercase__				:    Tuple  =					np.dot(filtered_eigenvectors.T		,     UpperCAmelCase     )
  logging.info('''Principal Component Analysis computed'''     )

  return projected_data
 else:
  logging.basicConfig(level=logging.ERROR		,     format='''%(message)s'''		,     force=UpperCAmelCase     )
  logging.error('''Dataset empty'''     )
  raise AssertionError
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 assert classes > dimensions

 # Check if features have been already loaded
 if features.any:
  lowercase__ ,							lowercase__				:    Any  =					eigh(
      covariance_between_classes(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )		,     covariance_within_classes(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )		,     )
  lowercase__				:    Optional[int]  =					eigenvectors[:, ::-1][:, :dimensions]
  lowercase__ ,							lowercase__ ,							lowercase__				:    Optional[int]  =					np.linalg.svd(UpperCAmelCase     )
  lowercase__				:    List[str]  =					svd_matrix[:, 0:dimensions]
  lowercase__				:    str  =					np.dot(filtered_svd_matrix.T		,     UpperCAmelCase     )
  logging.info('''Linear Discriminant Analysis computed'''     )

  return projected_data
 else:
  logging.basicConfig(level=logging.ERROR		,     format='''%(message)s'''		,     force=UpperCAmelCase     )
  logging.error('''Dataset empty'''     )
  raise AssertionError
def 			__UpperCamelCase       (   ):
 # Create dummy dataset with 2 classes and 3 features
 lowercase__				:    List[str]  =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
 lowercase__				:    Optional[Any]  =					np.array([0, 0, 0, 1, 1]     )
 lowercase__				:    str  =					2
 lowercase__				:    Dict  =					2

 # Assert that the function raises an AssertionError if dimensions > classes
 with pytest.raises(UpperCAmelCase     ) as error_info:
  lowercase__				:    int  =					linear_discriminant_analysis(
      UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
  if isinstance(UpperCAmelCase		,     np.ndarray     ):
   raise AssertionError(
       '''Did not raise AssertionError for dimensions > classes'''     )
  assert error_info.type is AssertionError
def 			__UpperCamelCase       (   ):
 lowercase__				:    Optional[int]  =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
 lowercase__				:    int  =					2
 lowercase__				:    Any  =					np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]     )

 with pytest.raises(UpperCAmelCase     ) as error_info:
  lowercase__				:    Dict  =					principal_component_analysis(UpperCAmelCase		,     UpperCAmelCase     )
  if not np.allclose(UpperCAmelCase		,     UpperCAmelCase     ):
   raise AssertionError
  assert error_info.type is AssertionError


if __name__ == "__main__":
  import doctest

  doctest.testmod()

