




"""simple docstring"""







import numpy







class  lowerCamelCase__ :






    def __init__(			self     ,A     ,A ):
          UpperCAmelCase          =	input_array

          # Random initial weights are assigned where first argument is the
          # number of nodes in previous layer and second argument is the
          # number of nodes in the next layer.

          # Random initial weights are assigned.
          # self.input_array.shape[1] is used to represent number of nodes in input layer.
          # First hidden layer consists of 4 nodes.
          UpperCAmelCase          =	numpy.random.rand(
              self.input_array.shape[1]     ,4 )

          # Random initial values for the first hidden layer.
          # First hidden layer has 4 nodes.
          # Second hidden layer has 3 nodes.
          UpperCAmelCase          =	numpy.random.rand(
              4     ,3 )

          # Random initial values for the second hidden layer.
          # Second hidden layer has 3 nodes.
          # Output layer has 1 node.
          UpperCAmelCase          =	numpy.random.rand(3     ,1 )

          # Real output values provided.
          UpperCAmelCase          =	output_array

          # Predicted output values by the neural network.
          # Predicted_output array initially consists of zeroes.
          UpperCAmelCase          =	numpy.zeros(output_array.shape )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	sigmoid(
              numpy.dot(self.input_array     ,self.input_layer_and_first_hidden_layer_weights ) )

          # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
          # connecting the first hidden set of nodes with the second hidden set of nodes.
          UpperCAmelCase          =	sigmoid(
              numpy.dot(
                  self.layer_between_input_and_first_hidden_layer     ,self.first_hidden_layer_and_second_hidden_layer_weights     ,) )

          # layer_between_second_hidden_layer_and_output is the layer connecting
          # second hidden layer with the output node.
          UpperCAmelCase          =	sigmoid(
              numpy.dot(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer     ,self.second_hidden_layer_and_output_layer_weights     ,) )

          return self.layer_between_second_hidden_layer_and_output






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	numpy.dot(
              self.layer_between_first_hidden_layer_and_second_hidden_layer.T     ,2
              * (self.output_array - self.predicted_output)
              * sigmoid_derivative(self.predicted_output )     ,)
          UpperCAmelCase          =	numpy.dot(
              self.layer_between_input_and_first_hidden_layer.T     ,numpy.dot(
                  2
                  * (self.output_array - self.predicted_output)
                  * sigmoid_derivative(self.predicted_output )     ,self.second_hidden_layer_and_output_layer_weights.T     ,)
              * sigmoid_derivative(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer )     ,)
          UpperCAmelCase          =	numpy.dot(
              self.input_array.T     ,numpy.dot(
                  numpy.dot(
                      2
                      * (self.output_array - self.predicted_output)
                      * sigmoid_derivative(self.predicted_output )     ,self.second_hidden_layer_and_output_layer_weights.T     ,)
                  * sigmoid_derivative(
                      self.layer_between_first_hidden_layer_and_second_hidden_layer )     ,self.first_hidden_layer_and_second_hidden_layer_weights.T     ,)
              * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer )     ,)

          self.input_layer_and_first_hidden_layer_weights += (
              updated_input_layer_and_first_hidden_layer_weights
          )
          self.first_hidden_layer_and_second_hidden_layer_weights += (
              updated_first_hidden_layer_and_second_hidden_layer_weights
          )
          self.second_hidden_layer_and_output_layer_weights += (
              updated_second_hidden_layer_and_output_layer_weights
          )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          for iteration in range(1     ,iterations + 1 ):
                UpperCAmelCase          =	self.feedforward()
                self.back_propagation()
                if give_loss:
                      UpperCAmelCase          =	numpy.mean(numpy.square(output - self.feedforward() ) )
                      print(F'''Iteration {iteration} Loss: {loss}''' )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	input_arr

          UpperCAmelCase          =	sigmoid(
              numpy.dot(self.array     ,self.input_layer_and_first_hidden_layer_weights ) )

          UpperCAmelCase          =	sigmoid(
              numpy.dot(
                  self.layer_between_input_and_first_hidden_layer     ,self.first_hidden_layer_and_second_hidden_layer_weights     ,) )

          UpperCAmelCase          =	sigmoid(
              numpy.dot(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer     ,self.second_hidden_layer_and_output_layer_weights     ,) )

          return int(self.layer_between_second_hidden_layer_and_output > 0.6 )







def _a			(		_snake_case							):







      """simple docstring"""






      return 1 / (1 + numpy.exp(-value							))

def _a			(		_snake_case							):







      """simple docstring"""






      return (value) * (1 - (value))

def _a			(		):







      """simple docstring"""






      UpperCAmelCase          =	numpy.array(
          (
              [0, 0, 0],
              [0, 0, 1],
              [0, 1, 0],
              [0, 1, 1],
              [1, 0, 0],
              [1, 0, 1],
              [1, 1, 0],
              [1, 1, 1],
          )				,	dtype=numpy.floataa				,	)

      # True output values for the given input values.
      UpperCAmelCase          =	numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])				,	dtype=numpy.floataa							)

      # Calling neural network class.
      UpperCAmelCase          =	TwoHiddenLayerNeuralNetwork(
          input_array=_snake_case				,	output_array=_snake_case							)

      # Calling training function.
      # Set give_loss to True if you want to see loss in every iteration.
      neural_network.train(output=_snake_case				,	iterations=10				,	give_loss=_snake_case							)

      return neural_network.predict(numpy.array(([1, 1, 1])				,	dtype=numpy.floataa							)							)


if __name__ == "__main__":
  example()











"""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 _a			(		_snake_case				,	_snake_case=False							):







      """simple docstring"""






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

def _a			(		_snake_case							):







      """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(_snake_case				,	_snake_case							)

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

                  UpperCAmelCase          =	key.replace(F'''sequential.{sequential_layer}.'''				,	F'''layers.{int(_snake_case							)//3}.linear.'''							)
            elif re.match(_snake_case				,	_snake_case							):
                  UpperCAmelCase          =	int(re.match(_snake_case				,	_snake_case							).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 _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	init_clap(_snake_case				,	enable_fusion=_snake_case							)

      clap_model.eval()
      UpperCAmelCase          =	clap_model.state_dict()
      UpperCAmelCase          =	rename_state_dict(_snake_case							)

      UpperCAmelCase          =	ClapConfig()
      UpperCAmelCase          =	enable_fusion
      UpperCAmelCase          =	ClapModel(_snake_case							)

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

      model.save_pretrained(_snake_case							)
      transformers_config.save_pretrained(_snake_case							)


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)



