





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			(    A				,							A=False     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    A     ):




   '''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(A				,							A     )

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

         UpperCAmelCase__						=key.replace(F"""sequential.{sequential_layer}."""				,							F"""layers.{int(A     )//3}.linear."""     )
      elif re.match(A				,							A     ):
         UpperCAmelCase__						=int(re.match(A				,							A     ).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			(    A				,							A				,							A				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=init_clap(A				,							enable_fusion=A     )

   clap_model.eval()
   UpperCAmelCase__						=clap_model.state_dict()
   UpperCAmelCase__						=rename_state_dict(A     )

   UpperCAmelCase__						=ClapConfig()
   UpperCAmelCase__						=enable_fusion
   UpperCAmelCase__						=ClapModel(A     )

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

   model.save_pretrained(A     )
   transformers_config.save_pretrained(A     )


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)










from math import factorial






def 				_UpperCAmelCase			(    A = 20     ):




   '''simple docstring'''

   UpperCAmelCase__						=2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
   # 2, 3,...
   UpperCAmelCase__						=n // 2

   return int(factorial(A     ) / (factorial(A     ) * factorial(n - k     ))     )


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        print(solution(20))
    else:
        try:
            UpperCamelCase_												=	int(sys.argv[1])
            print(solution(n))
        except ValueError:
            print('Invalid entry - please enter a number.')



