import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_SCREAMING_SNAKE_CASE										=						{
    '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',
}

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






def  snake_case  (       snake_case__			:Dict   ,	snake_case__			:Union[str, Any]=False)						->   List[str]:
       _A   ,  _A        =  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  snake_case  (       snake_case__			:str)						->   Optional[int]:
       _A        =  {}

       _A        =  R""".*sequential.(\d+).*"""
       _A        =  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        =  key.replace(snake_case__   ,	snake_case__)

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

                     _A        =  key.replace(F'''sequential.{sequential_layer}.'''   ,	F'''layers.{int(snake_case__)//3}.linear.''')
              elif re.match(snake_case__   ,	snake_case__):
                     _A        =  int(re.match(snake_case__   ,	snake_case__).group(1))

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

                     _A        =  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        =  value
                     _A        =  mixed_qkv.size(0) // 3

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

                     _A        =  query_layer
                     _A        =  key_layer
                     _A        =  value_layer
              else:
                     _A        =  value

       return model_state_dict






def  snake_case  (       snake_case__			:List[str]   ,	snake_case__			:Union[str, Any]   ,	snake_case__			:Any   ,	snake_case__			:str=False)						->   Any:
       _A   ,  _A        =  init_clap(snake_case__   ,	enable_fusion=snake_case__)

       clap_model.eval()
       _A        =  clap_model.state_dict()
       _A        =  rename_state_dict(snake_case__)

       _A        =  ClapConfig()
       _A        =  enable_fusion
       _A        =  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__":
    _SCREAMING_SNAKE_CASE										=						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')
    _SCREAMING_SNAKE_CASE										=						parser.parse_args()

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





from math import isqrt, loga






def  snake_case  (       snake_case__			:int)						->   list[int]:
       _A        =  [True] * max_number
       for i in range(2   ,	isqrt(max_number - 1) + 1):
              if is_prime[i]:
                     for j in range(i**2   ,	snake_case__   ,	snake_case__):
                            _A        =  False

       return [i for i in range(2   ,	snake_case__) if is_prime[i]]






def  snake_case  (       snake_case__			:int = 800_800   ,	snake_case__			:int = 800_800)						->   int:
       _A        =  degree * loga(snake_case__)
       _A        =  int(snake_case__)
       _A        =  calculate_prime_numbers(snake_case__)

       _A        =  0
       _A        =  0
       _A        =  len(snake_case__) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left])
                  + prime_numbers[left] * loga(prime_numbers[right])
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
    print(F'''{solution() = }''')




