



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__snake_case							:						List[str]      ={
    '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',
}

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






def       lowerCAmelCase__						(       lowerCamelCase_						:						Optional[Any]			,lowerCamelCase_						:						Optional[Any]=False):





				'''simple docstring'''

				lowerCAmelCase__		,						lowerCAmelCase__	:							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_						:						Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__	:							List[Any]				=	{}

				lowerCAmelCase__	:							Tuple				=	r'''.*sequential.(\d+).*'''
				lowerCAmelCase__	:							str				=	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:
																lowerCAmelCase__	:							Tuple				=	key.replace(lowerCamelCase_			,lowerCamelCase_)

								if re.match(lowerCamelCase_			,lowerCamelCase_):
												# replace sequential layers with list
												lowerCAmelCase__	:							Optional[Any]				=	re.match(lowerCamelCase_			,lowerCamelCase_).group(1)

												lowerCAmelCase__	:							str				=	key.replace(f"""sequential.{sequential_layer}."""			,f"""layers.{int(lowerCamelCase_)//3}.linear.""")
								elif re.match(lowerCamelCase_			,lowerCamelCase_):
												lowerCAmelCase__	:							Dict				=	int(re.match(lowerCamelCase_			,lowerCamelCase_).group(1))

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

												lowerCAmelCase__	:							List[str]				=	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
												lowerCAmelCase__	:							List[str]				=	value
												lowerCAmelCase__	:							Union[str, Any]				=	mixed_qkv.size(0) // 3

												lowerCAmelCase__	:							Union[str, Any]				=	mixed_qkv[:qkv_dim]
												lowerCAmelCase__	:							int				=	mixed_qkv[qkv_dim : qkv_dim * 2]
												lowerCAmelCase__	:							int				=	mixed_qkv[qkv_dim * 2 :]

												lowerCAmelCase__	:							Optional[int]				=	query_layer
												lowerCAmelCase__	:							int				=	key_layer
												lowerCAmelCase__	:							List[str]				=	value_layer
								else:
												lowerCAmelCase__	:							Any				=	value

				return model_state_dict






def       lowerCAmelCase__						(       lowerCamelCase_						:						Optional[Any]			,lowerCamelCase_						:						Dict			,lowerCamelCase_						:						Tuple			,lowerCamelCase_						:						Optional[int]=False):





				'''simple docstring'''

				lowerCAmelCase__		,						lowerCAmelCase__	:							Optional[Any]				=	init_clap(lowerCamelCase_			,enable_fusion=lowerCamelCase_)

				clap_model.eval()
				lowerCAmelCase__	:							List[Any]				=	clap_model.state_dict()
				lowerCAmelCase__	:							Tuple				=	rename_state_dict(lowerCamelCase_)

				lowerCAmelCase__	:							Dict				=	ClapConfig()
				lowerCAmelCase__	:							Optional[Any]				=	enable_fusion
				lowerCAmelCase__	:							Union[str, Any]				=	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__":
							__snake_case							:						List[Any]      =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')
							__snake_case							:						str      =parser.parse_args()

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




from itertools import product







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		sides_number
      A_							       =		max_face_number * dice_number
      A_							       =		[0] * (max_total + 1)

      A_							       =		1
      A_							       =		range(SCREAMING_SNAKE_CASE		,				max_face_number + 1   )
      for dice_numbers in product(SCREAMING_SNAKE_CASE		,				repeat=SCREAMING_SNAKE_CASE   ):
            A_							       =		sum(SCREAMING_SNAKE_CASE   )
            totals_frequencies[total] += 1

      return totals_frequencies







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      A_							       =		total_frequency_distribution(
          sides_number=4		,				dice_number=9   )
      A_							       =		total_frequency_distribution(
          sides_number=6		,				dice_number=6   )

      A_							       =		0
      A_							       =		9
      A_							       =		4 * 9
      A_							       =		6
      for peter_total in range(SCREAMING_SNAKE_CASE		,				max_peter_total + 1   ):
            peter_wins_count += peter_totals_frequencies[peter_total] * sum(
                colin_totals_frequencies[min_colin_total:peter_total]   )

      A_							       =		(4**9) * (6**6)
      A_							       =		peter_wins_count / total_games_number

      A_							       =		round(SCREAMING_SNAKE_CASE		,				ndigits=7   )

      return rounded_peter_win_probability


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





