



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 A						(       __UpperCamelCase ,	__UpperCamelCase=False					)						->      Optional[Any]:
			A__     , A__       =    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 A						(       __UpperCamelCase					)						->      List[str]:
			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(_A ,	_A					)

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

									A__       =    key.replace(f'''sequential.{sequential_layer}.''' ,	f'''layers.{int(_A					)//3}.linear.'''					)
						elif re.match(_A ,	_A					):
									A__       =    int(re.match(_A ,	_A					).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 A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase=False					)						->      List[Any]:
			A__     , A__       =    init_clap(_A ,	enable_fusion=_A					)

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

			A__       =    ClapConfig()
			A__       =    enable_fusion
			A__       =    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__":
							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)









import math
import random


def A						(       __UpperCamelCase ,	__UpperCamelCase = False					)						->      float:
			if deriv:
						return value * (1 - value)
			return 1 / (1 + math.exp(-value					))


# Initial Value
SCREAMING_SNAKE_CASE__       =      0.02


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      float:
			A__       =    float(2 * (random.randint(1 ,	100					)) - 1					)

			for _ in range(__UpperCamelCase					):
						# Forward propagation
						A__       =    sigmoid_function(INITIAL_VALUE * weight					)
						# How much did we miss?
						A__       =    (expected / 100) - layer_a
						# Error delta
						A__       =    layer_1_error * sigmoid_function(__UpperCamelCase ,	__UpperCamelCase					)
						# Update weight
						weight += INITIAL_VALUE * layer_1_delta

			return layer_a * 100


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE__       =      int(input('''Expected value: '''))
							SCREAMING_SNAKE_CASE__       =      int(input('''Number of propagations: '''))
							print(forward_propagation(expected, number_propagations))




