


'''simple docstring'''

import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
	# Create universe of discourse in Python using linspace ()
	_UpperCAmelCase				: List[str]							    =				np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

	# Create two fuzzy sets by defining any membership function
	# (trapmf(), gbellmf(), gaussmf(), etc).
	_UpperCAmelCase				: List[str]							    =				[0, 2_5, 5_0]
	_UpperCAmelCase				: Dict							    =				[2_5, 5_0, 7_5]
	_UpperCAmelCase				: int							    =				fuzz.membership.trimf(X, abca)
	_UpperCAmelCase				: Any							    =				fuzz.membership.trimf(X, abca)

	# Compute the different operations using inbuilt functions.
	_UpperCAmelCase				: Union[str, Any]							    =				np.ones(7_5)
	_UpperCAmelCase				: Dict							    =				np.zeros((7_5,))
	# 1. Union = max(µA(x), µB(x))
	_UpperCAmelCase				: Union[str, Any]							    =				fuzz.fuzzy_or(X, young, X, middle_aged)[1]
	# 2. Intersection = min(µA(x), µB(x))
	_UpperCAmelCase				: Optional[int]							    =				fuzz.fuzzy_and(X, young, X, middle_aged)[1]
	# 3. Complement (A) = (1- min(µA(x))
	_UpperCAmelCase				: List[str]							    =				fuzz.fuzzy_not(young)
	# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
	_UpperCAmelCase				: Any							    =				fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
	# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
	_UpperCAmelCase				: str							    =				young + middle_aged - (young * middle_aged)
	# 6. Algebraic Product = (µA(x) * µB(x))
	_UpperCAmelCase				: Optional[int]							    =				young * middle_aged
	# 7. Bounded Sum = min[1,(µA(x), µB(x))]
	_UpperCAmelCase				: Tuple							    =				fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
	# 8. Bounded difference = min[0,(µA(x), µB(x))]
	_UpperCAmelCase				: Any							    =				fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

	# max-min composition
	# max-product composition

	# Plot each set A, set B and each operation result using plot() and subplot().
	from matplotlib import pyplot as plt

	plt.figure()

	plt.subplot(4, 3, 1)
	plt.plot(X, young)
	plt.title("""Young""")
	plt.grid(True)

	plt.subplot(4, 3, 2)
	plt.plot(X, middle_aged)
	plt.title("""Middle aged""")
	plt.grid(True)

	plt.subplot(4, 3, 3)
	plt.plot(X, union)
	plt.title("""union""")
	plt.grid(True)

	plt.subplot(4, 3, 4)
	plt.plot(X, intersection)
	plt.title("""intersection""")
	plt.grid(True)

	plt.subplot(4, 3, 5)
	plt.plot(X, complement_a)
	plt.title("""complement_a""")
	plt.grid(True)

	plt.subplot(4, 3, 6)
	plt.plot(X, difference)
	plt.title("""difference a/b""")
	plt.grid(True)

	plt.subplot(4, 3, 7)
	plt.plot(X, alg_sum)
	plt.title("""alg_sum""")
	plt.grid(True)

	plt.subplot(4, 3, 8)
	plt.plot(X, alg_product)
	plt.title("""alg_product""")
	plt.grid(True)

	plt.subplot(4, 3, 9)
	plt.plot(X, bdd_sum)
	plt.title("""bdd_sum""")
	plt.grid(True)

	plt.subplot(4, 3, 1_0)
	plt.plot(X, bdd_difference)
	plt.title("""bdd_difference""")
	plt.grid(True)

	plt.subplots_adjust(hspace=0.5)
	plt.show()





'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCAmelCase				: 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""",
}

_UpperCAmelCase				: Optional[int]							    =				AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				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 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				{}

						__lowerCAmelCase							       =				r'''.*sequential.(\d+).*'''
						__lowerCAmelCase							       =				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							       =				key.replace(lowerCamelCase,				lowerCamelCase)

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

																		__lowerCAmelCase							       =				key.replace(F"""sequential.{sequential_layer}.""",				F"""layers.{int(lowerCamelCase)//3}.linear.""")
												elif re.match(lowerCamelCase,				lowerCamelCase):
																		__lowerCAmelCase							       =				int(re.match(lowerCamelCase,				lowerCamelCase).group(1))

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

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

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

																		__lowerCAmelCase							       =				query_layer
																		__lowerCAmelCase							       =				key_layer
																		__lowerCAmelCase							       =				value_layer
												else:
																		__lowerCAmelCase							       =				value

						return model_state_dict

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				init_clap(lowerCamelCase,				enable_fusion=lowerCamelCase)

						clap_model.eval()
						__lowerCAmelCase							       =				clap_model.state_dict()
						__lowerCAmelCase							       =				rename_state_dict(lowerCamelCase)

						__lowerCAmelCase							       =				ClapConfig()
						__lowerCAmelCase							       =				enable_fusion
						__lowerCAmelCase							       =				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__":
	_UpperCAmelCase				: 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""")
	_UpperCAmelCase				: Optional[int]							    =				parser.parse_args()

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

