



'''simple docstring'''



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase	:		Dict				=   {
    '''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''',
}

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



def   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						str=False					):






	'''simple docstring'''




	__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   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]					):






	'''simple docstring'''




	__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   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						Tuple				,		lowerCamelCase   :						List[Any]=False					):






	'''simple docstring'''




	__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__":
	lowerCAmelCase	:		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''')
	lowerCAmelCase	:		List[str]				=   parser.parse_args()

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




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  self.scheduler_classes[0](**UpperCamelCase     )

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

						self.assertEqual(output_a.shape			,				sample.shape     )
						self.assertEqual(output_a.shape			,				output_a.shape     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

						with self.assertRaises(UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
							scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,				timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )