



"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__snake_case							:     List[Any]   =					{
    '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							:     List[str]   =					AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')


def     _lowercase       (						__snake_case      ,__snake_case=False			)		->     List[Any]:
			__lowerCAmelCase   :						Union[str, Any]				= 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     _lowercase       (						__snake_case			)		->     Optional[int]:
			__lowerCAmelCase   :						int				= {}

			__lowerCAmelCase   :						str				= R'.*sequential.(\d+).*'
			__lowerCAmelCase   :						Dict				= 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   :						List[Any]				= key.replace(__snake_case      ,__snake_case			)

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

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

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

									__lowerCAmelCase   :						Optional[Any]				= 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   :						str				= value
									__lowerCAmelCase   :						int				= mixed_qkv.size(0			) // 3

									__lowerCAmelCase   :						Union[str, Any]				= mixed_qkv[:qkv_dim]
									__lowerCAmelCase   :						Dict				= mixed_qkv[qkv_dim : qkv_dim * 2]
									__lowerCAmelCase   :						str				= mixed_qkv[qkv_dim * 2 :]

									__lowerCAmelCase   :						Optional[int]				= query_layer
									__lowerCAmelCase   :						List[str]				= key_layer
									__lowerCAmelCase   :						Dict				= value_layer
						else:
									__lowerCAmelCase   :						Union[str, Any]				= value

			return model_state_dict


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case      ,__snake_case=False			)		->     str:
			__lowerCAmelCase   :						Any				= init_clap(__snake_case      ,enable_fusion=__snake_case			)

			clap_model.eval()
			__lowerCAmelCase   :						List[str]				= clap_model.state_dict()
			__lowerCAmelCase   :						Tuple				= rename_state_dict(__snake_case			)

			__lowerCAmelCase   :						int				= ClapConfig()
			__lowerCAmelCase   :						Tuple				= enable_fusion
			__lowerCAmelCase   :						List[str]				= 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__":
	__snake_case							:     List[str]   =					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							:     List[Any]   =					parser.parse_args()

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




"""simple docstring"""






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch



@require_torch
class     A__     :




	'''simple docstring'''





	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Optional[Any]:

				"""simple docstring"""

				return self.get_dummy_input()

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> str:

				"""simple docstring"""

				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""")

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    _SCREAMING_SNAKE_CASE:	Tuple=False ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=False ,    _SCREAMING_SNAKE_CASE:	Dict=False ,    )       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= 4
				__lowerCAmelCase   :						List[str]				= 32
				__lowerCAmelCase   :						str				= (32, 32)

				__lowerCAmelCase   :						int				= torch.manual_seed(0)
				__lowerCAmelCase   :						Dict				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= (batch_size, num_channels) + sizes
				__lowerCAmelCase   :						Dict				= randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= {"hidden_states": hidden_states}

				if include_temb:
							__lowerCAmelCase   :						List[str]				= 128
							__lowerCAmelCase   :						Dict				= randn_tensor((batch_size, temb_channels) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				if include_res_hidden_states_tuple:
							__lowerCAmelCase   :						Dict				= torch.manual_seed(1)
							__lowerCAmelCase   :						int				= (randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE),)

				if include_encoder_hidden_states:
							__lowerCAmelCase   :						int				= floats_tensor((batch_size, 32, 32)).to(_SCREAMING_SNAKE_CASE)

				if include_skip_sample:
							__lowerCAmelCase   :						Optional[Any]				= randn_tensor(((batch_size, 3) + sizes) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				return dummy_input

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= {
				    "in_channels": 32,
				    "out_channels": 32,
				    "temb_channels": 128,
				}
				if self.block_type == "up":
							__lowerCAmelCase   :						Tuple				= 32

				if self.block_type == "mid":
							init_dict.pop("out_channels")

				__lowerCAmelCase   :						str				= self.dummy_input
				return init_dict, inputs_dict

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[str])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						Dict				= self.block_class(**_SCREAMING_SNAKE_CASE)
				unet_block.to(_SCREAMING_SNAKE_CASE)
				unet_block.eval()

				with torch.no_grad():
							__lowerCAmelCase   :						int				= unet_block(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						List[Any]				= output[0]

				self.assertEqual(output.shape ,    self.output_shape)

				__lowerCAmelCase   :						str				= output[0, -1, -3:, -3:]
				__lowerCAmelCase   :						List[Any]				= torch.tensor(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
				assert torch_all_close(output_slice.flatten() ,    _SCREAMING_SNAKE_CASE ,    atol=5e-3)







	@unittest.skipIf(torch_device == "mps" ,    "Training is not supported in mps")
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						str				= self.block_class(**_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.train()
				__lowerCAmelCase   :						Union[str, Any]				= model(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Union[str, Any]				= output[0]

				__lowerCAmelCase   :						List[Any]				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= randn_tensor(output.shape ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				loss.backward()