



'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__lowerCAmelCase									=   {
    '''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									=   AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Any:
		_a       ,					_a     :      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_  )			->      int:
		_a     :      Optional[int]        =							{}

		_a     :      Tuple        =							r'.*sequential.(\d+).*'
		_a     :      int        =							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     :      Union[str, Any]        =							key.replace(lowerCAmelCase_						,			lowerCAmelCase_  )

				if re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						# replace sequential layers with list
						_a     :      List[str]        =							re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )

						_a     :      Optional[Any]        =							key.replace(f"""sequential.{sequential_layer}."""						,			f"""layers.{int(lowerCAmelCase_  )//3}.linear."""  )
				elif re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						_a     :      str        =							int(re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )  )

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

						_a     :      int        =							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     :      str        =							value
						_a     :      List[str]        =							mixed_qkv.size(0  ) // 3

						_a     :      str        =							mixed_qkv[:qkv_dim]
						_a     :      int        =							mixed_qkv[qkv_dim : qkv_dim * 2]
						_a     :      Any        =							mixed_qkv[qkv_dim * 2 :]

						_a     :      List[Any]        =							query_layer
						_a     :      Union[str, Any]        =							key_layer
						_a     :      Tuple        =							value_layer
				else:
						_a     :      Dict        =							value

		return model_state_dict



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      int:
		_a       ,					_a     :      Optional[Any]        =							init_clap(lowerCAmelCase_						,			enable_fusion=lowerCAmelCase_  )

		clap_model.eval()
		_a     :      Tuple        =							clap_model.state_dict()
		_a     :      Optional[int]        =							rename_state_dict(lowerCAmelCase_  )

		_a     :      List[str]        =							ClapConfig()
		_a     :      Tuple        =							enable_fusion
		_a     :      int        =							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									=   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									=   parser.parse_args()

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










'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		CycleDiffusionPipeline
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    "negative_prompt",
				    "height",
				    "width",
				    "negative_prompt_embeds",
				}
				snake_case_      =		PipelineTesterMixin.required_optional_params - {"latents"}
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}     )
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS


				def   A_ (		self    :					Tuple					):
									torch.manual_seed(0					)
									snake_case_									=				UNetaDConditionModel(
									    block_out_channels=(32, 64)       ,				layers_per_block=2       ,				sample_size=32       ,				in_channels=4       ,				out_channels=4       ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,				cross_attention_dim=32       ,				)
									snake_case_									=				DDIMScheduler(
									    beta_start=0.0_0085       ,				beta_end=0.012       ,				beta_schedule='''scaled_linear'''       ,				num_train_timesteps=1000       ,				clip_sample=lowercase_       ,				set_alpha_to_one=lowercase_       ,				)
									torch.manual_seed(0					)
									snake_case_									=				AutoencoderKL(
									    block_out_channels=[32, 64]       ,				in_channels=3       ,				out_channels=3       ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,				latent_channels=4       ,				)
									torch.manual_seed(0					)
									snake_case_									=				CLIPTextConfig(
									    bos_token_id=0       ,				eos_token_id=2       ,				hidden_size=32       ,				intermediate_size=37       ,				layer_norm_eps=1e-05       ,				num_attention_heads=4       ,				num_hidden_layers=5       ,				pad_token_id=1       ,				vocab_size=1000       ,				)
									snake_case_									=				CLIPTextModel(lowercase_					)
									snake_case_									=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

									snake_case_									=				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components


				def   A_ (		self    :					Any       ,				lowercase_    :					int       ,				lowercase_    :					Optional[Any]=0					):
									snake_case_									=				floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(lowercase_					)					).to(lowercase_					)
									snake_case_									=				image / 2 + 0.5
									if str(lowercase_					).startswith('''mps'''					):
														snake_case_									=				torch.manual_seed(lowercase_					)
									else:
														snake_case_									=				torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
									snake_case_									=				{
									    '''prompt''': '''An astronaut riding an elephant''',
									    '''source_prompt''': '''An astronaut riding a horse''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''eta''': 0.1,
									    '''strength''': 0.8,
									    '''guidance_scale''': 3,
									    '''source_guidance_scale''': 1,
									    '''output_type''': '''numpy''',
									}
									return inputs


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									snake_case_									=				self.get_dummy_components()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

									snake_case_									=				images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2


				@unittest.skipIf(torch_device != '''cuda'''       ,				'''This test requires a GPU'''					)
				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.get_dummy_components()
									for name, module in components.items():
														if hasattr(lowercase_       ,				'''half'''					):
																			snake_case_									=				module.half()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

									snake_case_									=				images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2


				@skip_mps
				def   A_ (		self    :					Optional[int]					):
									return super().test_save_load_local()


				@unittest.skip('''non-deterministic pipeline'''					)
				def   A_ (		self    :					List[Any]					):
									return super().test_inference_batch_single_identical()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_dict_tuple_outputs_equivalent()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_save_load_optional_components()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_attention_slicing_forward_pass()







@slow
@require_torch_gpu
class 						a				(   unittest.TestCase     ):


				def   A_ (		self    :					List[Any]					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

									snake_case_									=				'''CompVis/stable-diffusion-v1-4'''
									snake_case_									=				DDIMScheduler.from_pretrained(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				CycleDiffusionPipeline.from_pretrained(
									    lowercase_       ,				scheduler=lowercase_       ,				safety_checker=lowercase_       ,				torch_dtype=torch.floataa       ,				revision='''fp16'''					)

									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing()

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images

									# the values aren't exactly equal, but the images look the same visually
									assert np.abs(image - expected_image					).max() < 5e-1


				def   A_ (		self    :					List[str]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

									snake_case_									=				'''CompVis/stable-diffusion-v1-4'''
									snake_case_									=				DDIMScheduler.from_pretrained(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				CycleDiffusionPipeline.from_pretrained(lowercase_       ,				scheduler=lowercase_       ,				safety_checker=lowercase_					)

									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing()

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images

									assert np.abs(image - expected_image					).max() < 2e-2

