





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        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
							_UpperCAmelCase				:  int			   =   CycleDiffusionPipeline
							_UpperCAmelCase				:  Union[str, Any]			   =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    "negative_prompt",
							    "height",
							    "width",
							    "negative_prompt_embeds",
							}
							_UpperCAmelCase				:  Union[str, Any]			   =   PipelineTesterMixin.required_optional_params - {"latents"}
							_UpperCAmelCase				:  Optional[int]			   =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
							_UpperCAmelCase				:  Optional[int]			   =   IMAGE_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase				:  List[Any]			   =   IMAGE_TO_IMAGE_IMAGE_PARAMS







							def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
										torch.manual_seed(0      )
										lowerCamelCase__       :						Tuple        =							UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)		, layers_per_block=2		, sample_size=3_2		, in_channels=4		, out_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		, cross_attention_dim=3_2		, )
										lowerCamelCase__       :						Optional[int]        =							DDIMScheduler(
										    beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''		, num_train_timesteps=1_0_0_0		, clip_sample=A		, set_alpha_to_one=A		, )
										torch.manual_seed(0      )
										lowerCamelCase__       :						Dict        =							AutoencoderKL(
										    block_out_channels=[3_2, 6_4]		, in_channels=3		, out_channels=3		, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		, latent_channels=4		, )
										torch.manual_seed(0      )
										lowerCamelCase__       :						Union[str, Any]        =							CLIPTextConfig(
										    bos_token_id=0		, eos_token_id=2		, hidden_size=3_2		, intermediate_size=3_7		, layer_norm_eps=1e-05		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1_0_0_0		, )
										lowerCamelCase__       :						Tuple        =							CLIPTextModel(A      )
										lowerCamelCase__       :						Dict        =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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







							def 						__lowerCamelCase   (				self					:     int		, A					:     int		, A					:     List[Any]=0      )       ->List[str]:
										lowerCamelCase__       :						Dict        =							floats_tensor((1, 3, 3_2, 3_2)		, rng=random.Random(A      )      ).to(A      )
										lowerCamelCase__       :						Optional[Any]        =							image / 2 + 0.5
										if str(A      ).startswith('''mps'''      ):
													lowerCamelCase__       :						int        =							torch.manual_seed(A      )
										else:
													lowerCamelCase__       :						Union[str, Any]        =							torch.Generator(device=A      ).manual_seed(A      )
										lowerCamelCase__       :						str        =							{
										    '''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 						__lowerCamelCase   (				self					:     str      )       ->Any:
										lowerCamelCase__       :						Tuple        =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator

										lowerCamelCase__       :						Optional[Any]        =							self.get_dummy_components()
										lowerCamelCase__       :						Optional[int]        =							CycleDiffusionPipeline(**A      )
										lowerCamelCase__       :						List[Any]        =							pipe.to(A      )
										pipe.set_progress_bar_config(disable=A      )

										lowerCamelCase__       :						str        =							self.get_dummy_inputs(A      )
										lowerCamelCase__       :						Any        =							pipe(**A      )
										lowerCamelCase__       :						Optional[Any]        =							output.images

										lowerCamelCase__       :						Dict        =							images[0, -3:, -3:, -1]

										assert images.shape == (1, 3_2, 3_2, 3)
										lowerCamelCase__       :						List[Any]        =							np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]      )

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







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

										lowerCamelCase__       :						Dict        =							self.get_dummy_inputs(A      )
										lowerCamelCase__       :						Any        =							pipe(**A      )
										lowerCamelCase__       :						Union[str, Any]        =							output.images

										lowerCamelCase__       :						List[str]        =							images[0, -3:, -3:, -1]

										assert images.shape == (1, 3_2, 3_2, 3)
										lowerCamelCase__       :						Any        =							np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]      )

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







							@skip_mps
							def 						__lowerCamelCase   (				self					:     int      )       ->Optional[int]:
										return super().test_save_load_local()







							@unittest.skip('''non-deterministic pipeline'''      )
							def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Dict:
										return super().test_inference_batch_single_identical()







							@skip_mps
							def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[Any]:
										return super().test_dict_tuple_outputs_equivalent()







							@skip_mps
							def 						__lowerCamelCase   (				self					:     Any      )       ->int:
										return super().test_save_load_optional_components()







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


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







							def 						__lowerCamelCase   (				self					:     Dict      )       ->Tuple:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







							def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Any:
										lowerCamelCase__       :						Optional[Any]        =							load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/cycle-diffusion/black_colored_car.png'''      )
										lowerCamelCase__       :						Any        =							load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''      )
										lowerCamelCase__       :						Any        =							init_image.resize((5_1_2, 5_1_2)      )

										lowerCamelCase__       :						int        =							'''CompVis/stable-diffusion-v1-4'''
										lowerCamelCase__       :						Tuple        =							DDIMScheduler.from_pretrained(A		, subfolder='''scheduler'''      )
										lowerCamelCase__       :						str        =							CycleDiffusionPipeline.from_pretrained(
										    A		, scheduler=A		, safety_checker=A		, torch_dtype=torch.floataa		, revision='''fp16'''      )

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

										lowerCamelCase__       :						Dict        =							'''A black colored car'''
										lowerCamelCase__       :						Optional[Any]        =							'''A blue colored car'''

										lowerCamelCase__       :						List[str]        =							torch.manual_seed(0      )
										lowerCamelCase__       :						Tuple        =							pipe(
										    prompt=A		, source_prompt=A		, image=A		, num_inference_steps=1_0_0		, eta=0.1		, strength=0.85		, guidance_scale=3		, source_guidance_scale=1		, generator=A		, output_type='''np'''		, )
										lowerCamelCase__       :						List[str]        =							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 						__lowerCamelCase   (				self					:     Tuple      )       ->int:
										lowerCamelCase__       :						Any        =							load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/cycle-diffusion/black_colored_car.png'''      )
										lowerCamelCase__       :						Optional[Any]        =							load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''      )
										lowerCamelCase__       :						List[Any]        =							init_image.resize((5_1_2, 5_1_2)      )

										lowerCamelCase__       :						Tuple        =							'''CompVis/stable-diffusion-v1-4'''
										lowerCamelCase__       :						Any        =							DDIMScheduler.from_pretrained(A		, subfolder='''scheduler'''      )
										lowerCamelCase__       :						Dict        =							CycleDiffusionPipeline.from_pretrained(A		, scheduler=A		, safety_checker=A      )

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

										lowerCamelCase__       :						int        =							'''A black colored car'''
										lowerCamelCase__       :						Tuple        =							'''A blue colored car'''

										lowerCamelCase__       :						Optional[int]        =							torch.manual_seed(0      )
										lowerCamelCase__       :						Union[str, Any]        =							pipe(
										    prompt=A		, source_prompt=A		, image=A		, num_inference_steps=1_0_0		, eta=0.1		, strength=0.85		, guidance_scale=3		, source_guidance_scale=1		, generator=A		, output_type='''np'''		, )
										lowerCamelCase__       :						Tuple        =							output.images

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










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_A :   Optional[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',
}

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



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							create_model(
       '''HTSAT-tiny'''      ,  '''roberta'''      ,  UpperCAmelCase      ,  precision='''fp32'''      ,  device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''      ,  enable_fusion=UpperCAmelCase      ,  fusion_type='''aff_2d''' if enable_fusion else None      ,  )
   return model, model_cfg



def 	_a						(   UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							{}

   lowerCamelCase__       :						int        =							R'''.*sequential.(\d+).*'''
   lowerCamelCase__       :						Any        =							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[str]        =							key.replace(UpperCAmelCase      ,  UpperCAmelCase					)

      if re.match(UpperCAmelCase      ,  UpperCAmelCase					):
         # replace sequential layers with list
         lowerCamelCase__       :						List[Any]        =							re.match(UpperCAmelCase      ,  UpperCAmelCase					).group(1					)

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

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

         lowerCamelCase__       :						List[str]        =							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__       :						Optional[Any]        =							value
         lowerCamelCase__       :						Optional[Any]        =							mixed_qkv.size(0					) // 3

         lowerCamelCase__       :						Tuple        =							mixed_qkv[:qkv_dim]
         lowerCamelCase__       :						Dict        =							mixed_qkv[qkv_dim : qkv_dim * 2]
         lowerCamelCase__       :						int        =							mixed_qkv[qkv_dim * 2 :]

         lowerCamelCase__       :						Optional[int]        =							query_layer
         lowerCamelCase__       :						str        =							key_layer
         lowerCamelCase__       :						List[str]        =							value_layer
      else:
         lowerCamelCase__       :						Tuple        =							value

   return model_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    List[Any]:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							init_clap(UpperCAmelCase      ,  enable_fusion=UpperCAmelCase					)

   clap_model.eval()
   lowerCamelCase__       :						List[Any]        =							clap_model.state_dict()
   lowerCamelCase__       :						Dict        =							rename_state_dict(UpperCAmelCase					)

   lowerCamelCase__       :						Optional[int]        =							ClapConfig()
   lowerCamelCase__       :						Optional[int]        =							enable_fusion
   lowerCamelCase__       :						Optional[Any]        =							ClapModel(UpperCAmelCase					)

   # ignore the spectrogram embedding layer
   model.load_state_dict(UpperCAmelCase      ,  strict=UpperCAmelCase					)

   model.save_pretrained(UpperCAmelCase					)
   transformers_config.save_pretrained(UpperCAmelCase					)


if __name__ == "__main__":
       _A :   int  			=  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')
       _A :   Tuple  			=  parser.parse_args()

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



