



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()
class    lowercase__     (					unittest.TestCase   ):







       '''simple docstring'''


       def   lowercase__       (							self    :						Dict					)				->		Optional[int]:


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()



       @property
       def   lowercase__       (							self    :						int					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							1
             UpperCAmelCase_    				=							3
             UpperCAmelCase_    				=							(32, 32)

             UpperCAmelCase_    				=							floats_tensor((batch_size, num_channels) + sizes	,       rng=random.Random(0					)					).to(_UpperCAmelCase					)
             return image



       @property
       def   lowercase__       (							self    :						Dict					)				->		Dict:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							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	,       )
             return model



       @property
       def   lowercase__       (							self    :						Any					)				->		str:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							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	,       )
             return model



       @property
       def   lowercase__       (							self    :						Optional[int]					)				->		Tuple:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							RobertaSeriesConfig(
                 hidden_size=32	,       project_dim=32	,       intermediate_size=37	,       layer_norm_eps=1e-05	,       num_attention_heads=4	,       num_hidden_layers=5	,       pad_token_id=1	,       vocab_size=5006	,       )
             return RobertaSeriesModelWithTransformation(_UpperCAmelCase					)



       @property
       def   lowercase__       (							self    :						str					)				->		str:


             '''simple docstring'''




             def extract(*_UpperCAmelCase    :						str	,       **_UpperCAmelCase    :						Optional[Any]					):
                   class    lowercase__     :







                          '''simple docstring'''


                          def __init__(							self    :						Optional[Any]					)				->		str:


                                '''simple docstring'''




                                UpperCAmelCase_    				=							torch.ones([0]					)







                          def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						Optional[int]					)				->		List[Any]:


                                '''simple docstring'''




                                self.pixel_values.to(_UpperCAmelCase					)
                                return self



                   return Out()

             return extract



       def   lowercase__       (							self    :						Optional[Any]					)				->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_    				=							self.dummy_cond_unet
             UpperCAmelCase_    				=							PNDMScheduler(skip_prk_steps=_UpperCAmelCase					)
             UpperCAmelCase_    				=							self.dummy_vae
             UpperCAmelCase_    				=							self.dummy_text_encoder
             UpperCAmelCase_    				=							XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"					)
             UpperCAmelCase_    				=							77

             UpperCAmelCase_    				=							self.dummy_image.to(_UpperCAmelCase					)
             UpperCAmelCase_    				=							init_image / 2 + 0.5

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_    				=							AltDiffusionImgaImgPipeline(
                 unet=_UpperCAmelCase	,       scheduler=_UpperCAmelCase	,       vae=_UpperCAmelCase	,       text_encoder=_UpperCAmelCase	,       tokenizer=_UpperCAmelCase	,       safety_checker=_UpperCAmelCase	,       feature_extractor=self.dummy_extractor	,       )
             UpperCAmelCase_    				=							VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor	,       do_normalize=_UpperCAmelCase					)
             UpperCAmelCase_    				=							alt_pipe.to(_UpperCAmelCase					)
             alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							"A painting of a squirrel eating a burger"
             UpperCAmelCase_    				=							torch.Generator(device=_UpperCAmelCase					).manual_seed(0					)
             UpperCAmelCase_    				=							alt_pipe(
                 [prompt]	,       generator=_UpperCAmelCase	,       guidance_scale=6.0	,       num_inference_steps=2	,       output_type="np"	,       image=_UpperCAmelCase	,       )

             UpperCAmelCase_    				=							output.images

             UpperCAmelCase_    				=							torch.Generator(device=_UpperCAmelCase					).manual_seed(0					)
             UpperCAmelCase_    				=							alt_pipe(
                 [prompt]	,       generator=_UpperCAmelCase	,       guidance_scale=6.0	,       num_inference_steps=2	,       output_type="np"	,       image=_UpperCAmelCase	,       return_dict=_UpperCAmelCase	,       )[0]

             UpperCAmelCase_    				=							image[0, -3:, -3:, -1]
             UpperCAmelCase_    				=							image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_    				=							np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]					)

             assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-3
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 5e-3



       @unittest.skipIf(torch_device != "cuda"	,       "This test requires a GPU"					)
       def   lowercase__       (							self    :						List[str]					)				->		str:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.dummy_cond_unet
             UpperCAmelCase_    				=							PNDMScheduler(skip_prk_steps=_UpperCAmelCase					)
             UpperCAmelCase_    				=							self.dummy_vae
             UpperCAmelCase_    				=							self.dummy_text_encoder
             UpperCAmelCase_    				=							XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"					)
             UpperCAmelCase_    				=							77

             UpperCAmelCase_    				=							self.dummy_image.to(_UpperCAmelCase					)

             # put models in fp16
             UpperCAmelCase_    				=							unet.half()
             UpperCAmelCase_    				=							vae.half()
             UpperCAmelCase_    				=							bert.half()

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_    				=							AltDiffusionImgaImgPipeline(
                 unet=_UpperCAmelCase	,       scheduler=_UpperCAmelCase	,       vae=_UpperCAmelCase	,       text_encoder=_UpperCAmelCase	,       tokenizer=_UpperCAmelCase	,       safety_checker=_UpperCAmelCase	,       feature_extractor=self.dummy_extractor	,       )
             UpperCAmelCase_    				=							VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor	,       do_normalize=_UpperCAmelCase					)
             UpperCAmelCase_    				=							alt_pipe.to(_UpperCAmelCase					)
             alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							"A painting of a squirrel eating a burger"
             UpperCAmelCase_    				=							torch.manual_seed(0					)
             UpperCAmelCase_    				=							alt_pipe(
                 [prompt]	,       generator=_UpperCAmelCase	,       num_inference_steps=2	,       output_type="np"	,       image=_UpperCAmelCase	,       ).images

             assert image.shape == (1, 32, 32, 3)







       @unittest.skipIf(torch_device != "cuda"	,       "This test requires a GPU"					)
       def   lowercase__       (							self    :						Union[str, Any]					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"					)
             # resize to resolution that is divisible by 8 but not 16 or 32
             UpperCAmelCase_    				=							init_image.resize((760, 504)					)

             UpperCAmelCase_    				=							"BAAI/AltDiffusion"
             UpperCAmelCase_    				=							AltDiffusionImgaImgPipeline.from_pretrained(
                 _UpperCAmelCase	,       safety_checker=_UpperCAmelCase	,       )
             pipe.to(_UpperCAmelCase					)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)
             pipe.enable_attention_slicing()

             UpperCAmelCase_    				=							"A fantasy landscape, trending on artstation"

             UpperCAmelCase_    				=							torch.manual_seed(0					)
             UpperCAmelCase_    				=							pipe(
                 prompt=_UpperCAmelCase	,       image=_UpperCAmelCase	,       strength=0.75	,       guidance_scale=7.5	,       generator=_UpperCAmelCase	,       output_type="np"	,       )
             UpperCAmelCase_    				=							output.images[0]

             UpperCAmelCase_    				=							image[255:258, 383:386, -1]

             assert image.shape == (504, 760, 3)
             UpperCAmelCase_    				=							np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]					)

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



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







       '''simple docstring'''


       def   lowercase__       (							self    :						Tuple					)				->		List[str]:


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()







       def   lowercase__       (							self    :						Optional[int]					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"					)
             UpperCAmelCase_    				=							init_image.resize((768, 512)					)
             UpperCAmelCase_    				=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"					)

             UpperCAmelCase_    				=							"BAAI/AltDiffusion"
             UpperCAmelCase_    				=							AltDiffusionImgaImgPipeline.from_pretrained(
                 _UpperCAmelCase	,       safety_checker=_UpperCAmelCase	,       )
             pipe.to(_UpperCAmelCase					)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)
             pipe.enable_attention_slicing()

             UpperCAmelCase_    				=							"A fantasy landscape, trending on artstation"

             UpperCAmelCase_    				=							torch.manual_seed(0					)
             UpperCAmelCase_    				=							pipe(
                 prompt=_UpperCAmelCase	,       image=_UpperCAmelCase	,       strength=0.75	,       guidance_scale=7.5	,       generator=_UpperCAmelCase	,       output_type="np"	,       )
             UpperCAmelCase_    				=							output.images[0]

             assert image.shape == (512, 768, 3)
             # img2img is flaky across GPUs even in fp32, so using MAE here
             assert np.abs(expected_image - image					).max() < 1e-2









"""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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							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      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							{}

      UpperCAmelCase_    				=							r".*sequential.(\d+).*"
      UpperCAmelCase_    				=							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:
                        UpperCAmelCase_    				=							key.replace(lowerCAmelCase__			,    lowerCAmelCase__   )

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

                  UpperCAmelCase_    				=							key.replace(f"""sequential.{sequential_layer}."""			,    f"""layers.{int(lowerCAmelCase__   )//3}.linear."""   )
            elif re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							int(re.match(lowerCAmelCase__			,    lowerCAmelCase__   ).group(1   )   )

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

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

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

                  UpperCAmelCase_    				=							query_layer
                  UpperCAmelCase_    				=							key_layer
                  UpperCAmelCase_    				=							value_layer
            else:
                  UpperCAmelCase_    				=							value

      return model_state_dict
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							init_clap(lowerCAmelCase__			,    enable_fusion=lowerCAmelCase__   )

      clap_model.eval()
      UpperCAmelCase_    				=							clap_model.state_dict()
      UpperCAmelCase_    				=							rename_state_dict(lowerCAmelCase__   )

      UpperCAmelCase_    				=							ClapConfig()
      UpperCAmelCase_    				=							enable_fusion
      UpperCAmelCase_    				=							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)




