






'''simple docstring'''
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						Optional[int]=False							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[int]									=create_model(
          '''HTSAT-tiny''',	'''roberta''',	_lowercase,	precision='''fp32''',	device='''cuda:0''' if torch.cuda.is_available() else '''cpu''',	enable_fusion=_lowercase,	fusion_type='''aff_2d''' if enable_fusion else None,	)
      return model, model_cfg





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									={}

      SCREAMING_SNAKE_CASE__     :				str									=R'''.*sequential.(\d+).*'''
      SCREAMING_SNAKE_CASE__     :				str									=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:
                        SCREAMING_SNAKE_CASE__     :				Tuple									=key.replace(_lowercase,	_lowercase							)

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

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

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

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

                  SCREAMING_SNAKE_CASE__     :				Any									=mixed_qkv[:qkv_dim]
                  SCREAMING_SNAKE_CASE__     :				List[str]									=mixed_qkv[qkv_dim : qkv_dim * 2]
                  SCREAMING_SNAKE_CASE__     :				int									=mixed_qkv[qkv_dim * 2 :]

                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=query_layer
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=key_layer
                  SCREAMING_SNAKE_CASE__     :				Any									=value_layer
            else:
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=value

      return model_state_dict





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						str,	UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						str=False							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[Any]									=init_clap(_lowercase,	enable_fusion=_lowercase							)

      clap_model.eval()
      SCREAMING_SNAKE_CASE__     :				Any									=clap_model.state_dict()
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=rename_state_dict(_lowercase							)

      SCREAMING_SNAKE_CASE__     :				str									=ClapConfig()
      SCREAMING_SNAKE_CASE__     :				List[str]									=enable_fusion
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=ClapModel(_lowercase							)

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

      model.save_pretrained(_lowercase							)
      transformers_config.save_pretrained(_lowercase							)


if __name__ == "__main__":
  a_					= 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_					= 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 CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							ShapEImgaImgPipeline
 snake_case_  							=							["""image"""]
 snake_case_  							=							["""image"""]
 snake_case_  							=							[
     """num_images_per_prompt""",
     """num_inference_steps""",
     """generator""",
     """latents""",
     """guidance_scale""",
     """frame_size""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
       return 32




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Union[str, Any]:
       return 8




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Union[str, Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Dict									=CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size					,      image_size=64					,      projection_dim=self.text_embedder_hidden_size					,      intermediate_size=37					,      num_attention_heads=4					,      num_channels=3					,      num_hidden_layers=5					,      patch_size=1					,      )

       SCREAMING_SNAKE_CASE__     :				str									=CLIPVisionModel(__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				int									=CLIPImageProcessor(
           crop_size=2_24					,      do_center_crop=__lowercase					,      do_normalize=__lowercase					,      do_resize=__lowercase					,      image_mean=[0.48145466, 0.4578275, 0.40821073]					,      image_std=[0.26862954, 0.26130258, 0.27577711]					,      resample=3					,      size=2_24					,      )

       return image_processor




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Dict:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				str									={
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 16,
           '''embedding_dim''': self.time_input_dim,
           '''num_embeddings''': 32,
           '''embedding_proj_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''num_layers''': 1,
           '''clip_embed_dim''': self.time_input_dim * 2,
           '''additional_embeddings''': 0,
           '''time_embed_act_fn''': '''gelu''',
           '''norm_in_type''': '''layer''',
           '''embedding_proj_norm_type''': '''layer''',
           '''encoder_hid_proj_type''': None,
           '''added_emb_type''': None,
       }

       SCREAMING_SNAKE_CASE__     :				str									=PriorTransformer(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''param_shapes''': (
               (self.renderer_dim, 93),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
           ),
           '''d_latent''': self.time_input_dim,
           '''d_hidden''': self.renderer_dim,
           '''n_output''': 12,
           '''background''': (
               0.1,
               0.1,
               0.1,
           ),
       }
       SCREAMING_SNAKE_CASE__     :				str									=ShapERenderer(**__lowercase      )
       return model




 def      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				int									=self.dummy_prior
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_image_encoder
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_image_processor
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.dummy_renderer

       SCREAMING_SNAKE_CASE__     :				int									=HeunDiscreteScheduler(
           beta_schedule='''exp'''					,      num_train_timesteps=10_24					,      prediction_type='''sample'''					,      use_karras_sigmas=__lowercase					,      clip_sample=__lowercase					,      clip_sample_range=1.0					,      )
       SCREAMING_SNAKE_CASE__     :				Any									={
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''image_processor''': image_processor,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components




 def      __magic_name__	(      self	:							Any					,      __lowercase	:							List[str]					,      __lowercase	:							Any=0      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				int									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				List[str]									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									={
           '''image''': input_image,
           '''generator''': generator,
           '''num_inference_steps''': 1,
           '''frame_size''': 32,
           '''output_type''': '''np''',
       }
       return inputs




 def      __magic_name__	(      self	:							List[str]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				int									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				str									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images[0]
       SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]

       assert image.shape == (20, 32, 32, 3)

       SCREAMING_SNAKE_CASE__     :				List[Any]									=np.array(
           [
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
           ]      )

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




 def      __magic_name__	(      self	:							List[Any]      )	->						List[str]:
       # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
       self._test_inference_batch_consistent(batch_sizes=[1, 2]      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch_device == '''cpu'''
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=True
       self._test_inference_batch_single_identical(
           batch_size=2					,      test_max_difference=__lowercase					,      relax_max_difference=__lowercase					,      )




 def      __magic_name__	(      self	:							Dict      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()
       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=pipe.to(__lowercase      )
       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=1
       SCREAMING_SNAKE_CASE__     :				List[str]									=2

       SCREAMING_SNAKE_CASE__     :				Dict									=self.get_dummy_inputs(__lowercase      )

       for key in inputs.keys():
             if key in self.batch_params:
                   SCREAMING_SNAKE_CASE__     :				Tuple									=batch_size * [inputs[key]]

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipe(**__lowercase					,      num_images_per_prompt=__lowercase      )[0]

       assert images.shape[0] == batch_size * num_images_per_prompt






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




 def      __magic_name__	(      self	:							Optional[Any]      )	->						int:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def      __magic_name__	(      self	:							int      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				List[str]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/shap_e/test_shap_e_img2img_out.npy'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=pipe.to(__lowercase      )
       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=pipe(
           __lowercase					,      generator=__lowercase					,      guidance_scale=3.0					,      num_inference_steps=64					,      frame_size=64					,      output_type='''np'''					,      ).images[0]

       assert images.shape == (20, 64, 64, 3)

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )