


import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




	"""simple docstring"""


	__UpperCAmelCase					:							Optional[Any]  						=						ShapEPipeline
	__UpperCAmelCase					:							Tuple  						=						["prompt"]
	__UpperCAmelCase					:							str  						=						["prompt"]
	__UpperCAmelCase					:							Dict  						=						[
	    "num_images_per_prompt",
	    "num_inference_steps",
	    "generator",
	    "latents",
	    "guidance_scale",
	    "frame_size",
	    "output_type",
	    "return_dict",
	]
	__UpperCAmelCase					:							Optional[int]  						=						False

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

	@property
	def 					__snake_case      (							self :						Dict       )			->					Optional[Any]:
								return 32

	@property
	def 					__snake_case      (							self :						str       )			->					Any:
								return self.time_input_dim * 4

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

	@property
	def 					__snake_case      (							self :						Tuple       )			->					Tuple:
								__snake_case	:    List[Any]											=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
								return tokenizer

	@property
	def 					__snake_case      (							self :						Tuple       )			->					Optional[Any]:
								torch.manual_seed(0       )
								__snake_case	:    Optional[Any]											=     CLIPTextConfig(
								    bos_token_id=0					,			eos_token_id=2					,			hidden_size=self.text_embedder_hidden_size					,			projection_dim=self.text_embedder_hidden_size					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1000					,			)
								return CLIPTextModelWithProjection(lowerCamelCase       )

	@property
	def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
								torch.manual_seed(0       )

								__snake_case	:    int											=     {
								    "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",
								    "encoder_hid_proj_type": None,
								    "added_emb_type": None,
								}

								__snake_case	:    Tuple											=     PriorTransformer(**lowerCamelCase       )
								return model

	@property
	def 					__snake_case      (							self :						Optional[Any]       )			->					List[Any]:
								torch.manual_seed(0       )

								__snake_case	:    List[str]											=     {
								    "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,
								    ),
								}
								__snake_case	:    int											=     ShapERenderer(**lowerCamelCase       )
								return model

	def 					__snake_case      (							self :						str       )			->					Any:
								__snake_case	:    List[str]											=     self.dummy_prior
								__snake_case	:    Optional[int]											=     self.dummy_text_encoder
								__snake_case	:    Optional[Any]											=     self.dummy_tokenizer
								__snake_case	:    Any											=     self.dummy_renderer

								__snake_case	:    List[Any]											=     HeunDiscreteScheduler(
								    beta_schedule="exp"					,			num_train_timesteps=1024					,			prediction_type="sample"					,			use_karras_sigmas=lowerCamelCase					,			clip_sample=lowerCamelCase					,			clip_sample_range=1.0					,			)
								__snake_case	:    Dict											=     {
								    "prior": prior,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "renderer": renderer,
								    "scheduler": scheduler,
								}

								return components

	def 					__snake_case      (							self :						Dict					,			lowerCamelCase :						str					,			lowerCamelCase :						List[str]=0       )			->					List[Any]:
								if str(lowerCamelCase       ).startswith("mps"       ):
															__snake_case	:    int											=     torch.manual_seed(lowerCamelCase       )
								else:
															__snake_case	:    Any											=     torch.Generator(device=lowerCamelCase       ).manual_seed(lowerCamelCase       )
								__snake_case	:    List[str]											=     {
								    "prompt": "horse",
								    "generator": generator,
								    "num_inference_steps": 1,
								    "frame_size": 32,
								    "output_type": "np",
								}
								return inputs

	def 					__snake_case      (							self :						Tuple       )			->					Any:
								__snake_case	:    Union[str, Any]											=     "cpu"

								__snake_case	:    Any											=     self.get_dummy_components()

								__snake_case	:    Tuple											=     self.pipeline_class(**lowerCamelCase       )
								__snake_case	:    List[str]											=     pipe.to(lowerCamelCase       )

								pipe.set_progress_bar_config(disable=lowerCamelCase       )

								__snake_case	:    List[Any]											=     pipe(**self.get_dummy_inputs(lowerCamelCase       )       )
								__snake_case	:    str											=     output.images[0]
								__snake_case	:    Optional[int]											=     image[0, -3:, -3:, -1]

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

								__snake_case	:    str											=     np.array(
								    [
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								    ]       )

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

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

	def 					__snake_case      (							self :						Dict       )			->					Any:
								__snake_case	:    Union[str, Any]											=     torch_device == "cpu"
								__snake_case	:    Optional[int]											=     True

								self._test_inference_batch_single_identical(
								    batch_size=2					,			test_max_difference=lowerCamelCase					,			relax_max_difference=lowerCamelCase					,			)






	def 					__snake_case      (							self :						Optional[Any]       )			->					Dict:
								__snake_case	:    Any											=     self.get_dummy_components()
								__snake_case	:    List[Any]											=     self.pipeline_class(**lowerCamelCase       )
								__snake_case	:    Tuple											=     pipe.to(lowerCamelCase       )
								pipe.set_progress_bar_config(disable=lowerCamelCase       )

								__snake_case	:    Tuple											=     1
								__snake_case	:    Optional[Any]											=     2

								__snake_case	:    Tuple											=     self.get_dummy_inputs(lowerCamelCase       )

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

								__snake_case	:    List[str]											=     pipe(**lowerCamelCase					,			num_images_per_prompt=lowerCamelCase       )[0]

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

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




	"""simple docstring"""


	def 					__snake_case      (							self :						Union[str, Any]       )			->					Tuple:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






	def 					__snake_case      (							self :						Dict       )			->					Any:
								__snake_case	:    List[Any]											=     load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/shap_e/test_shap_e_np_out.npy"       )
								__snake_case	:    List[str]											=     ShapEPipeline.from_pretrained("openai/shap-e"       )
								__snake_case	:    List[Any]											=     pipe.to(lowerCamelCase       )
								pipe.set_progress_bar_config(disable=lowerCamelCase       )

								__snake_case	:    Dict											=     torch.Generator(device=lowerCamelCase       ).manual_seed(0       )

								__snake_case	:    Tuple											=     pipe(
								    "a shark"					,			generator=lowerCamelCase					,			guidance_scale=15.0					,			num_inference_steps=64					,			frame_size=64					,			output_type="np"					,			).images[0]

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

								assert_mean_pixel_difference(lowerCamelCase					,			lowerCamelCase       )





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :	int			      =       {
    "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  :	Dict			      =       AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    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			):
							__snake_case	:    int											=     {}

							__snake_case	:    List[Any]											=     R".*sequential.(\d+).*"
							__snake_case	:    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:
																												__snake_case	:    Optional[int]											=     key.replace(__lowerCamelCase							,			__lowerCamelCase			)

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

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

																					# Because in CLAP they use `nn.Sequential`...
																					__snake_case	:    List[str]											=     1 if projecton_layer == 0 else 2

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

																					__snake_case	:    List[str]											=     mixed_qkv[:qkv_dim]
																					__snake_case	:    int											=     mixed_qkv[qkv_dim : qkv_dim * 2]
																					__snake_case	:    str											=     mixed_qkv[qkv_dim * 2 :]

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

							return model_state_dict







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    List[str]											=     init_clap(__lowerCamelCase							,			enable_fusion=__lowerCamelCase			)

							clap_model.eval()
							__snake_case	:    Union[str, Any]											=     clap_model.state_dict()
							__snake_case	:    Any											=     rename_state_dict(__lowerCamelCase			)

							__snake_case	:    Dict											=     ClapConfig()
							__snake_case	:    Dict											=     enable_fusion
							__snake_case	:    Optional[Any]											=     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__":
			_snake_case  :	Union[str, Any]			      =       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  :	Tuple			      =       parser.parse_args()

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

