

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 				lowerCamelCase__							(       __lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  ShapEImgaImgPipeline
	_A			=  ['image']
	_A			=  ['image']
	_A			=  [
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	_A			=  False







	@property
	def      _lowerCamelCase       (      self	:List[str]		)	->				Dict:
								return 3_2







	@property
	def      _lowerCamelCase       (      self	:Tuple		)	->				Tuple:
								return 3_2







	@property
	def      _lowerCamelCase       (      self	:str		)	->				Tuple:
								return self.time_input_dim * 4







	@property
	def      _lowerCamelCase       (      self	:str		)	->				Optional[int]:
								return 8







	@property
	def      _lowerCamelCase       (      self	:Dict		)	->				List[Any]:
								torch.manual_seed(0		)
								__UpperCamelCase			:     Tuple         =      CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size  ,     image_size=6_4  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     num_attention_heads=4  ,     num_channels=3  ,     num_hidden_layers=5  ,     patch_size=1  ,     )

								__UpperCamelCase			:     str         =      CLIPVisionModel(a		)
								return model







	@property
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      CLIPImageProcessor(
								    crop_size=2_2_4  ,     do_center_crop=a  ,     do_normalize=a  ,     do_resize=a  ,     image_mean=[0.48145466, 0.4578275, 0.40821073]  ,     image_std=[0.26862954, 0.26130258, 0.27577711]  ,     resample=3  ,     size=2_2_4  ,     )

								return image_processor







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

								__UpperCamelCase			:     Tuple         =      {
								    "num_attention_heads": 2,
								    "attention_head_dim": 1_6,
								    "embedding_dim": self.time_input_dim,
								    "num_embeddings": 3_2,
								    "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,
								}

								__UpperCamelCase			:     Union[str, Any]         =      PriorTransformer(**a		)
								return model







	@property
	def      _lowerCamelCase       (      self	:str		)	->				Tuple:
								torch.manual_seed(0		)

								__UpperCamelCase			:     Optional[Any]         =      {
								    "param_shapes": (
								        (self.renderer_dim, 9_3),
								        (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": 1_2,
								    "background": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								__UpperCamelCase			:     Any         =      ShapERenderer(**a		)
								return model







	def      _lowerCamelCase       (      self	:int		)	->				Dict:
								__UpperCamelCase			:     Optional[int]         =      self.dummy_prior
								__UpperCamelCase			:     Tuple         =      self.dummy_image_encoder
								__UpperCamelCase			:     List[str]         =      self.dummy_image_processor
								__UpperCamelCase			:     Optional[Any]         =      self.dummy_renderer

								__UpperCamelCase			:     str         =      HeunDiscreteScheduler(
								    beta_schedule="exp"  ,     num_train_timesteps=1_0_2_4  ,     prediction_type="sample"  ,     use_karras_sigmas=a  ,     clip_sample=a  ,     clip_sample_range=1.0  ,     )
								__UpperCamelCase			:     List[Any]         =      {
								    "prior": prior,
								    "image_encoder": image_encoder,
								    "image_processor": image_processor,
								    "renderer": renderer,
								    "scheduler": scheduler,
								}

								return components







	def      _lowerCamelCase       (      self	:int  ,     a	:List[str]  ,     a	:List[Any]=0		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      floats_tensor((1, 3, 6_4, 6_4)  ,     rng=random.Random(a		)		).to(a		)

								if str(a		).startswith("mps"		):
															__UpperCamelCase			:     str         =      torch.manual_seed(a		)
								else:
															__UpperCamelCase			:     str         =      torch.Generator(device=a		).manual_seed(a		)
								__UpperCamelCase			:     Any         =      {
								    "image": input_image,
								    "generator": generator,
								    "num_inference_steps": 1,
								    "frame_size": 3_2,
								    "output_type": "np",
								}
								return inputs







	def      _lowerCamelCase       (      self	:Any		)	->				Optional[int]:
								__UpperCamelCase			:     str         =      "cpu"

								__UpperCamelCase			:     Optional[Any]         =      self.get_dummy_components()

								__UpperCamelCase			:     Union[str, Any]         =      self.pipeline_class(**a		)
								__UpperCamelCase			:     List[Any]         =      pipe.to(a		)

								pipe.set_progress_bar_config(disable=a		)

								__UpperCamelCase			:     List[str]         =      pipe(**self.get_dummy_inputs(a		)		)
								__UpperCamelCase			:     int         =      output.images[0]
								__UpperCamelCase			:     List[str]         =      image[0, -3:, -3:, -1]

								assert image.shape == (2_0, 3_2, 3_2, 3)

								__UpperCamelCase			:     Optional[int]         =      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      _lowerCamelCase       (      self	:str		)	->				Dict:
								# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
								self._test_inference_batch_consistent(batch_sizes=[1, 2]		)







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Union[str, Any]:
								__UpperCamelCase			:     List[Any]         =      torch_device == "cpu"
								__UpperCamelCase			:     Any         =      True
								self._test_inference_batch_single_identical(
								    batch_size=2  ,     test_max_difference=a  ,     relax_max_difference=a  ,     )







	def      _lowerCamelCase       (      self	:List[str]		)	->				Any:
								__UpperCamelCase			:     Dict         =      self.get_dummy_components()
								__UpperCamelCase			:     Optional[Any]         =      self.pipeline_class(**a		)
								__UpperCamelCase			:     Any         =      pipe.to(a		)
								pipe.set_progress_bar_config(disable=a		)

								__UpperCamelCase			:     Union[str, Any]         =      1
								__UpperCamelCase			:     Optional[Any]         =      2

								__UpperCamelCase			:     List[Any]         =      self.get_dummy_inputs(a		)

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

								__UpperCamelCase			:     Any         =      pipe(**a  ,     num_images_per_prompt=a		)[0]

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




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




	'''simple docstring'''




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







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

								__UpperCamelCase			:     Tuple         =      torch.Generator(device=a		).manual_seed(0		)

								__UpperCamelCase			:     Union[str, Any]         =      pipe(
								    a  ,     generator=a  ,     guidance_scale=3.0  ,     num_inference_steps=6_4  ,     frame_size=6_4  ,     output_type="np"  ,     ).images[0]

								assert images.shape == (2_0, 6_4, 6_4, 3)

								assert_mean_pixel_difference(a  ,     a		)

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase						:      List[str]									= argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase						:      int									= parser.parse_args()

		lowercase						:      str									= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase						:      int									= CLIPImageProcessor()
		lowercase						:      Optional[int]									= CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase						:      int									= UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)