'''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 					__magic_name__		(					_UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = ShapEImgaImgPipeline
			UpperCamelCase__       = ['''image''']
			UpperCamelCase__       = ['''image''']
			UpperCamelCase__       = [
			    '''num_images_per_prompt''',
			    '''num_inference_steps''',
			    '''generator''',
			    '''latents''',
			    '''guidance_scale''',
			    '''frame_size''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase__       = False







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return 32







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										return 32







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										return self.time_input_dim * 4







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										return 8







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										torch.manual_seed(0					)
										lowercase_      :    Tuple       		= 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       ,    )

										lowercase_      :    Tuple       		= CLIPVisionModel(lowercase_					)
										return model







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Optional[int]       		= CLIPImageProcessor(
										    crop_size=224       ,    do_center_crop=lowercase_       ,    do_normalize=lowercase_       ,    do_resize=lowercase_       ,    image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,    image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,    resample=3       ,    size=224       ,    )

										return image_processor







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										torch.manual_seed(0					)

										lowercase_      :    Optional[Any]       		= {
										    """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,
										}

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







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										torch.manual_seed(0					)

										lowercase_      :    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,
										    ),
										}
										lowercase_      :    int       		= ShapERenderer(**lowercase_					)
										return model







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[str]       		= self.dummy_prior
										lowercase_      :    Union[str, Any]       		= self.dummy_image_encoder
										lowercase_      :    Union[str, Any]       		= self.dummy_image_processor
										lowercase_      :    List[Any]       		= self.dummy_renderer

										lowercase_      :    List[str]       		= HeunDiscreteScheduler(
										    beta_schedule="""exp"""       ,    num_train_timesteps=1024       ,    prediction_type="""sample"""       ,    use_karras_sigmas=lowercase_       ,    clip_sample=lowercase_       ,    clip_sample_range=1.0       ,    )
										lowercase_      :    int       		= {
										    """prior""": prior,
										    """image_encoder""": image_encoder,
										    """image_processor""": image_processor,
										    """renderer""": renderer,
										    """scheduler""": scheduler,
										}

										return components







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						List[Any]       ,    lowercase_       :						List[str]=0					):
										lowercase_      :    str       		= floats_tensor((1, 3, 64, 64)       ,    rng=random.Random(lowercase_					)					).to(lowercase_					)

										if str(lowercase_					).startswith("""mps"""					):
																	lowercase_      :    int       		= torch.manual_seed(lowercase_					)
										else:
																	lowercase_      :    Dict       		= torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
										lowercase_      :    int       		= {
										    """image""": input_image,
										    """generator""": generator,
										    """num_inference_steps""": 1,
										    """frame_size""": 32,
										    """output_type""": """np""",
										}
										return inputs







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    Optional[Any]       		= """cpu"""

										lowercase_      :    Union[str, Any]       		= self.get_dummy_components()

										lowercase_      :    Optional[Any]       		= self.pipeline_class(**lowercase_					)
										lowercase_      :    Tuple       		= pipe.to(lowercase_					)

										pipe.set_progress_bar_config(disable=lowercase_					)

										lowercase_      :    int       		= pipe(**self.get_dummy_inputs(lowercase_					)					)
										lowercase_      :    Union[str, Any]       		= output.images[0]
										lowercase_      :    Tuple       		= image[0, -3:, -3:, -1]

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

										lowercase_      :    Any       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						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 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Union[str, Any]       		= torch_device == """cpu"""
										lowercase_      :    int       		= True
										self._test_inference_batch_single_identical(
										    batch_size=2       ,    test_max_difference=lowercase_       ,    relax_max_difference=lowercase_       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Optional[int]       		= self.get_dummy_components()
										lowercase_      :    Any       		= self.pipeline_class(**lowercase_					)
										lowercase_      :    Optional[int]       		= pipe.to(lowercase_					)
										pipe.set_progress_bar_config(disable=lowercase_					)

										lowercase_      :    List[str]       		= 1
										lowercase_      :    Optional[Any]       		= 2

										lowercase_      :    Optional[Any]       		= self.get_dummy_inputs(lowercase_					)

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

										lowercase_      :    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 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







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

										lowercase_      :    Optional[Any]       		= torch.Generator(device=lowercase_					).manual_seed(0					)

										lowercase_      :    List[Any]       		= 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_					)






'''simple docstring'''




import argparse


_lowercase		:				Optional[int]	       =		"docs/source/_static/js/custom.js"




def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple				)    ->   Dict:
							with open(UpperCAmelCase__   ,   encoding="""utf-8"""   ,   newline="""\n"""				) as f:
														lowercase_      :    Optional[int]       		= f.readlines()
							lowercase_      :    Tuple       		= 0

							# First let's put the right version
							while not lines[index].startswith("""const stableVersion ="""				):
														index += 1
							lowercase_      :    Optional[Any]       		= F'''const stableVersion = "v{version}"\n'''

							# Then update the dictionary
							while not lines[index].startswith("""const versionMapping = {"""				):
														index += 1

							# We go until the end
							while not lines[index].startswith("""}"""				):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

							with open(UpperCAmelCase__   ,   """w"""   ,   encoding="""utf-8"""   ,   newline="""\n"""				) as f:
														f.writelines(UpperCAmelCase__				)


if __name__ == "__main__":
						_lowercase		:				Union[str, Any]	       =		argparse.ArgumentParser()
						parser.add_argument("--version", help="Release version.")
						_lowercase		:				Dict	       =		parser.parse_args()
						update_custom_js(args.version)





