



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 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							ShapEPipeline
	__A							=							["prompt"]
	__A							=							["prompt"]
	__A							=							[
	    "num_images_per_prompt",
	    "num_inference_steps",
	    "generator",
	    "latents",
	    "guidance_scale",
	    "frame_size",
	    "output_type",
	    "return_dict",
	]
	__A							=							False




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 32




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 32




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.time_input_dim * 4




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 8




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
						return tokenizer




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.manual_seed(0		)
						a					     =   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-0_5		,       num_attention_heads=4		,       num_hidden_layers=5		,       pad_token_id=1		,       vocab_size=1000		,       )
						return CLIPTextModelWithProjection(lowerCamelCase_		)




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.manual_seed(0		)

						a					     =   {
						    "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,
						}

						a					     =   PriorTransformer(**lowerCamelCase_		)
						return model




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.manual_seed(0		)

						a					     =   {
						    "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,
						    ),
						}
						a					     =   ShapERenderer(**lowerCamelCase_		)
						return model




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.dummy_prior
						a					     =   self.dummy_text_encoder
						a					     =   self.dummy_tokenizer
						a					     =   self.dummy_renderer

						a					     =   HeunDiscreteScheduler(
						    beta_schedule="exp"		,       num_train_timesteps=1024		,       prediction_type="sample"		,       use_karras_sigmas=lowerCamelCase_		,       clip_sample=lowerCamelCase_		,       clip_sample_range=1.0		,       )
						a					     =   {
						    "prior": prior,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "renderer": renderer,
						    "scheduler": scheduler,
						}

						return components




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0		):
						"""simple docstring"""

						if str(lowerCamelCase_		).startswith("mps"		):
											a					     =   torch.manual_seed(lowerCamelCase_		)
						else:
											a					     =   torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)
						a					     =   {
						    "prompt": "horse",
						    "generator": generator,
						    "num_inference_steps": 1,
						    "frame_size": 32,
						    "output_type": "np",
						}
						return inputs




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "cpu"

						a					     =   self.get_dummy_components()

						a					     =   self.pipeline_class(**lowerCamelCase_		)
						a					     =   pipe.to(lowerCamelCase_		)

						pipe.set_progress_bar_config(disable=lowerCamelCase_		)

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

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

						a					     =   np.array(
						    [
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						    ]		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self._test_inference_batch_consistent(batch_sizes=[1, 2]		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   torch_device == "cpu"
						a					     =   True

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_dummy_components()
						a					     =   self.pipeline_class(**lowerCamelCase_		)
						a					     =   pipe.to(lowerCamelCase_		)
						pipe.set_progress_bar_config(disable=lowerCamelCase_		)

						a					     =   1
						a					     =   2

						a					     =   self.get_dummy_inputs(lowerCamelCase_		)

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

						a					     =   pipe(**lowerCamelCase_		,       num_images_per_prompt=lowerCamelCase_		)[0]

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




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





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/shap_e/test_shap_e_np_out.npy"		)
						a					     =   ShapEPipeline.from_pretrained("openai/shap-e"		)
						a					     =   pipe.to(lowerCamelCase_		)
						pipe.set_progress_bar_config(disable=lowerCamelCase_		)

						a					     =   torch.Generator(device=lowerCamelCase_		).manual_seed(0		)

						a					     =   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_		)





def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					if number > 0:
										raise ValueError("input must be a negative integer"       )
					a					     =   len(bin(A       )[3:]       )
					a					     =   bin(abs(A       ) - (1 << binary_number_length)       )[3:]
					a					     =   (
					    (
					        "1"
					        + "0" * (binary_number_length - len(A       ))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()
