"""simple docstring"""





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  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			ShapEPipeline
						snake_case__			  =			["prompt"]
						snake_case__			  =			["prompt"]
						snake_case__			  =			[
						    "num_images_per_prompt",
						    "num_inference_steps",
						    "generator",
						    "latents",
						    "guidance_scale",
						    "frame_size",
						    "output_type",
						    "return_dict",
						]
						snake_case__			  =			False






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return 32






						@property
						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													return 32






						@property
						def       lowerCamelCase__     (       self      :			int    ):
													return self.time_input_dim * 4






						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													return 8






						@property
						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Dict		   =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
													return tokenizer






						@property
						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													torch.manual_seed(0    )
													__lowerCamelCase					:		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(UpperCAmelCase    )






						@property
						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													torch.manual_seed(0    )

													__lowerCamelCase					:		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",
													    "encoder_hid_proj_type": None,
													    "added_emb_type": None,
													}

													__lowerCamelCase					:		str		   =     PriorTransformer(**UpperCAmelCase    )
													return model






						@property
						def       lowerCamelCase__     (       self      :			Dict    ):
													torch.manual_seed(0    )

													__lowerCamelCase					:		List[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,
													    ),
													}
													__lowerCamelCase					:		List[str]		   =     ShapERenderer(**UpperCAmelCase    )
													return model






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Tuple		   =     self.dummy_prior
													__lowerCamelCase					:		List[Any]		   =     self.dummy_text_encoder
													__lowerCamelCase					:		int		   =     self.dummy_tokenizer
													__lowerCamelCase					:		Optional[int]		   =     self.dummy_renderer

													__lowerCamelCase					:		Optional[Any]		   =     HeunDiscreteScheduler(
													    beta_schedule="exp"							,					num_train_timesteps=1024							,					prediction_type="sample"							,					use_karras_sigmas=UpperCAmelCase							,					clip_sample=UpperCAmelCase							,					clip_sample_range=1.0							,					)
													__lowerCamelCase					:		Optional[Any]		   =     {
													    "prior": prior,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "renderer": renderer,
													    "scheduler": scheduler,
													}

													return components






						def       lowerCamelCase__     (       self      :			List[Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			str=0    ):
													if str(UpperCAmelCase    ).startswith("mps"    ):
																				__lowerCamelCase					:		Tuple		   =     torch.manual_seed(UpperCAmelCase    )
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     {
													    "prompt": "horse",
													    "generator": generator,
													    "num_inference_steps": 1,
													    "frame_size": 32,
													    "output_type": "np",
													}
													return inputs






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Any		   =     "cpu"

													__lowerCamelCase					:		Dict		   =     self.get_dummy_components()

													__lowerCamelCase					:		Optional[Any]		   =     self.pipeline_class(**UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     pipe.to(UpperCAmelCase    )

													pipe.set_progress_bar_config(disable=UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     pipe(**self.get_dummy_inputs(UpperCAmelCase    )    )
													__lowerCamelCase					:		Tuple		   =     output.images[0]
													__lowerCamelCase					:		int		   =     image[0, -3:, -3:, -1]

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

													__lowerCamelCase					:		Union[str, Any]		   =     np.array(
													    [
													        0.0_0_0_3_9_2_1_6,
													        0.0_0_0_3_9_2_1_6,
													        0.0_0_0_3_9_2_1_6,
													        0.0_0_0_3_9_2_1_6,
													        0.0_0_0_3_9_2_1_6,
													        0.0_0_0_3_9_2_1_6,
													        0.0_0_0_3_9_2_1_6,
													        0.0_0_0_3_9_2_1_6,
													        0.0_0_0_3_9_2_1_6,
													    ]    )

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






						def       lowerCamelCase__     (       self      :			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       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		str		   =     torch_device == "cpu"
													__lowerCamelCase					:		Union[str, Any]		   =     True

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







						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		str		   =     self.get_dummy_components()
													__lowerCamelCase					:		Optional[int]		   =     self.pipeline_class(**UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     pipe.to(UpperCAmelCase    )
													pipe.set_progress_bar_config(disable=UpperCAmelCase    )

													__lowerCamelCase					:		int		   =     1
													__lowerCamelCase					:		List[str]		   =     2

													__lowerCamelCase					:		List[Any]		   =     self.get_dummy_inputs(UpperCAmelCase    )

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

													__lowerCamelCase					:		Optional[int]		   =     pipe(**UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase    )[0]

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

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






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







						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Any		   =     load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/shap_e/test_shap_e_np_out.npy"    )
													__lowerCamelCase					:		Optional[int]		   =     ShapEPipeline.from_pretrained("openai/shap-e"    )
													__lowerCamelCase					:		Union[str, Any]		   =     pipe.to(UpperCAmelCase    )
													pipe.set_progress_bar_config(disable=UpperCAmelCase    )

													__lowerCamelCase					:		int		   =     torch.Generator(device=UpperCAmelCase    ).manual_seed(0    )

													__lowerCamelCase					:		Tuple		   =     pipe(
													    "a shark"							,					generator=UpperCAmelCase							,					guidance_scale=1_5.0							,					num_inference_steps=64							,					frame_size=64							,					output_type="np"							,					).images[0]

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

													assert_mean_pixel_difference(UpperCAmelCase							,					UpperCAmelCase    )
"""simple docstring"""





import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class  _snake_case     :
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			True
						snake_case__			  =			None
						snake_case__			  =			1
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None






						def       lowerCamelCase__     (       self      :			Any    ):
													return self.__class__(**{k: copy.deepcopy(UpperCAmelCase    ) for k, v in self.__dict__.items()}    )