


import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
   import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin


@require_torch
class 	_a							(      unittest.TestCase					,     UpperCamelCase__      ):

       def 				lowerCamelCase_			(							self:     Dict							)      ->       Tuple:





         """simple docstring"""







         lowercase__						=				load_tool('''text-to-speech'''							)
         self.tool.setup()

       def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Optional[int]:





         """simple docstring"""







         torch.manual_seed(0							)
         lowercase__						=				self.tool('''hey'''							)
         lowercase__						=				result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]   ,						torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485]							)   ,						)							)


       def 				lowerCamelCase_			(							self:     str							)      ->       Optional[int]:





         """simple docstring"""







         torch.manual_seed(0							)
         lowercase__						=				self.tool('''hey'''							)
         lowercase__						=				result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]   ,						torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485]							)   ,						)							)






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 	_a							(      UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      Union[str, Any]  				=			ShapEImgaImgPipeline
							_lowercase     :      Optional[Any]  				=			['''image''']
							_lowercase     :      Optional[int]  				=			['''image''']
							_lowercase     :      Optional[int]  				=			[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							_lowercase     :      Tuple  				=			False

							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       int:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     str							)      ->       List[str]:





									"""simple docstring"""







									return self.time_input_dim * 4

							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       str:





									"""simple docstring"""







									return 8

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Dict:





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__						=				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__						=				CLIPVisionModel(UpperCamelCase_							)
									return model

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				CLIPImageProcessor(
									    crop_size=224   ,						do_center_crop=UpperCamelCase_   ,						do_normalize=UpperCamelCase_   ,						do_resize=UpperCamelCase_   ,						image_mean=[0.48145466, 0.4578275, 0.40821073]   ,						image_std=[0.26862954, 0.26130258, 0.27577711]   ,						resample=3   ,						size=224   ,						)

									return image_processor

							@property
							def 				lowerCamelCase_			(							self:     List[str]							)      ->       str:





									"""simple docstring"""







									torch.manual_seed(0							)

									lowercase__						=				{
									    '''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__						=				PriorTransformer(**UpperCamelCase_							)
									return model

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       List[Any]:





									"""simple docstring"""







									torch.manual_seed(0							)

									lowercase__						=				{
									    '''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__						=				ShapERenderer(**UpperCamelCase_							)
									return model

							def 				lowerCamelCase_			(							self:     str							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				self.dummy_prior
									lowercase__						=				self.dummy_image_encoder
									lowercase__						=				self.dummy_image_processor
									lowercase__						=				self.dummy_renderer

									lowercase__						=				HeunDiscreteScheduler(
									    beta_schedule='''exp'''   ,						num_train_timesteps=1_024   ,						prediction_type='''sample'''   ,						use_karras_sigmas=UpperCamelCase_   ,						clip_sample=UpperCamelCase_   ,						clip_sample_range=1.0   ,						)
									lowercase__						=				{
									    '''prior''': prior,
									    '''image_encoder''': image_encoder,
									    '''image_processor''': image_processor,
									    '''renderer''': renderer,
									    '''scheduler''': scheduler,
									}

									return components

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Optional[int]=0							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)

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

							def 				lowerCamelCase_			(							self:     int							)      ->       str:





									"""simple docstring"""







									lowercase__						=				'''cpu'''

									lowercase__						=				self.get_dummy_components()

									lowercase__						=				self.pipeline_class(**UpperCamelCase_							)
									lowercase__						=				pipe.to(UpperCamelCase_							)

									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

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

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

									lowercase__						=				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:     int							)      ->       int:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				torch_device == '''cpu'''
									lowercase__						=				True
									self._test_inference_batch_single_identical(
									    batch_size=2   ,						test_max_difference=UpperCamelCase_   ,						relax_max_difference=UpperCamelCase_   ,						)


							def 				lowerCamelCase_			(							self:     List[str]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				self.get_dummy_components()
									lowercase__						=				self.pipeline_class(**UpperCamelCase_							)
									lowercase__						=				pipe.to(UpperCamelCase_							)
									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				1
									lowercase__						=				2

									lowercase__						=				self.get_dummy_inputs(UpperCamelCase_							)

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

									lowercase__						=				pipe(**UpperCamelCase_   ,						num_images_per_prompt=UpperCamelCase_							)[0]

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


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

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Optional[Any]:





									"""simple docstring"""







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


							def 				lowerCamelCase_			(							self:     str							)      ->       str:





									"""simple docstring"""







									lowercase__						=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''							)
									lowercase__						=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/shap_e/test_shap_e_img2img_out.npy'''							)
									lowercase__						=				ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''							)
									lowercase__						=				pipe.to(UpperCamelCase_							)
									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				torch.Generator(device=UpperCamelCase_							).manual_seed(0							)

									lowercase__						=				pipe(
									    UpperCamelCase_   ,						generator=UpperCamelCase_   ,						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(UpperCamelCase_   ,						UpperCamelCase_							)


