"""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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      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)	->			str:





        '''simple docstring'''





        return 32



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





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        return self.time_input_dim * 4



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





        '''simple docstring'''





        return 8



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





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     str										=	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    ,			)

        a__:     List[str]										=	CLIPVisionModel(lowercase)
        return model



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





        '''simple docstring'''





        a__:     Optional[Any]										=	CLIPImageProcessor(
            crop_size=2_24    ,			do_center_crop=lowercase    ,			do_normalize=lowercase    ,			do_resize=lowercase    ,			image_mean=[0.48145466, 0.4578275, 0.40821073]    ,			image_std=[0.26862954, 0.26130258, 0.27577711]    ,			resample=3    ,			size=2_24    ,			)

        return image_processor



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





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Optional[int]										=	{
            '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,
        }

        a__:     Optional[Any]										=	PriorTransformer(**lowercase)
        return model



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





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     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,
            ),
        }
        a__:     Optional[Any]										=	ShapERenderer(**lowercase)
        return model



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





        '''simple docstring'''





        a__:     List[Any]										=	self.dummy_prior
        a__:     Optional[Any]										=	self.dummy_image_encoder
        a__:     str										=	self.dummy_image_processor
        a__:     Tuple										=	self.dummy_renderer

        a__:     int										=	HeunDiscreteScheduler(
            beta_schedule='exp'    ,			num_train_timesteps=10_24    ,			prediction_type='sample'    ,			use_karras_sigmas=lowercase    ,			clip_sample=lowercase    ,			clip_sample_range=1.0    ,			)
        a__:     str										=	{
            'prior': prior,
            'image_encoder': image_encoder,
            'image_processor': image_processor,
            'renderer': renderer,
            'scheduler': scheduler,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			int:





        '''simple docstring'''





        a__:     Optional[Any]										=	floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowercase)).to(lowercase)

        if str(lowercase).startswith('mps'):
          a__:     Any										=	torch.manual_seed(lowercase)
        else:
          a__:     Optional[Any]										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Dict										=	{
            'image': input_image,
            'generator': generator,
            'num_inference_steps': 1,
            'frame_size': 32,
            'output_type': 'np',
        }
        return inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Dict										=	'cpu'

        a__:     Dict										=	self.get_dummy_components()

        a__:     str										=	self.pipeline_class(**lowercase)
        a__:     Tuple										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Any										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images[0]
        a__:     Tuple										=	image[0, -3:, -3:, -1]

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

        a__:     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)	->			List[str]:





        '''simple docstring'''





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



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





        '''simple docstring'''





        a__:     str										=	torch_device == 'cpu'
        a__:     Dict										=	True
        self._test_inference_batch_single_identical(
            batch_size=2    ,			test_max_difference=lowercase    ,			relax_max_difference=lowercase    ,			)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Tuple										=	self.get_dummy_components()
        a__:     List[Any]										=	self.pipeline_class(**lowercase)
        a__:     List[str]										=	pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	1
        a__:     Tuple										=	2

        a__:     Tuple										=	self.get_dummy_inputs(lowercase)

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

        a__:     List[str]										=	pipe(**lowercase    ,			num_images_per_prompt=lowercase)[0]

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







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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png')
        a__:     Any										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/shap_e/test_shap_e_img2img_out.npy')
        a__:     List[str]										=	ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img')
        a__:     List[str]										=	pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Tuple										=	torch.Generator(device=lowercase).manual_seed(0)

        a__:     Optional[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 os
from typing import Dict, List, Tuple, TypeVar, Union


lowercase__					       =	TypeVar('T')

lowercase__					       =	Union[List[T], Tuple[T, ...]]
lowercase__					       =	Union[T, List[T], Dict[str, T]]
lowercase__					       =	Union[str, bytes, os.PathLike]


