



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 SCREAMING_SNAKE_CASE__	(						_snake_case					,		unittest.TestCase       ):




    '''simple docstring'''






    __lowerCamelCase     :					Dict   = ShapEImgaImgPipeline
    __lowerCamelCase     :					Optional[int]   = ["image"]
    __lowerCamelCase     :					Tuple   = ["image"]
    __lowerCamelCase     :					int   = [
        "num_images_per_prompt",
        "num_inference_steps",
        "generator",
        "latents",
        "guidance_scale",
        "frame_size",
        "output_type",
        "return_dict",
    ]
    __lowerCamelCase     :					Any   = False






    @property
    def      _lowerCAmelCase   (			self						):
        return 32






    @property
    def      _lowerCAmelCase   (			self						):
        return 32






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






    @property
    def      _lowerCAmelCase   (			self						):
        return 8






    @property
    def      _lowerCAmelCase   (			self						):
        torch.manual_seed(0						)
        A					:			Dict		=  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(_lowerCamelCase						)
        return model






    @property
    def      _lowerCAmelCase   (			self						):
        A					:			Tuple		=  CLIPImageProcessor(
            crop_size=224,						do_center_crop=_lowerCamelCase,						do_normalize=_lowerCamelCase,						do_resize=_lowerCamelCase,						image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073],						image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711],						resample=3,						size=224,						)

        return image_processor






    @property
    def      _lowerCAmelCase   (			self						):
        torch.manual_seed(0						)

        A					:			Dict		=  {
            '''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					:			int		=  PriorTransformer(**_lowerCamelCase						)
        return model






    @property
    def      _lowerCAmelCase   (			self						):
        torch.manual_seed(0						)

        A					:			int		=  {
            '''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					:			List[str]		=  ShapERenderer(**_lowerCamelCase						)
        return model






    def      _lowerCAmelCase   (			self						):
        A					:			Dict		=  self.dummy_prior
        A					:			Optional[int]		=  self.dummy_image_encoder
        A					:			Optional[int]		=  self.dummy_image_processor
        A					:			int		=  self.dummy_renderer

        A					:			Optional[int]		=  HeunDiscreteScheduler(
            beta_schedule="""exp""",						num_train_timesteps=1024,						prediction_type="""sample""",						use_karras_sigmas=_lowerCamelCase,						clip_sample=_lowerCamelCase,						clip_sample_range=1.0,						)
        A					:			int		=  {
            '''prior''': prior,
            '''image_encoder''': image_encoder,
            '''image_processor''': image_processor,
            '''renderer''': renderer,
            '''scheduler''': scheduler,
        }

        return components






    def      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__=0						):
        A					:			Any		=  floats_tensor((1, 3, 64, 64),						rng=random.Random(_lowerCamelCase						)						).to(_lowerCamelCase						)

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






    def      _lowerCAmelCase   (			self						):
        A					:			Dict		=  '''cpu'''

        A					:			List[str]		=  self.get_dummy_components()

        A					:			Any		=  self.pipeline_class(**_lowerCamelCase						)
        A					:			Optional[int]		=  pipe.to(_lowerCamelCase						)

        pipe.set_progress_bar_config(disable=_lowerCamelCase						)

        A					:			int		=  pipe(**self.get_dummy_inputs(_lowerCamelCase						)						)
        A					:			Any		=  output.images[0]
        A					:			List[str]		=  image[0, -3:, -3:, -1]

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

        A					:			Tuple		=  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      _lowerCAmelCase   (			self						):
        # 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						):
        A					:			Optional[Any]		=  torch_device == '''cpu'''
        A					:			List[Any]		=  True
        self._test_inference_batch_single_identical(
            batch_size=2,						test_max_difference=_lowerCamelCase,						relax_max_difference=_lowerCamelCase,						)






    def      _lowerCAmelCase   (			self						):
        A					:			int		=  self.get_dummy_components()
        A					:			Union[str, Any]		=  self.pipeline_class(**_lowerCamelCase						)
        A					:			str		=  pipe.to(_lowerCamelCase						)
        pipe.set_progress_bar_config(disable=_lowerCamelCase						)

        A					:			Union[str, Any]		=  1
        A					:			List[str]		=  2

        A					:			Optional[int]		=  self.get_dummy_inputs(_lowerCamelCase						)

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

        A					:			List[Any]		=  pipe(**_lowerCamelCase,						num_images_per_prompt=_lowerCamelCase						)[0]

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






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




    '''simple docstring'''






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






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

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

        A					:			int		=  pipe(
            _lowerCamelCase,						generator=_lowerCamelCase,						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(_lowerCamelCase,						_lowerCamelCase						)






snake_case							:       Any										=	{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

snake_case							:       Any										=	{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def 							__lowerCamelCase		(					UpperCAmelCase_		:						dict[int, list[int]]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						list[bool]		):




      """simple docstring"""





      a		:Any									=			True
      a		:Any									=			[]

      for neighbour in graph[vert]:
            if not visited[neighbour]:
                  order += topology_sort(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)

      order.append(UpperCAmelCase_		)

      return order
def 							__lowerCamelCase		(					UpperCAmelCase_		:						dict[int, list[int]]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						list[bool]		):




      """simple docstring"""





      a		:Optional[Any]									=			True
      a		:int									=			[vert]

      for neighbour in reversed_graph[vert]:
            if not visited[neighbour]:
                  component += find_components(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)

      return component






def 							__lowerCamelCase		(					UpperCAmelCase_		:						dict[int, list[int]]		):




      """simple docstring"""





      a		:int									=			len(UpperCAmelCase_		) * [False]
      a		:dict[int, list[int]]									=			{vert: [] for vert in range(len(UpperCAmelCase_		)		)}

      for vert, neighbours in graph.items():
            for neighbour in neighbours:
                  reversed_graph[neighbour].append(UpperCAmelCase_		)

      a		:Optional[Any]									=			[]
      for i, was_visited in enumerate(UpperCAmelCase_		):
            if not was_visited:
                  order += topology_sort(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)

      a		:Tuple									=			[]
      a		:int									=			len(UpperCAmelCase_		) * [False]

      for i in range(len(UpperCAmelCase_		)		):
            a		:Union[str, Any]									=			order[len(UpperCAmelCase_		) - i - 1]
            if not visited[vert]:
                  a		:Union[str, Any]									=			find_components(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)
                  components_list.append(UpperCAmelCase_		)

      return components_list





