


"""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_(       a__							,					unittest.TestCase			):
     __UpperCamelCase  =					ShapEImgaImgPipeline
     __UpperCamelCase  =					['''image''']
     __UpperCamelCase  =					['''image''']
     __UpperCamelCase  =					[
         '''num_images_per_prompt''',
         '''num_inference_steps''',
         '''generator''',
         '''latents''',
         '''guidance_scale''',
         '''frame_size''',
         '''output_type''',
         '''return_dict''',
     ]
     __UpperCamelCase  =					False






     @property
     def      lowerCamelCase__     (	self  :					Dict				):
           return 3_2






     @property
     def      lowerCamelCase__     (	self  :					List[str]				):
           return 3_2






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






     @property
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           return 8






     @property
     def      lowerCamelCase__     (	self  :					Dict				):
           torch.manual_seed(0				)
           lowerCAmelCase					:	Tuple       =   CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size					,				image_size=6_4					,				projection_dim=self.text_embedder_hidden_size					,				intermediate_size=3_7					,				num_attention_heads=4					,				num_channels=3					,				num_hidden_layers=5					,				patch_size=1					,				)

           lowerCAmelCase					:	Optional[Any]       =   CLIPVisionModel(UpperCamelCase_				)
           return model






     @property
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	List[str]       =   CLIPImageProcessor(
               crop_size=2_2_4					,				do_center_crop=UpperCamelCase_					,				do_normalize=UpperCamelCase_					,				do_resize=UpperCamelCase_					,				image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,				image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,				resample=3					,				size=2_2_4					,				)

           return image_processor






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

           lowerCAmelCase					:	List[str]       =   {
               '''num_attention_heads''': 2,
               '''attention_head_dim''': 1_6,
               '''embedding_dim''': self.time_input_dim,
               '''num_embeddings''': 3_2,
               '''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,
           }

           lowerCAmelCase					:	List[Any]       =   PriorTransformer(**UpperCamelCase_				)
           return model






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

           lowerCAmelCase					:	Union[str, Any]       =   {
               '''param_shapes''': (
                   (self.renderer_dim, 9_3),
                   (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''': 1_2,
               '''background''': (
                   0.1,
                   0.1,
                   0.1,
               ),
           }
           lowerCAmelCase					:	Any       =   ShapERenderer(**UpperCamelCase_				)
           return model






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Optional[int]       =   self.dummy_prior
           lowerCAmelCase					:	str       =   self.dummy_image_encoder
           lowerCAmelCase					:	Optional[Any]       =   self.dummy_image_processor
           lowerCAmelCase					:	Tuple       =   self.dummy_renderer

           lowerCAmelCase					:	List[str]       =   HeunDiscreteScheduler(
               beta_schedule='''exp'''					,				num_train_timesteps=1_0_2_4					,				prediction_type='''sample'''					,				use_karras_sigmas=UpperCamelCase_					,				clip_sample=UpperCamelCase_					,				clip_sample_range=1.0					,				)
           lowerCAmelCase					:	Optional[Any]       =   {
               '''prior''': prior,
               '''image_encoder''': image_encoder,
               '''image_processor''': image_processor,
               '''renderer''': renderer,
               '''scheduler''': scheduler,
           }

           return components






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[int]=0				):
           lowerCAmelCase					:	Any       =   floats_tensor((1, 3, 6_4, 6_4)					,				rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)

           if str(UpperCamelCase_				).startswith('''mps'''				):
                 lowerCAmelCase					:	str       =   torch.manual_seed(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Optional[int]       =   torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
           lowerCAmelCase					:	int       =   {
               '''image''': input_image,
               '''generator''': generator,
               '''num_inference_steps''': 1,
               '''frame_size''': 3_2,
               '''output_type''': '''np''',
           }
           return inputs






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	int       =   '''cpu'''

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

           lowerCAmelCase					:	str       =   self.pipeline_class(**UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   pipe.to(UpperCamelCase_				)

           pipe.set_progress_bar_config(disable=UpperCamelCase_				)

           lowerCAmelCase					:	int       =   pipe(**self.get_dummy_inputs(UpperCamelCase_				)				)
           lowerCAmelCase					:	Tuple       =   output.images[0]
           lowerCAmelCase					:	List[Any]       =   image[0, -3:, -3:, -1]

           assert image.shape == (2_0, 3_2, 3_2, 3)

           lowerCAmelCase					:	List[Any]       =   np.array(
               [
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
                   0.00_039_216,
               ]				)

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






     def      lowerCamelCase__     (	self  :					List[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  :					Union[str, Any]				):
           lowerCAmelCase					:	List[Any]       =   torch_device == '''cpu'''
           lowerCAmelCase					:	List[str]       =   True
           self._test_inference_batch_single_identical(
               batch_size=2					,				test_max_difference=UpperCamelCase_					,				relax_max_difference=UpperCamelCase_					,				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Union[str, Any]       =   self.get_dummy_components()
           lowerCAmelCase					:	int       =   self.pipeline_class(**UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   pipe.to(UpperCamelCase_				)
           pipe.set_progress_bar_config(disable=UpperCamelCase_				)

           lowerCAmelCase					:	Dict       =   1
           lowerCAmelCase					:	List[Any]       =   2

           lowerCAmelCase					:	Union[str, Any]       =   self.get_dummy_inputs(UpperCamelCase_				)

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

           lowerCAmelCase					:	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  :					List[Any]				):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	Dict       =   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''				)
           lowerCAmelCase					:	Union[str, Any]       =   load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/shap_e/test_shap_e_img2img_out.npy'''				)
           lowerCAmelCase					:	int       =   ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''				)
           lowerCAmelCase					:	Any       =   pipe.to(UpperCamelCase_				)
           pipe.set_progress_bar_config(disable=UpperCamelCase_				)

           lowerCAmelCase					:	Any       =   torch.Generator(device=UpperCamelCase_				).manual_seed(0				)

           lowerCAmelCase					:	str       =   pipe(
               UpperCamelCase_					,				generator=UpperCamelCase_					,				guidance_scale=3.0					,				num_inference_steps=6_4					,				frame_size=6_4					,				output_type='''np'''					,				).images[0]

           assert images.shape == (2_0, 6_4, 6_4, 3)

           assert_mean_pixel_difference(UpperCamelCase_					,				UpperCamelCase_				)






"""simple docstring"""





# using dfs for finding eulerian path traversal


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			List[Any] ,   _snake_case				:			str ,   _snake_case				:			List[Any]=None			):
      lowerCAmelCase					:	Any       =   (path or []) + [u]
      for v in graph[u]:
            if visited_edge[u][v] is False:
                  lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   True, True
                  lowerCAmelCase					:	int       =   dfs(_snake_case ,   _snake_case ,   _snake_case ,   _snake_case			)
      return path


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			Dict			):
      lowerCAmelCase					:	Tuple       =   0
      lowerCAmelCase					:	Optional[Any]       =   -1
      for i in range(_snake_case			):
            if i not in graph.keys():
                  continue
            if len(graph[i]			) % 2 == 1:
                  odd_degree_nodes += 1
                  lowerCAmelCase					:	Optional[Any]       =   i
      if odd_degree_nodes == 0:
            return 1, odd_node
      if odd_degree_nodes == 2:
            return 2, odd_node
      return 3, odd_node


def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			List[Any]			):
      lowerCAmelCase					:	Any       =   [[False for _ in range(max_node + 1			)] for _ in range(max_node + 1			)]
      lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   check_circuit_or_path(_snake_case ,   _snake_case			)
      if check == 3:
            print('''graph is not Eulerian'''			)
            print('''no path'''			)
            return
      lowerCAmelCase					:	Dict       =   1
      if check == 2:
            lowerCAmelCase					:	int       =   odd_node
            print('''graph has a Euler path'''			)
      if check == 1:
            print('''graph has a Euler cycle'''			)
      lowerCAmelCase					:	List[str]       =   dfs(_snake_case ,   _snake_case ,   _snake_case			)
      print(_snake_case			)


def      _snake_case						(						):
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
      lowerCAmelCase					:	Union[str, Any]       =   {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
      lowerCAmelCase					:	List[Any]       =   {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3], 2: [1, 3], 3: [1, 2]}
      lowerCAmelCase					:	Any       =   {
          1: [],
          2: []
          # all degree is zero
      }
      lowerCAmelCase					:	List[str]       =   10
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)


if __name__ == "__main__":
 main()


