



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 							lowerCAmelCase_						(    a__			,       unittest.TestCase				):
       UpperCAmelCase__							:   List[Any]  	=    ShapEImgaImgPipeline
       UpperCAmelCase__							:   str  	=    ["image"]
       UpperCAmelCase__							:   int  	=    ["image"]
       UpperCAmelCase__							:   Optional[int]  	=    [
           "num_images_per_prompt",
           "num_inference_steps",
           "generator",
           "latents",
           "guidance_scale",
           "frame_size",
           "output_type",
           "return_dict",
       ]
       UpperCAmelCase__							:   Tuple  	=    False
       @property
       def    snake_case_			(     self			)	->						Union[str, Any]:
          return 32
       @property
       def    snake_case_			(     self			)	->						str:
          return 32
       @property
       def    snake_case_			(     self			)	->						int:
          return self.time_input_dim * 4
       @property
       def    snake_case_			(     self			)	->						Optional[int]:
          return 8
       @property
       def    snake_case_			(     self			)	->						Optional[int]:
          torch.manual_seed(0			)
          UpperCamelCase       :    Union[str, Any]		      =      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,     )

          UpperCamelCase       :    List[Any]		      =      CLIPVisionModel(SCREAMING_SNAKE_CASE_			)
          return model
       @property
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Dict		      =      CLIPImageProcessor(
              crop_size=224,     do_center_crop=SCREAMING_SNAKE_CASE_,     do_normalize=SCREAMING_SNAKE_CASE_,     do_resize=SCREAMING_SNAKE_CASE_,     image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],     image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],     resample=3,     size=224,     )

          return image_processor
       @property
       def    snake_case_			(     self			)	->						Any:
          torch.manual_seed(0			)

          UpperCamelCase       :    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,
          }

          UpperCamelCase       :    Any		      =      PriorTransformer(**SCREAMING_SNAKE_CASE_			)
          return model
       @property
       def    snake_case_			(     self			)	->						Optional[int]:
          torch.manual_seed(0			)

          UpperCamelCase       :    Dict		      =      {
              '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,
              ),
          }
          UpperCamelCase       :    List[Any]		      =      ShapERenderer(**SCREAMING_SNAKE_CASE_			)
          return model
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    Dict		      =      self.dummy_prior
          UpperCamelCase       :    Tuple		      =      self.dummy_image_encoder
          UpperCamelCase       :    Optional[Any]		      =      self.dummy_image_processor
          UpperCamelCase       :    Any		      =      self.dummy_renderer

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

          return components
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=0			)	->						List[Any]:
          UpperCamelCase       :    Any		      =      floats_tensor((1, 3, 64, 64),     rng=random.Random(SCREAMING_SNAKE_CASE_			)			).to(SCREAMING_SNAKE_CASE_			)

          if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
             UpperCamelCase       :    Dict		      =      torch.manual_seed(SCREAMING_SNAKE_CASE_			)
          else:
             UpperCamelCase       :    Dict		      =      torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      {
              'image': input_image,
              'generator': generator,
              'num_inference_steps': 1,
              'frame_size': 32,
              'output_type': 'np',
          }
          return inputs
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Tuple		      =      'cpu'

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

          UpperCamelCase       :    List[str]		      =      self.pipeline_class(**SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      pipe.to(SCREAMING_SNAKE_CASE_			)

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase       :    Optional[int]		      =      output.images[0]
          UpperCamelCase       :    Any		      =      image[0, -3:, -3:, -1]

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

          UpperCamelCase       :    Any		      =      np.array(
              [
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
              ]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
       def    snake_case_			(     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    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Optional[Any]		      =      torch_device == 'cpu'
          UpperCamelCase       :    Any		      =      True
          self._test_inference_batch_single_identical(
              batch_size=2,     test_max_difference=SCREAMING_SNAKE_CASE_,     relax_max_difference=SCREAMING_SNAKE_CASE_,     )






       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    Union[str, Any]		      =      self.get_dummy_components()
          UpperCamelCase       :    Tuple		      =      self.pipeline_class(**SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      pipe.to(SCREAMING_SNAKE_CASE_			)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Any		      =      1
          UpperCamelCase       :    Optional[Any]		      =      2

          UpperCamelCase       :    Optional[int]		      =      self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase       :    Any		      =      pipe(**SCREAMING_SNAKE_CASE_,     num_images_per_prompt=SCREAMING_SNAKE_CASE_			)[0]

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






@slow
@require_torch_gpu
class 							lowerCAmelCase_						(    unittest.TestCase				):
       def    snake_case_			(     self			)	->						List[Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






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

          UpperCamelCase       :    Dict		      =      torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(0			)

          UpperCamelCase       :    int		      =      pipe(
              SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_,     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(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)







'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A			)			->			bool:






       """simple docstring"""







       if not isinstance(A    ,					A			):
              raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
       if len(A			) == 0:
              raise ValueError('''Input list must be a non empty list'''			)
       if len(A			) == 1:
              return True
       lowercase__      =					series[1] - series[0]
       for index in range(len(A			) - 1			):
              if series[index + 1] - series[index] != common_diff:
                     return False
       return True

def _SCREAMING_SNAKE_CASE (A			)			->			float:






       """simple docstring"""







       if not isinstance(A    ,					A			):
              raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
       if len(A			) == 0:
              raise ValueError('''Input list must be a non empty list'''			)
       lowercase__      =					0
       for val in series:
              answer += val
       return answer / len(A			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




