






'''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        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							ShapEImgaImgPipeline
 snake_case_												=							["""image"""]
 snake_case_												=							["""image"""]
 snake_case_												=							[
     """num_images_per_prompt""",
     """num_inference_steps""",
     """generator""",
     """latents""",
     """guidance_scale""",
     """frame_size""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_												=							False




 @property
 def        __magic_name__		( self		:							Union[str, Any] )		->  Optional[Any]:
       return 32




 @property
 def        __magic_name__		( self		:							Optional[Any] )		->  Optional[Any]:
       return 32




 @property
 def        __magic_name__		( self		:							List[str] )		->  Union[str, Any]:
       return self.time_input_dim * 4




 @property
 def        __magic_name__		( self		:							Any )		->  str:
       return 8




 @property
 def        __magic_name__		( self		:							Optional[Any] )		->  int:
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			int													=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   , )

       SCREAMING_SNAKE_CASE__       :			int													=CLIPVisionModel(__lowercase )
       return model




 @property
 def        __magic_name__		( self		:							Dict )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			str													=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        __magic_name__		( self		:							Tuple )		->  str:
       torch.manual_seed(0 )

       SCREAMING_SNAKE_CASE__       :			List[str]													={
           '''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,
       }

       SCREAMING_SNAKE_CASE__       :			Any													=PriorTransformer(**__lowercase )
       return model




 @property
 def        __magic_name__		( self		:							Any )		->  Dict:
       torch.manual_seed(0 )

       SCREAMING_SNAKE_CASE__       :			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,
           ),
       }
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=ShapERenderer(**__lowercase )
       return model




 def        __magic_name__		( self		:							List[str] )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.dummy_prior
       SCREAMING_SNAKE_CASE__       :			int													=self.dummy_image_encoder
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.dummy_image_processor
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.dummy_renderer

       SCREAMING_SNAKE_CASE__       :			List[str]													=HeunDiscreteScheduler(
           beta_schedule='''exp'''   , num_train_timesteps=10_24   , prediction_type='''sample'''   , use_karras_sigmas=__lowercase   , clip_sample=__lowercase   , clip_sample_range=1.0   , )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													={
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''image_processor''': image_processor,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components




 def        __magic_name__		( self		:							str   , __lowercase		:							Union[str, Any]   , __lowercase		:							str=0 )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			int													=floats_tensor((1, 3, 64, 64)   , rng=random.Random(__lowercase ) ).to(__lowercase )

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




 def        __magic_name__		( self		:							List[Any] )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			int													='''cpu'''

       SCREAMING_SNAKE_CASE__       :			Dict													=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.pipeline_class(**__lowercase )
       SCREAMING_SNAKE_CASE__       :			int													=pipe.to(__lowercase )

       pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[str]													=pipe(**self.get_dummy_inputs(__lowercase ) )
       SCREAMING_SNAKE_CASE__       :			List[str]													=output.images[0]
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=image[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=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        __magic_name__		( self		:							List[str] )		->  Dict:
       # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
       self._test_inference_batch_consistent(batch_sizes=[1, 2] )




 def        __magic_name__		( self		:							Union[str, Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Tuple													=torch_device == '''cpu'''
       SCREAMING_SNAKE_CASE__       :			str													=True
       self._test_inference_batch_single_identical(
           batch_size=2   , test_max_difference=__lowercase   , relax_max_difference=__lowercase   , )




 def        __magic_name__		( self		:							int )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			str													=self.get_dummy_components()
       SCREAMING_SNAKE_CASE__       :			List[str]													=self.pipeline_class(**__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=pipe.to(__lowercase )
       pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=1
       SCREAMING_SNAKE_CASE__       :			int													=2

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self.get_dummy_inputs(__lowercase )

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

       SCREAMING_SNAKE_CASE__       :			int													=pipe(**__lowercase   , num_images_per_prompt=__lowercase )[0]

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






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




 def        __magic_name__		( self		:							Union[str, Any] )		->  Union[str, Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




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

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.Generator(device=__lowercase ).manual_seed(0 )

       SCREAMING_SNAKE_CASE__       :			int													=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 math





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      return math.sqrt(UpperCamelCase__							) * math.sqrt(UpperCamelCase__							) == num





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[Any]													=0
      SCREAMING_SNAKE_CASE__       :			Any													=n
      while left <= right:
            SCREAMING_SNAKE_CASE__       :			str													=(left + right) // 2
            if mid**2 == n:
                  return True
            elif mid**2 > n:
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=mid - 1
            else:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=mid + 1
      return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()