import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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 				a							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Union[str, Any]    			=		ShapEPipeline
 lowerCamelCase	:Optional[int]    			=		['''prompt''']
 lowerCamelCase	:str    			=		['''prompt''']
 lowerCamelCase	:Optional[int]    			=		[
     '''num_images_per_prompt''',
     '''num_inference_steps''',
     '''generator''',
     '''latents''',
     '''guidance_scale''',
     '''frame_size''',
     '''output_type''',
     '''return_dict''',
 ]
 lowerCamelCase	:Any    			=		False



 @property
 def      UpperCAmelCase							(					self							)  ->					str:
        return 32



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



 @property
 def      UpperCAmelCase							(					self							)  ->					str:
        return self.time_input_dim * 4



 @property
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        return 8



 @property
 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
        return tokenizer



 @property
 def      UpperCAmelCase							(					self							)  ->					Dict:
        torch.manual_seed(0							)
        _A        =  CLIPTextConfig(
            bos_token_id=0		,				eos_token_id=2		,				hidden_size=self.text_embedder_hidden_size		,				projection_dim=self.text_embedder_hidden_size		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				)
        return CLIPTextModelWithProjection(lowerCAmelCase_							)



 @property
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        torch.manual_seed(0							)

        _A        =  {
            """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""",
            """encoder_hid_proj_type""": None,
            """added_emb_type""": None,
        }

        _A        =  PriorTransformer(**lowerCAmelCase_							)
        return model



 @property
 def      UpperCAmelCase							(					self							)  ->					str:
        torch.manual_seed(0							)

        _A        =  {
            """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        =  ShapERenderer(**lowerCAmelCase_							)
        return model



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.dummy_prior
        _A        =  self.dummy_text_encoder
        _A        =  self.dummy_tokenizer
        _A        =  self.dummy_renderer

        _A        =  HeunDiscreteScheduler(
            beta_schedule="""exp"""		,				num_train_timesteps=10_24		,				prediction_type="""sample"""		,				use_karras_sigmas=lowerCAmelCase_		,				clip_sample=lowerCAmelCase_		,				clip_sample_range=1.0		,				)
        _A        =  {
            """prior""": prior,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """renderer""": renderer,
            """scheduler""": scheduler,
        }

        return components



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=0							)  ->					str:
        if str(lowerCAmelCase_							).startswith("""mps"""							):
               _A        =  torch.manual_seed(lowerCAmelCase_							)
        else:
               _A        =  torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
        _A        =  {
            """prompt""": """horse""",
            """generator""": generator,
            """num_inference_steps""": 1,
            """frame_size""": 32,
            """output_type""": """np""",
        }
        return inputs



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  """cpu"""

        _A        =  self.get_dummy_components()

        _A        =  self.pipeline_class(**lowerCAmelCase_							)
        _A        =  pipe.to(lowerCAmelCase_							)

        pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

        _A        =  pipe(**self.get_dummy_inputs(lowerCAmelCase_							)							)
        _A        =  output.images[0]
        _A        =  image[0, -3:, -3:, -1]

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

        _A        =  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      UpperCAmelCase							(					self							)  ->					str:
        # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
        self._test_inference_batch_consistent(batch_sizes=[1, 2]							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  torch_device == """cpu"""
        _A        =  True

        self._test_inference_batch_single_identical(
            batch_size=2		,				test_max_difference=lowerCAmelCase_		,				relax_max_difference=lowerCAmelCase_		,				)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_dummy_components()
        _A        =  self.pipeline_class(**lowerCAmelCase_							)
        _A        =  pipe.to(lowerCAmelCase_							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

        _A        =  1
        _A        =  2

        _A        =  self.get_dummy_inputs(lowerCAmelCase_							)

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

        _A        =  pipe(**lowerCAmelCase_		,				num_images_per_prompt=lowerCAmelCase_							)[0]

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



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







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/shap_e/test_shap_e_np_out.npy"""							)
        _A        =  ShapEPipeline.from_pretrained("""openai/shap-e"""							)
        _A        =  pipe.to(lowerCAmelCase_							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

        _A        =  torch.Generator(device=lowerCAmelCase_							).manual_seed(0							)

        _A        =  pipe(
            """a shark"""		,				generator=lowerCAmelCase_		,				guidance_scale=15.0		,				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_							)





import math
import sys






def  snake_case  (       snake_case__			:int)						->   int:
       if number != int(snake_case__):
              raise ValueError("""the value of input must be a natural number""")
       if number < 0:
              raise ValueError("""the value of input must not be a negative number""")
       if number == 0:
              return 1
       _A        =  [-1] * (number + 1)
       _A        =  0
       for i in range(1   ,	number + 1):
              _A        =  sys.maxsize
              _A        =  int(math.sqrt(snake_case__))
              for j in range(1   ,	root + 1):
                     _A        =  1 + answers[i - (j**2)]
                     _A        =  min(snake_case__   ,	snake_case__)
              _A        =  answer
       return answers[number]


if __name__ == "__main__":
    import doctest

    doctest.testmod()




