'''simple docstring'''

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 			UpperCAmelCase  (						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    ShapEPipeline
       SCREAMING_SNAKE_CASE							=    ["""prompt"""]
       SCREAMING_SNAKE_CASE							=    ["""prompt"""]
       SCREAMING_SNAKE_CASE							=    [
           """num_images_per_prompt""",
           """num_inference_steps""",
           """generator""",
           """latents""",
           """guidance_scale""",
           """frame_size""",
           """output_type""",
           """return_dict""",
       ]
       SCREAMING_SNAKE_CASE							=    False

       @property
       def _lowerCAmelCase(						self )  ->  Tuple:
        return 32

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

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

       @property
       def _lowerCAmelCase(						self )  ->  Tuple:
        return 8

       @property
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    Union[str, Any]  =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
        return tokenizer

       @property
       def _lowerCAmelCase(						self )  ->  Dict:
        torch.manual_seed(0 )
        lowercase__				:    List[Any]  =					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=1000	,   )
        return CLIPTextModelWithProjection(__lowerCAmelCase )

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

        lowercase__				:    Optional[int]  =					{
            """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,
        }

        lowercase__				:    Dict  =					PriorTransformer(**__lowerCAmelCase )
        return model

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

        lowercase__				:    str  =					{
            """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,
            ),
        }
        lowercase__				:    List[Any]  =					ShapERenderer(**__lowerCAmelCase )
        return model

       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    List[Any]  =					self.dummy_prior
        lowercase__				:    str  =					self.dummy_text_encoder
        lowercase__				:    int  =					self.dummy_tokenizer
        lowercase__				:    int  =					self.dummy_renderer

        lowercase__				:    Tuple  =					HeunDiscreteScheduler(
            beta_schedule='''exp'''	,   num_train_timesteps=1024	,   prediction_type='''sample'''	,   use_karras_sigmas=__lowerCAmelCase	,   clip_sample=__lowerCAmelCase	,   clip_sample_range=1.0	,   )
        lowercase__				:    Optional[int]  =					{
            """prior""": prior,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """renderer""": renderer,
            """scheduler""": scheduler,
        }

        return components

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0 )  ->  int:
        if str(__lowerCAmelCase ).startswith('''mps''' ):
         lowercase__				:    List[str]  =					torch.manual_seed(__lowerCAmelCase )
        else:
         lowercase__				:    List[Any]  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    int  =					{
            """prompt""": """horse""",
            """generator""": generator,
            """num_inference_steps""": 1,
            """frame_size""": 32,
            """output_type""": """np""",
        }
        return inputs

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    List[Any]  =					"""cpu"""

        lowercase__				:    Union[str, Any]  =					self.get_dummy_components()

        lowercase__				:    Optional[Any]  =					self.pipeline_class(**__lowerCAmelCase )
        lowercase__				:    List[str]  =					pipe.to(__lowerCAmelCase )

        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    Optional[Any]  =					pipe(**self.get_dummy_inputs(__lowerCAmelCase ) )
        lowercase__				:    Optional[Any]  =					output.images[0]
        lowercase__				:    Tuple  =					image[0, -3:, -3:, -1]

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

        lowercase__				:    List[str]  =					np.array(
            [
                0.0_0_0_3_9_2_1_6,
                0.0_0_0_3_9_2_1_6,
                0.0_0_0_3_9_2_1_6,
                0.0_0_0_3_9_2_1_6,
                0.0_0_0_3_9_2_1_6,
                0.0_0_0_3_9_2_1_6,
                0.0_0_0_3_9_2_1_6,
                0.0_0_0_3_9_2_1_6,
                0.0_0_0_3_9_2_1_6,
            ] )

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

       def _lowerCAmelCase(						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 _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    str  =					torch_device == """cpu"""
        lowercase__				:    str  =					True

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


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					self.get_dummy_components()
        lowercase__				:    int  =					self.pipeline_class(**__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    Optional[int]  =					1
        lowercase__				:    Dict  =					2

        lowercase__				:    Optional[Any]  =					self.get_dummy_inputs(__lowerCAmelCase )

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

        lowercase__				:    int  =					pipe(**__lowerCAmelCase	,   num_images_per_prompt=__lowerCAmelCase )[0]

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



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





       '''simple docstring'''



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


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[int]  =					load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/shap_e/test_shap_e_np_out.npy''' )
        lowercase__				:    List[str]  =					ShapEPipeline.from_pretrained('''openai/shap-e''' )
        lowercase__				:    str  =					pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    List[Any]  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(0 )

        lowercase__				:    Optional[int]  =					pipe(
            '''a shark'''	,   generator=__lowerCAmelCase	,   guidance_scale=1_5.0	,   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 )



def     _a					(						lowerCamelCase = 100_0000							):
 lowerCamelCase		:	Any							=      set(range(3,	lowerCamelCase,	2							)							)
 primes.add(2							)
 for p in range(3,	lowerCamelCase,	2							):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p,	lowerCamelCase,	lowerCamelCase							)							)							)

 lowerCamelCase		:	Any							=      [float(lowerCamelCase							) for n in range(limit + 1							)]

 for p in primes:
  for n in range(lowerCamelCase,	limit + 1,	lowerCamelCase							):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]							)							)


if __name__ == "__main__":
      print(f'''{solution() = }''')




