






'''simple docstring'''
import functools





def  _a(  UpperCamelCase__				:						list[int],	UpperCamelCase__				:						list[int]							):




      '''simple docstring'''






      if not isinstance(UpperCamelCase__,	UpperCamelCase__							) or not all(isinstance(UpperCamelCase__,	UpperCamelCase__							) for day in days							):
            raise ValueError('''The parameter days should be a list of integers'''							)

      if len(UpperCamelCase__							) != 3 or not all(isinstance(UpperCamelCase__,	UpperCamelCase__							) for cost in costs							):
            raise ValueError('''The parameter costs should be a list of three integers'''							)

      if len(UpperCamelCase__							) == 0:
            return 0

      if min(UpperCamelCase__							) <= 0:
            raise ValueError('''All days elements should be greater than 0'''							)

      if max(UpperCamelCase__							) >= 3_6_6:
            raise ValueError('''All days elements should be less than 366'''							)

      SCREAMING_SNAKE_CASE__     :				Dict									=set(UpperCamelCase__							)

      @functools.cache
      def dynamic_programming(UpperCamelCase__				:						int							) -> int:
            if index > 3_6_5:
                  return 0

            if index not in days_set:
                  return dynamic_programming(index + 1							)

            return min(
                costs[0] + dynamic_programming(index + 1							),	costs[1] + dynamic_programming(index + 7							),	costs[2] + dynamic_programming(index + 3_0							),	)

      return dynamic_programming(1							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''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	:							List[Any]      )	->						List[Any]:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
       return 32




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Union[str, Any]:
       return 8




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Union[str, Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Dict									=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__     :				str									=CLIPVisionModel(__lowercase      )
       return model




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

       SCREAMING_SNAKE_CASE__     :				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__     :				str									=PriorTransformer(**__lowercase      )
       return model




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

       SCREAMING_SNAKE_CASE__     :				Optional[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__     :				str									=ShapERenderer(**__lowercase      )
       return model




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

       SCREAMING_SNAKE_CASE__     :				int									=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__     :				Any									={
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''image_processor''': image_processor,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components




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

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




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

       SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				str									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

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

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

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




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

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=1
       SCREAMING_SNAKE_CASE__     :				List[str]									=2

       SCREAMING_SNAKE_CASE__     :				Dict									=self.get_dummy_inputs(__lowercase      )

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

       SCREAMING_SNAKE_CASE__     :				List[Any]									=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	:							Optional[Any]      )	->						int:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def      __magic_name__	(      self	:							int      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				List[str]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/shap_e/test_shap_e_img2img_out.npy'''      )
       SCREAMING_SNAKE_CASE__     :				List[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__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=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      )