






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			):

     '''simple docstring'''

     _A						:			Tuple									=      ShapEImgaImgPipeline
     _A						:			Optional[int]									=      ['''image''']
     _A						:			List[Any]									=      ['''image''']
     _A						:			str									=      [
         '''num_images_per_prompt''',
         '''num_inference_steps''',
         '''generator''',
         '''latents''',
         '''guidance_scale''',
         '''frame_size''',
         '''output_type''',
         '''return_dict''',
     ]
     _A						:			int									=      False



     @property
     def       lowerCAmelCase				(  self				:	List[str]							)			->  int:


            """simple docstring"""

            return 32



     @property
     def       lowerCAmelCase				(  self				:	int							)			->  Any:


            """simple docstring"""

            return 32



     @property
     def       lowerCAmelCase				(  self				:	Dict							)			->  Optional[int]:


            """simple docstring"""

            return self.time_input_dim * 4



     @property
     def       lowerCAmelCase				(  self				:	List[Any]							)			->  List[str]:


            """simple docstring"""

            return 8



     @property
     def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Optional[Any]:


            """simple docstring"""

            torch.manual_seed(0							)
            __lowercase      :							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		,     )

            __lowercase      :							Union[str, Any]				=     CLIPVisionModel(__a							)
            return model



     @property
     def       lowerCAmelCase				(  self				:	List[str]							)			->  Dict:


            """simple docstring"""

            __lowercase      :							str				=     CLIPImageProcessor(
                crop_size=224		,     do_center_crop=__a		,     do_normalize=__a		,     do_resize=__a		,     image_mean=[0.48145466, 0.4578275, 0.40821073]		,     image_std=[0.26862954, 0.26130258, 0.27577711]		,     resample=3		,     size=224		,     )

            return image_processor



     @property
     def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Optional[int]:


            """simple docstring"""

            torch.manual_seed(0							)

            __lowercase      :							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,
            }

            __lowercase      :							str				=     PriorTransformer(**__a							)
            return model



     @property
     def       lowerCAmelCase				(  self				:	Dict							)			->  int:


            """simple docstring"""

            torch.manual_seed(0							)

            __lowercase      :							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,
                ),
            }
            __lowercase      :							Any				=     ShapERenderer(**__a							)
            return model



     def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Any:


            """simple docstring"""

            __lowercase      :							Union[str, Any]				=     self.dummy_prior
            __lowercase      :							Optional[int]				=     self.dummy_image_encoder
            __lowercase      :							Any				=     self.dummy_image_processor
            __lowercase      :							Optional[int]				=     self.dummy_renderer

            __lowercase      :							str				=     HeunDiscreteScheduler(
                beta_schedule="""exp"""		,     num_train_timesteps=1024		,     prediction_type="""sample"""		,     use_karras_sigmas=__a		,     clip_sample=__a		,     clip_sample_range=1.0		,     )
            __lowercase      :							List[str]				=     {
                """prior""": prior,
                """image_encoder""": image_encoder,
                """image_processor""": image_processor,
                """renderer""": renderer,
                """scheduler""": scheduler,
            }

            return components



     def       lowerCAmelCase				(  self				:	Dict		,     __a				:	int		,     __a				:	str=0							)			->  int:


            """simple docstring"""

            __lowercase      :							Optional[Any]				=     floats_tensor((1, 3, 64, 64)		,     rng=random.Random(__a							)							).to(__a							)

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



     def       lowerCAmelCase				(  self				:	Tuple							)			->  List[str]:


            """simple docstring"""

            __lowercase      :							str				=     """cpu"""

            __lowercase      :							Any				=     self.get_dummy_components()

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

            pipe.set_progress_bar_config(disable=__a							)

            __lowercase      :							str				=     pipe(**self.get_dummy_inputs(__a							)							)
            __lowercase      :							List[str]				=     output.images[0]
            __lowercase      :							Optional[int]				=     image[0, -3:, -3:, -1]

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

            __lowercase      :							str				=     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       lowerCAmelCase				(  self				:	int							)			->  str:


            """simple docstring"""

            self._test_inference_batch_consistent(batch_sizes=[1, 2]							)



     def       lowerCAmelCase				(  self				:	Any							)			->  Dict:


            """simple docstring"""

            __lowercase      :							List[Any]				=     torch_device == """cpu"""
            __lowercase      :							Tuple				=     True
            self._test_inference_batch_single_identical(
                batch_size=2		,     test_max_difference=__a		,     relax_max_difference=__a		,     )



     def       lowerCAmelCase				(  self				:	str							)			->  Any:


            """simple docstring"""

            __lowercase      :							int				=     self.get_dummy_components()
            __lowercase      :							Any				=     self.pipeline_class(**__a							)
            __lowercase      :							List[str]				=     pipe.to(__a							)
            pipe.set_progress_bar_config(disable=__a							)

            __lowercase      :							Tuple				=     1
            __lowercase      :							Dict				=     2

            __lowercase      :							Dict				=     self.get_dummy_inputs(__a							)

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

            __lowercase      :							Union[str, Any]				=     pipe(**__a		,     num_images_per_prompt=__a							)[0]

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







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

     '''simple docstring'''

     def       lowerCAmelCase				(  self				:	int							)			->  Optional[Any]:


            """simple docstring"""

            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()



     def       lowerCAmelCase				(  self				:	Any							)			->  List[Any]:


            """simple docstring"""

            __lowercase      :							Union[str, Any]				=     load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""							)
            __lowercase      :							Optional[Any]				=     load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/shap_e/test_shap_e_img2img_out.npy"""							)
            __lowercase      :							str				=     ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""							)
            __lowercase      :							str				=     pipe.to(__a							)
            pipe.set_progress_bar_config(disable=__a							)

            __lowercase      :							Any				=     torch.Generator(device=__a							).manual_seed(0							)

            __lowercase      :							Dict				=     pipe(
                __a		,     generator=__a		,     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(__a		,     __a							)







from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						list[str] | None = None      ):
							__lowercase      :							Tuple				=     word_bank or []
							# create a table
							__lowercase      :							int				=     len(lowerCAmelCase_      ) + 1

							__lowercase      :							list[list[list[str]]]				=     []
							for _ in range(lowerCAmelCase_      ):
														table.append([]      )
							# seed value
							__lowercase      :							Dict				=     [[]]  # because empty string has empty combination

							# iterate through the indices
							for i in range(lowerCAmelCase_      ):
														# condition
														if table[i] != []:
																					for word in word_bank:
																												# slice condition
																												if target[i : i + len(lowerCAmelCase_      )] == word:
																																			__lowercase      :							list[list[str]]				=     [
																																			    [word, *way] for way in table[i]
																																			]
																																			# adds the word to every combination the current position holds
																																			# now,push that combination to the table[i+len(word)]
																																			table[i + len(lowerCAmelCase_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
							for combination in table[len(lowerCAmelCase_      )]:
														combination.reverse()

							return table[len(lowerCAmelCase_      )]


if __name__ == "__main__":
	print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
	print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
	print(
	    all_construct(
	        '''hexagonosaurus''',
	        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
	    )
	)