
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  lowercase__(				__lowercase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	ShapEImgaImgPipeline
	a			:List[str]         =	['image']
	a			:Optional[int]         =	['image']
	a			:int         =	[
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	a			:Any         =	False
	@property
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Optional[int]:
					return 3_2
	@property
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Optional[Any]:
					return 3_2
	@property
	def 						_lowercase  (       self  :			Tuple       )       ->     Union[str, Any]:
					return self.time_input_dim * 4
	@property
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Any:
					return 8
	@property
	def 						_lowercase  (       self  :			List[str]       )       ->     int:
					torch.manual_seed(0       )
					lowercase_       =					CLIPVisionConfig(
					    hidden_size=self.text_embedder_hidden_size       ,							image_size=6_4       ,							projection_dim=self.text_embedder_hidden_size       ,							intermediate_size=3_7       ,							num_attention_heads=4       ,							num_channels=3       ,							num_hidden_layers=5       ,							patch_size=1       ,							)

					lowercase_       =					CLIPVisionModel(SCREAMING_SNAKE_CASE_       )
					return model
	@property
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     List[str]:
					lowercase_       =					CLIPImageProcessor(
					    crop_size=2_2_4       ,							do_center_crop=SCREAMING_SNAKE_CASE_       ,							do_normalize=SCREAMING_SNAKE_CASE_       ,							do_resize=SCREAMING_SNAKE_CASE_       ,							image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,							image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,							resample=3       ,							size=2_2_4       ,							)

					return image_processor
	@property
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     List[Any]:
					torch.manual_seed(0       )

					lowercase_       =					{
					    "num_attention_heads": 2,
					    "attention_head_dim": 1_6,
					    "embedding_dim": self.time_input_dim,
					    "num_embeddings": 3_2,
					    "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_       =					PriorTransformer(**SCREAMING_SNAKE_CASE_       )
					return model
	@property
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     List[Any]:
					torch.manual_seed(0       )

					lowercase_       =					{
					    "param_shapes": (
					        (self.renderer_dim, 9_3),
					        (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": 1_2,
					    "background": (
					        0.1,
					        0.1,
					        0.1,
					    ),
					}
					lowercase_       =					ShapERenderer(**SCREAMING_SNAKE_CASE_       )
					return model
	def 						_lowercase  (       self  :			str       )       ->     Union[str, Any]:
					lowercase_       =					self.dummy_prior
					lowercase_       =					self.dummy_image_encoder
					lowercase_       =					self.dummy_image_processor
					lowercase_       =					self.dummy_renderer

					lowercase_       =					HeunDiscreteScheduler(
					    beta_schedule='''exp'''       ,							num_train_timesteps=1_0_2_4       ,							prediction_type='''sample'''       ,							use_karras_sigmas=SCREAMING_SNAKE_CASE_       ,							clip_sample=SCREAMING_SNAKE_CASE_       ,							clip_sample_range=1.0       ,							)
					lowercase_       =					{
					    "prior": prior,
					    "image_encoder": image_encoder,
					    "image_processor": image_processor,
					    "renderer": renderer,
					    "scheduler": scheduler,
					}

					return components
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Dict=0       )       ->     Tuple:
					lowercase_       =					floats_tensor((1, 3, 6_4, 6_4)       ,							rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

					if str(SCREAMING_SNAKE_CASE_       ).startswith('''mps'''       ):
									lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					{
					    "image": input_image,
					    "generator": generator,
					    "num_inference_steps": 1,
					    "frame_size": 3_2,
					    "output_type": "np",
					}
					return inputs
	def 						_lowercase  (       self  :			int       )       ->     Any:
					lowercase_       =					"cpu"

					lowercase_       =					self.get_dummy_components()

					lowercase_       =					self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					pipe.to(SCREAMING_SNAKE_CASE_       )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

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

					assert image.shape == (2_0, 3_2, 3_2, 3)

					lowercase_       =					np.array(
					    [
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					    ]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
	def 						_lowercase  (       self  :			str       )       ->     Optional[Any]:
					# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
					self._test_inference_batch_consistent(batch_sizes=[1, 2]       )
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     str:
					lowercase_       =					torch_device == "cpu"
					lowercase_       =					True
					self._test_inference_batch_single_identical(
					    batch_size=2       ,							test_max_difference=SCREAMING_SNAKE_CASE_       ,							relax_max_difference=SCREAMING_SNAKE_CASE_       ,							)




	def 						_lowercase  (       self  :			Any       )       ->     List[str]:
					lowercase_       =					self.get_dummy_components()
					lowercase_       =					self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					pipe.to(SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					1
					lowercase_       =					2

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )

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

					lowercase_       =					pipe(**SCREAMING_SNAKE_CASE_       ,							num_images_per_prompt=SCREAMING_SNAKE_CASE_       )[0]

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

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



	"""simple docstring"""




	def 						_lowercase  (       self  :			Tuple       )       ->     str:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()




	def 						_lowercase  (       self  :			Dict       )       ->     List[str]:
					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''       )
					lowercase_       =					load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/shap_e/test_shap_e_img2img_out.npy'''       )
					lowercase_       =					ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''       )
					lowercase_       =					pipe.to(SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(0       )

					lowercase_       =					pipe(
					    SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							guidance_scale=3.0       ,							num_inference_steps=6_4       ,							frame_size=6_4       ,							output_type='''np'''       ,							).images[0]

					assert images.shape == (2_0, 6_4, 6_4, 3)

					assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowercase  :					Optional[Any]													=       TypeVar('T')




class  lowerCamelCase__							(				Generic[T]):




	'''simple docstring'''




	_A								=     42  # Cache store of keys
	_A								=     42  # References of the keys in cache
	_A								=     1_0  # Maximum capacity of cache







	def __init__(					self		:Optional[Any]     ,   a		:int						)		-> None:
								__UpperCamelCase	:   Union[str, Any]			   =					deque()
								__UpperCamelCase	:   str			   =					set()
								if not n:
															__UpperCamelCase	:   Union[str, Any]			   =					sys.maxsize
								elif n < 0:
															raise ValueError("n should be an integer greater than 0."						)
								else:
															__UpperCamelCase	:   Any			   =					n







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:T						)		-> None:
								if x not in self.key_reference:
															if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
																						__UpperCamelCase	:   int			   =					self.dq_store.pop()
																						self.key_reference.remove(a						)
								else:
															self.dq_store.remove(a						)

								self.dq_store.appendleft(a						)
								self.key_reference.add(a						)







	def    _lowerCamelCase				(					self		:Any						)		-> None:
								for k in self.dq_store:
															print(a						)







	def __repr__(					self		:Tuple						)		-> str:
								return f'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase  :					LRUCache[str | int]													=       LRUCache(4)
		lru_cache.refer('A')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('A')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"