



'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 		lowerCamelCase__      (						UpperCamelCase__	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						UnCLIPImageVariationPipeline
					A_						    =						IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
					A_						    =						IMAGE_VARIATION_BATCH_PARAMS

					A_						    =						[
					    """generator""",
					    """return_dict""",
					    """decoder_num_inference_steps""",
					    """super_res_num_inference_steps""",
					]
					A_						    =						False




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



						'''simple docstring'''





						return 32




					@property
					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       str:



						'''simple docstring'''





						return 32




					@property
					def 						__UpperCAmelCase     (  self				:	int					)  ->       Union[str, Any]:



						'''simple docstring'''





						return self.time_input_dim




					@property
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       int:



						'''simple docstring'''





						return self.time_input_dim * 4




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



						'''simple docstring'''





						return 100




					@property
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Any       =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)
						return tokenizer




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



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Union[str, 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(UpperCamelCase_					)




					@property
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       str:



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Optional[int]       =		CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size     ,  projection_dim=self.text_embedder_hidden_size     ,  num_hidden_layers=5     ,  num_attention_heads=4     ,  image_size=32     ,  intermediate_size=37     ,  patch_size=1     ,  )
						return CLIPVisionModelWithProjection(UpperCamelCase_					)




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



						'''simple docstring'''





						torch.manual_seed(0					)

						_lowercase					:       Union[str, Any]       =		{
						    'clip_embeddings_dim': self.text_embedder_hidden_size,
						    'time_embed_dim': self.time_embed_dim,
						    'cross_attention_dim': self.cross_attention_dim,
						}

						_lowercase					:       int       =		UnCLIPTextProjModel(**UpperCamelCase_					)
						return model




					@property
					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       int:



						'''simple docstring'''





						torch.manual_seed(0					)

						_lowercase					:       Tuple       =		{
						    'sample_size': 32,
						    # RGB in channels
						    'in_channels': 3,
						    # Out channels is double in channels because predicts mean and variance
						    'out_channels': 6,
						    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
						    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
						    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
						    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
						    'layers_per_block': 1,
						    'cross_attention_dim': self.cross_attention_dim,
						    'attention_head_dim': 4,
						    'resnet_time_scale_shift': 'scale_shift',
						    'class_embed_type': 'identity',
						}

						_lowercase					:       Dict       =		UNetaDConditionModel(**UpperCamelCase_					)
						return model




					@property
					def 						__UpperCAmelCase     (  self				:	Any					)  ->       Tuple:



						'''simple docstring'''





						return {
						    "sample_size": 64,
						    "layers_per_block": 1,
						    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
						    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "in_channels": 6,
						    "out_channels": 3,
						}




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



						'''simple docstring'''





						torch.manual_seed(0					)

						_lowercase					:       Tuple       =		UNetaDModel(**self.dummy_super_res_kwargs					)
						return model




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



						'''simple docstring'''





						torch.manual_seed(1					)

						_lowercase					:       Union[str, Any]       =		UNetaDModel(**self.dummy_super_res_kwargs					)
						return model




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Dict       =		self.dummy_decoder
						_lowercase					:       Tuple       =		self.dummy_text_proj
						_lowercase					:       Dict       =		self.dummy_text_encoder
						_lowercase					:       Dict       =		self.dummy_tokenizer
						_lowercase					:       Dict       =		self.dummy_super_res_first
						_lowercase					:       Dict       =		self.dummy_super_res_last

						_lowercase					:       List[str]       =		UnCLIPScheduler(
						    variance_type='learned_range'     ,  prediction_type='epsilon'     ,  num_train_timesteps=1000     ,  )

						_lowercase					:       str       =		UnCLIPScheduler(
						    variance_type='fixed_small_log'     ,  prediction_type='epsilon'     ,  num_train_timesteps=1000     ,  )

						_lowercase					:       List[str]       =		CLIPImageProcessor(crop_size=32     ,  size=32					)

						_lowercase					:       Optional[Any]       =		self.dummy_image_encoder

						return {
						    "decoder": decoder,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "text_proj": text_proj,
						    "feature_extractor": feature_extractor,
						    "image_encoder": image_encoder,
						    "super_res_first": super_res_first,
						    "super_res_last": super_res_last,
						    "decoder_scheduler": decoder_scheduler,
						    "super_res_scheduler": super_res_scheduler,
						}




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Union[str, Any]=0     ,  UpperCamelCase_				:	List[Any]=True					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       str       =		floats_tensor((1, 3, 32, 32)     ,  rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)
						if str(UpperCamelCase_					).startswith('mps'					):
							_lowercase					:       str       =		torch.manual_seed(UpperCamelCase_					)
						else:
							_lowercase					:       List[str]       =		torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)

						if pil_image:
							_lowercase					:       Tuple       =		input_image * 0.5 + 0.5
							_lowercase					:       str       =		input_image.clamp(0     ,  1					)
							_lowercase					:       List[str]       =		input_image.cpu().permute(0     ,  2     ,  3     ,  1					).float().numpy()
							_lowercase					:       Optional[int]       =		DiffusionPipeline.numpy_to_pil(UpperCamelCase_					)[0]

						return {
						    "image": input_image,
						    "generator": generator,
						    "decoder_num_inference_steps": 2,
						    "super_res_num_inference_steps": 2,
						    "output_type": "np",
						}




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       str:



						'''simple docstring'''





						_lowercase					:       List[str]       =		'cpu'

						_lowercase					:       str       =		self.get_dummy_components()

						_lowercase					:       List[str]       =		self.pipeline_class(**UpperCamelCase_					)
						_lowercase					:       int       =		pipe.to(UpperCamelCase_					)

						pipe.set_progress_bar_config(disable=UpperCamelCase_					)

						_lowercase					:       List[Any]       =		self.get_dummy_inputs(UpperCamelCase_     ,  pil_image=UpperCamelCase_					)

						_lowercase					:       Dict       =		pipe(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		output.images

						_lowercase					:       Union[str, Any]       =		self.get_dummy_inputs(UpperCamelCase_     ,  pil_image=UpperCamelCase_					)

						_lowercase					:       List[Any]       =		pipe(
						    **UpperCamelCase_     ,  return_dict=UpperCamelCase_     ,  )[0]

						_lowercase					:       Tuple       =		image[0, -3:, -3:, -1]
						_lowercase					:       List[str]       =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						_lowercase					:       Union[str, Any]       =		np.array(
						    [
						        0.99_97,
						        0.00_02,
						        0.99_97,
						        0.99_97,
						        0.99_69,
						        0.00_23,
						        0.99_97,
						        0.99_69,
						        0.99_70,
						    ]					)

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




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		'cpu'

						_lowercase					:       str       =		self.get_dummy_components()

						_lowercase					:       int       =		self.pipeline_class(**UpperCamelCase_					)
						_lowercase					:       List[str]       =		pipe.to(UpperCamelCase_					)

						pipe.set_progress_bar_config(disable=UpperCamelCase_					)

						_lowercase					:       Dict       =		self.get_dummy_inputs(UpperCamelCase_     ,  pil_image=UpperCamelCase_					)

						_lowercase					:       int       =		pipe(**UpperCamelCase_					)
						_lowercase					:       Dict       =		output.images

						_lowercase					:       Optional[int]       =		self.get_dummy_inputs(UpperCamelCase_     ,  pil_image=UpperCamelCase_					)

						_lowercase					:       Union[str, Any]       =		pipe(
						    **UpperCamelCase_     ,  return_dict=UpperCamelCase_     ,  )[0]

						_lowercase					:       Optional[Any]       =		image[0, -3:, -3:, -1]
						_lowercase					:       Tuple       =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						_lowercase					:       str       =		np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]					)

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




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		'cpu'

						_lowercase					:       Optional[int]       =		self.get_dummy_components()

						_lowercase					:       Any       =		self.pipeline_class(**UpperCamelCase_					)
						_lowercase					:       Optional[int]       =		pipe.to(UpperCamelCase_					)

						pipe.set_progress_bar_config(disable=UpperCamelCase_					)

						_lowercase					:       Tuple       =		self.get_dummy_inputs(UpperCamelCase_     ,  pil_image=UpperCamelCase_					)
						_lowercase					:       List[str]       =		[
						    pipeline_inputs['image'],
						    pipeline_inputs['image'],
						]

						_lowercase					:       List[Any]       =		pipe(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		output.images

						_lowercase					:       Union[str, Any]       =		self.get_dummy_inputs(UpperCamelCase_     ,  pil_image=UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		[
						    tuple_pipeline_inputs['image'],
						    tuple_pipeline_inputs['image'],
						]

						_lowercase					:       Tuple       =		pipe(
						    **UpperCamelCase_     ,  return_dict=UpperCamelCase_     ,  )[0]

						_lowercase					:       List[Any]       =		image[0, -3:, -3:, -1]
						_lowercase					:       Any       =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (2, 64, 64, 3)

						_lowercase					:       Dict       =		np.array(
						    [
						        0.99_97,
						        0.99_89,
						        0.00_08,
						        0.00_21,
						        0.99_60,
						        0.00_18,
						        0.00_14,
						        0.00_02,
						        0.99_33,
						    ]					)

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




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		torch.device('cpu'					)



						class 		lowerCamelCase__      :




											'''simple docstring'''



											A_						    =						1






						_lowercase					:       Tuple       =		self.get_dummy_components()

						_lowercase					:       List[str]       =		self.pipeline_class(**UpperCamelCase_					)
						_lowercase					:       str       =		pipe.to(UpperCamelCase_					)

						pipe.set_progress_bar_config(disable=UpperCamelCase_					)

						_lowercase					:       Optional[int]       =		torch.Generator(device=UpperCamelCase_					).manual_seed(0					)
						_lowercase					:       Dict       =		pipe.decoder.dtype
						_lowercase					:       Tuple       =		1

						_lowercase					:       Union[str, Any]       =		(
						    batch_size,
						    pipe.decoder.config.in_channels,
						    pipe.decoder.config.sample_size,
						    pipe.decoder.config.sample_size,
						)
						_lowercase					:       Tuple       =		pipe.prepare_latents(
						    UpperCamelCase_     ,  dtype=UpperCamelCase_     ,  device=UpperCamelCase_     ,  generator=UpperCamelCase_     ,  latents=UpperCamelCase_     ,  scheduler=DummyScheduler()					)

						_lowercase					:       Any       =		(
						    batch_size,
						    pipe.super_res_first.config.in_channels // 2,
						    pipe.super_res_first.config.sample_size,
						    pipe.super_res_first.config.sample_size,
						)
						_lowercase					:       List[Any]       =		pipe.prepare_latents(
						    UpperCamelCase_     ,  dtype=UpperCamelCase_     ,  device=UpperCamelCase_     ,  generator=UpperCamelCase_     ,  latents=UpperCamelCase_     ,  scheduler=DummyScheduler()					)

						_lowercase					:       Union[str, Any]       =		self.get_dummy_inputs(UpperCamelCase_     ,  pil_image=UpperCamelCase_					)

						_lowercase					:       List[Any]       =		pipe(
						    **UpperCamelCase_     ,  decoder_latents=UpperCamelCase_     ,  super_res_latents=UpperCamelCase_					).images

						_lowercase					:       Optional[int]       =		self.get_dummy_inputs(UpperCamelCase_     ,  pil_image=UpperCamelCase_					)
						# Don't pass image, instead pass embedding
						_lowercase					:       Optional[Any]       =		pipeline_inputs.pop('image'					)
						_lowercase					:       Optional[int]       =		pipe.image_encoder(UpperCamelCase_					).image_embeds

						_lowercase					:       Optional[Any]       =		pipe(
						    **UpperCamelCase_     ,  decoder_latents=UpperCamelCase_     ,  super_res_latents=UpperCamelCase_     ,  image_embeddings=UpperCamelCase_     ,  ).images

						# make sure passing text embeddings manually is identical
						assert np.abs(img_out_a - img_out_a					).max() < 1E-4




					@skip_mps
					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		torch_device == 'cpu'

						# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
						_lowercase					:       Any       =		1E-2

						self._test_attention_slicing_forward_pass(
						    test_max_difference=UpperCamelCase_     ,  expected_max_diff=UpperCamelCase_					)




					@skip_mps
					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		torch_device == 'cpu'
						_lowercase					:       List[str]       =		True
						_lowercase					:       List[str]       =		[
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]

						self._test_inference_batch_single_identical(
						    test_max_difference=UpperCamelCase_     ,  relax_max_difference=UpperCamelCase_     ,  additional_params_copy_to_batched_inputs=UpperCamelCase_     ,  )




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Dict       =		[
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]

						if torch_device == "mps":
							# TODO: MPS errors with larger batch sizes
							_lowercase					:       Dict       =		[2, 3]
							self._test_inference_batch_consistent(
							    batch_sizes=UpperCamelCase_     ,  additional_params_copy_to_batched_inputs=UpperCamelCase_     ,  )
						else:
							self._test_inference_batch_consistent(
							    additional_params_copy_to_batched_inputs=UpperCamelCase_					)




					@skip_mps
					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       List[str]:



						'''simple docstring'''





						return super().test_dict_tuple_outputs_equivalent()




					@skip_mps
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Tuple:



						'''simple docstring'''





						return super().test_save_load_local()




					@skip_mps
					def 						__UpperCAmelCase     (  self				:	str					)  ->       List[Any]:



						'''simple docstring'''





						return super().test_save_load_optional_components()






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




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       str:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       str       =		load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'					)
						_lowercase					:       Optional[int]       =		load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'					)

						_lowercase					:       Any       =		UnCLIPImageVariationPipeline.from_pretrained(
						    'kakaobrain/karlo-v1-alpha-image-variations'     ,  torch_dtype=torch.floataa					)
						_lowercase					:       Optional[int]       =		pipeline.to(UpperCamelCase_					)
						pipeline.set_progress_bar_config(disable=UpperCamelCase_					)

						_lowercase					:       str       =		torch.Generator(device='cpu'					).manual_seed(0					)
						_lowercase					:       Any       =		pipeline(
						    UpperCamelCase_     ,  generator=UpperCamelCase_     ,  output_type='np'     ,  )

						_lowercase					:       int       =		output.images[0]

						assert image.shape == (256, 256, 3)

						assert_mean_pixel_difference(UpperCamelCase_     ,  UpperCamelCase_     ,  15					)





'''simple docstring'''

import unittest

from knapsack import greedy_knapsack as kp



class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	int					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		[10, 20, 30, 40, 50, 60]
						_lowercase					:       Tuple       =		[2, 4, 6, 8, 10, 12]
						_lowercase					:       Optional[Any]       =		100
						self.assertEqual(kp.calc_profit(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)     ,  210					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       int:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'max_weight must greater than zero.'					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'Weight can not be negative.'					)




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'Profit can not be negative.'					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       List[str]:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'max_weight must greater than zero.'					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       List[Any]:



						'''simple docstring'''





						self.assertRaisesRegex(
						    UpperCamelCase_     ,  'The length of profit and weight must be same.'					)


if __name__ == "__main__":
		unittest.main()
