

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 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						UnCLIPImageVariationPipeline
			SCREAMING_SNAKE_CASE							   =						IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
			SCREAMING_SNAKE_CASE							   =						IMAGE_VARIATION_BATCH_PARAMS

			SCREAMING_SNAKE_CASE							   =						[
			    'generator',
			    'return_dict',
			    'decoder_num_inference_steps',
			    'super_res_num_inference_steps',
			]
			SCREAMING_SNAKE_CASE							   =						False





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




						'''simple docstring'''
						return 32





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




						'''simple docstring'''
						return 32





			@property
			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						return self.time_input_dim





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




						'''simple docstring'''
						return self.time_input_dim * 4





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




						'''simple docstring'''
						return 100





			@property
			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)
						return tokenizer





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




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =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(__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =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(__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						torch.manual_seed(0					)

						__a        ={
						    'clip_embeddings_dim': self.text_embedder_hidden_size,
						    'time_embed_dim': self.time_embed_dim,
						    'cross_attention_dim': self.cross_attention_dim,
						}

						__a        =UnCLIPTextProjModel(**__snake_case					)
						return model





			@property
			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						torch.manual_seed(0					)

						__a        ={
						    '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',
						}

						__a        =UNetaDConditionModel(**__snake_case					)
						return model





			@property
			def 		__magic_name__    (		self					)   -> str:




						'''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 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						torch.manual_seed(0					)

						__a        =UNetaDModel(**self.dummy_super_res_kwargs					)
						return model





			@property
			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						# seeded differently to get different unet than `self.dummy_super_res_first`
						torch.manual_seed(1					)

						__a        =UNetaDModel(**self.dummy_super_res_kwargs					)
						return model





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =self.dummy_decoder
						__a        =self.dummy_text_proj
						__a        =self.dummy_text_encoder
						__a        =self.dummy_tokenizer
						__a        =self.dummy_super_res_first
						__a        =self.dummy_super_res_last

						__a        =UnCLIPScheduler(
						    variance_type='learned_range'					,  prediction_type='epsilon'					,  num_train_timesteps=1000					,  )

						__a        =UnCLIPScheduler(
						    variance_type='fixed_small_log'					,  prediction_type='epsilon'					,  num_train_timesteps=1000					,  )

						__a        =CLIPImageProcessor(crop_size=32					,  size=32					)

						__a        =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 		__magic_name__    (		self					,  __snake_case					,  __snake_case=0					,  __snake_case=True					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =floats_tensor((1, 3, 32, 32)					,  rng=random.Random(__snake_case					)					).to(__snake_case					)
						if str(__snake_case					).startswith('mps'					):
									__a        =torch.manual_seed(__snake_case					)
						else:
									__a        =torch.Generator(device=__snake_case					).manual_seed(__snake_case					)

						if pil_image:
									__a        =input_image * 0.5 + 0.5
									__a        =input_image.clamp(0					,  1					)
									__a        =input_image.cpu().permute(0					,  2					,  3					,  1					).float().numpy()
									__a        =DiffusionPipeline.numpy_to_pil(__snake_case					)[0]

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





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        ='cpu'

						__a        =self.get_dummy_components()

						__a        =self.pipeline_class(**__snake_case					)
						__a        =pipe.to(__snake_case					)

						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =self.get_dummy_inputs(__snake_case					,  pil_image=__snake_case					)

						__a        =pipe(**__snake_case					)
						__a        =output.images

						__a        =self.get_dummy_inputs(__snake_case					,  pil_image=__snake_case					)

						__a        =pipe(
						    **__snake_case					,  return_dict=__snake_case					,  )[0]

						__a        =image[0, -3:, -3:, -1]
						__a        =image_from_tuple[0, -3:, -3:, -1]

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

						__a        =np.array(
						    [
						        0.9997,
						        0.0002,
						        0.9997,
						        0.9997,
						        0.9969,
						        0.0023,
						        0.9997,
						        0.9969,
						        0.9970,
						    ]					)

						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 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        ='cpu'

						__a        =self.get_dummy_components()

						__a        =self.pipeline_class(**__snake_case					)
						__a        =pipe.to(__snake_case					)

						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =self.get_dummy_inputs(__snake_case					,  pil_image=__snake_case					)

						__a        =pipe(**__snake_case					)
						__a        =output.images

						__a        =self.get_dummy_inputs(__snake_case					,  pil_image=__snake_case					)

						__a        =pipe(
						    **__snake_case					,  return_dict=__snake_case					,  )[0]

						__a        =image[0, -3:, -3:, -1]
						__a        =image_from_tuple[0, -3:, -3:, -1]

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

						__a        =np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]					)

						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 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        ='cpu'

						__a        =self.get_dummy_components()

						__a        =self.pipeline_class(**__snake_case					)
						__a        =pipe.to(__snake_case					)

						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =self.get_dummy_inputs(__snake_case					,  pil_image=__snake_case					)
						__a        =[
						    pipeline_inputs['image'],
						    pipeline_inputs['image'],
						]

						__a        =pipe(**__snake_case					)
						__a        =output.images

						__a        =self.get_dummy_inputs(__snake_case					,  pil_image=__snake_case					)
						__a        =[
						    tuple_pipeline_inputs['image'],
						    tuple_pipeline_inputs['image'],
						]

						__a        =pipe(
						    **__snake_case					,  return_dict=__snake_case					,  )[0]

						__a        =image[0, -3:, -3:, -1]
						__a        =image_from_tuple[0, -3:, -3:, -1]

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

						__a        =np.array(
						    [
						        0.9997,
						        0.9989,
						        0.0008,
						        0.0021,
						        0.9960,
						        0.0018,
						        0.0014,
						        0.0002,
						        0.9933,
						    ]					)

						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 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =torch.device('cpu'					)






						class 	__magic_name__    :
									SCREAMING_SNAKE_CASE							   =						1


						__a        =self.get_dummy_components()

						__a        =self.pipeline_class(**__snake_case					)
						__a        =pipe.to(__snake_case					)

						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =torch.Generator(device=__snake_case					).manual_seed(0					)
						__a        =pipe.decoder.dtype
						__a        =1

						__a        =(
						    batch_size,
						    pipe.decoder.config.in_channels,
						    pipe.decoder.config.sample_size,
						    pipe.decoder.config.sample_size,
						)
						__a        =pipe.prepare_latents(
						    __snake_case					,  dtype=__snake_case					,  device=__snake_case					,  generator=__snake_case					,  latents=__snake_case					,  scheduler=DummyScheduler()					)

						__a        =(
						    batch_size,
						    pipe.super_res_first.config.in_channels // 2,
						    pipe.super_res_first.config.sample_size,
						    pipe.super_res_first.config.sample_size,
						)
						__a        =pipe.prepare_latents(
						    __snake_case					,  dtype=__snake_case					,  device=__snake_case					,  generator=__snake_case					,  latents=__snake_case					,  scheduler=DummyScheduler()					)

						__a        =self.get_dummy_inputs(__snake_case					,  pil_image=__snake_case					)

						__a        =pipe(
						    **__snake_case					,  decoder_latents=__snake_case					,  super_res_latents=__snake_case					).images

						__a        =self.get_dummy_inputs(__snake_case					,  pil_image=__snake_case					)
						# Don't pass image, instead pass embedding
						__a        =pipeline_inputs.pop('image'					)
						__a        =pipe.image_encoder(__snake_case					).image_embeds

						__a        =pipe(
						    **__snake_case					,  decoder_latents=__snake_case					,  super_res_latents=__snake_case					,  image_embeddings=__snake_case					,  ).images

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





			@skip_mps
			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =torch_device == 'cpu'

						# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
						__a        =1e-2

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





			@skip_mps
			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =torch_device == 'cpu'
						__a        =True
						__a        =[
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]

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





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =[
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]

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





			@skip_mps
			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						return super().test_dict_tuple_outputs_equivalent()





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




						'''simple docstring'''
						return super().test_save_load_local()





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




						'''simple docstring'''
						return super().test_save_load_optional_components()






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





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





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




						'''simple docstring'''
						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'					)
						__a        =load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'					)

						__a        =UnCLIPImageVariationPipeline.from_pretrained(
						    'kakaobrain/karlo-v1-alpha-image-variations'					,  torch_dtype=torch.floataa					)
						__a        =pipeline.to(__snake_case					)
						pipeline.set_progress_bar_config(disable=__snake_case					)

						__a        =torch.Generator(device='cpu'					).manual_seed(0					)
						__a        =pipeline(
						    __snake_case					,  generator=__snake_case					,  output_type='np'					,  )

						__a        =output.images[0]

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

						assert_mean_pixel_difference(__snake_case					,  __snake_case					,  15					)









import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowerCAmelCase					:			Optional[Any]  				=			False






class 	__magic_name__    (			unittest.TestCase      ):
			pass






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





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
						__a        =torch.manual_seed(0					)
						__a        =pipe(
						    image=__snake_case					,  generator=__snake_case					,  guidance_scale=7.5					,  num_inference_steps=50					,  output_type='numpy'					,  ).images

						__a        =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]					)

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






