



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       KandinskyVaaImgaImgPipeline
		_lowerCamelCase			  =       ['''image_embeds''', '''negative_image_embeds''', '''image''']
		_lowerCamelCase			  =       [
		    '''image_embeds''',
		    '''negative_image_embeds''',
		    '''image''',
		]
		_lowerCamelCase			  =       [
		    '''generator''',
		    '''height''',
		    '''width''',
		    '''strength''',
		    '''guidance_scale''',
		    '''num_inference_steps''',
		    '''return_dict''',
		    '''guidance_scale''',
		    '''num_images_per_prompt''',
		    '''output_type''',
		    '''return_dict''',
		]
		_lowerCamelCase			  =       False





		@property
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								return 3_2





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







								'''simple docstring'''

								return 3_2





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







								'''simple docstring'''

								return self.time_input_dim





		@property
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								return self.time_input_dim * 4





		@property
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								return 1_0_0





		@property
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								torch.manual_seed(0   )

								snake_case_   : List[str]						=			{
								    'in_channels': 4,
								    # Out channels is double in channels because predicts mean and variance
								    'out_channels': 8,
								    'addition_embed_type': 'image',
								    '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,
								    'encoder_hid_dim': self.text_embedder_hidden_size,
								    'encoder_hid_dim_type': 'image_proj',
								    'cross_attention_dim': self.cross_attention_dim,
								    'attention_head_dim': 4,
								    'resnet_time_scale_shift': 'scale_shift',
								    'class_embed_type': None,
								}

								snake_case_   : Union[str, Any]						=			UNetaDConditionModel(**_lowercase   )
								return model





		@property
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								return {
								    "block_out_channels": [3_2, 6_4],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}





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







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : Any						=			VQModel(**self.dummy_movq_kwargs   )
								return model





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			self.dummy_unet
								snake_case_   : str						=			self.dummy_movq

								snake_case_   : str						=			{
								    'num_train_timesteps': 1_0_0_0,
								    'beta_schedule': 'linear',
								    'beta_start': 0.0_0085,
								    'beta_end': 0.012,
								    'clip_sample': False,
								    'set_alpha_to_one': False,
								    'steps_offset': 0,
								    'prediction_type': 'epsilon',
								    'thresholding': False,
								}

								snake_case_   : Tuple						=			DDIMScheduler(**_lowercase   )

								snake_case_   : Dict						=			{
								    'unet': unet,
								    'scheduler': scheduler,
								    'movq': movq,
								}

								return components





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase=0   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			floats_tensor((1, self.text_embedder_hidden_size)							,    rng=random.Random(_lowercase   )   ).to(_lowercase   )
								snake_case_   : Optional[Any]						=			floats_tensor((1, self.text_embedder_hidden_size)							,    rng=random.Random(seed + 1   )   ).to(
								    _lowercase   )
								# create init_image
								snake_case_   : Optional[Any]						=			floats_tensor((1, 3, 6_4, 6_4)							,    rng=random.Random(_lowercase   )   ).to(_lowercase   )
								snake_case_   : Dict						=			image.cpu().permute(0							,    2							,    3							,    1   )[0]
								snake_case_   : Any						=			Image.fromarray(np.uinta(_lowercase   )   ).convert("""RGB"""   ).resize((2_5_6, 2_5_6)   )

								if str(_lowercase   ).startswith("""mps"""   ):
														snake_case_   : int						=			torch.manual_seed(_lowercase   )
								else:
														snake_case_   : Any						=			torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )
								snake_case_   : List[str]						=			{
								    'image': init_image,
								    'image_embeds': image_embeds,
								    'negative_image_embeds': negative_image_embeds,
								    'generator': generator,
								    'height': 6_4,
								    'width': 6_4,
								    'num_inference_steps': 1_0,
								    'guidance_scale': 7.0,
								    'strength': 0.2,
								    'output_type': 'np',
								}
								return inputs





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			'cpu'

								snake_case_   : Any						=			self.get_dummy_components()

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

								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : Optional[Any]						=			pipe(**self.get_dummy_inputs(_lowercase   )   )
								snake_case_   : Optional[int]						=			output.images

								snake_case_   : int						=			pipe(
								    **self.get_dummy_inputs(_lowercase   )							,    return_dict=_lowercase							,    )[0]

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

								assert image.shape == (1, 6_4, 6_4, 3)

								snake_case_   : Optional[int]						=			np.array(
								    [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263]   )
								assert (
								    np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
								), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
								), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'




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





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

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





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







								'''simple docstring'''

								snake_case_   : int						=			load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""   )

								snake_case_   : Any						=			load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""   )
								snake_case_   : Optional[int]						=			'A red cartoon frog, 4k'

								snake_case_   : Dict						=			KandinskyVaaPriorPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-prior"""							,    torch_dtype=torch.floataa   )
								pipe_prior.to(_lowercase   )

								snake_case_   : str						=			KandinskyVaaImgaImgPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-decoder"""							,    torch_dtype=torch.floataa   )
								snake_case_   : Optional[int]						=			pipeline.to(_lowercase   )

								pipeline.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : Optional[int]						=			torch.Generator(device="""cpu"""   ).manual_seed(0   )
								snake_case_   : str						=			pipe_prior(
								    _lowercase							,    generator=_lowercase							,    num_inference_steps=5							,    negative_prompt=""""""							,    ).to_tuple()

								snake_case_   : Optional[int]						=			pipeline(
								    image=_lowercase							,    image_embeds=_lowercase							,    negative_image_embeds=_lowercase							,    generator=_lowercase							,    num_inference_steps=1_0_0							,    height=7_6_8							,    width=7_6_8							,    strength=0.2							,    output_type="""np"""							,    )

								snake_case_   : Tuple						=			output.images[0]

								assert image.shape == (7_6_8, 7_6_8, 3)

								assert_mean_pixel_difference(_lowercase							,    _lowercase   )






"""simple docstring"""





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCAmelCase       :					Optional[Any]      					=					False



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

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





		@property
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								return 1_2





		@property
		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								return 1_2





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







								'''simple docstring'''

								return 3_2





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







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : List[Any]						=			VQModel(
								    block_out_channels=[3_2, 6_4]							,    in_channels=3							,    out_channels=3							,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,    latent_channels=3							,    num_vq_embeddings=self.num_embed							,    vq_embed_dim=3							,    )
								return model





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







								'''simple docstring'''

								snake_case_   : int						=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
								return tokenizer





		@property
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : Dict						=			CLIPTextConfig(
								    bos_token_id=0							,    eos_token_id=2							,    hidden_size=self.text_embedder_hidden_size							,    intermediate_size=3_7							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=1_0_0_0							,    )
								return CLIPTextModel(_lowercase   )





		@property
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								torch.manual_seed(0   )

								snake_case_   : Union[str, Any]						=			1_2
								snake_case_   : Tuple						=			1_2

								snake_case_   : Tuple						=			{
								    """attention_bias""": True,
								    """cross_attention_dim""": 3_2,
								    """attention_head_dim""": height * width,
								    """num_attention_heads""": 1,
								    """num_vector_embeds""": self.num_embed,
								    """num_embeds_ada_norm""": self.num_embeds_ada_norm,
								    """norm_num_groups""": 3_2,
								    """sample_size""": width,
								    """activation_fn""": """geglu-approximate""",
								}

								snake_case_   : Optional[Any]						=			TransformeraDModel(**_lowercase   )
								return model





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







								'''simple docstring'''

								snake_case_   : str						=			"""cpu"""

								snake_case_   : List[str]						=			self.dummy_vqvae
								snake_case_   : Any						=			self.dummy_text_encoder
								snake_case_   : Tuple						=			self.dummy_tokenizer
								snake_case_   : int						=			self.dummy_transformer
								snake_case_   : int						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : Dict						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase   )

								snake_case_   : Optional[Any]						=			VQDiffusionPipeline(
								    vqvae=_lowercase							,    text_encoder=_lowercase							,    tokenizer=_lowercase							,    transformer=_lowercase							,    scheduler=_lowercase							,    learned_classifier_free_sampling_embeddings=_lowercase							,    )
								snake_case_   : int						=			pipe.to(_lowercase   )
								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : List[Any]						=			"""teddy bear playing in the pool"""

								snake_case_   : Dict						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : List[Any]						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Optional[int]						=			output.images

								snake_case_   : List[Any]						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Dict						=			pipe(
								    [prompt]							,    generator=_lowercase							,    output_type="""np"""							,    return_dict=_lowercase							,    num_inference_steps=2   )[0]

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

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : Dict						=			np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]   )

								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   ) ->						str:







								'''simple docstring'''

								snake_case_   : int						=			"""cpu"""

								snake_case_   : List[Any]						=			self.dummy_vqvae
								snake_case_   : Optional[int]						=			self.dummy_text_encoder
								snake_case_   : List[Any]						=			self.dummy_tokenizer
								snake_case_   : Union[str, Any]						=			self.dummy_transformer
								snake_case_   : str						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(
								    learnable=_lowercase							,    hidden_size=self.text_embedder_hidden_size							,    length=tokenizer.model_max_length   )

								snake_case_   : Union[str, Any]						=			VQDiffusionPipeline(
								    vqvae=_lowercase							,    text_encoder=_lowercase							,    tokenizer=_lowercase							,    transformer=_lowercase							,    scheduler=_lowercase							,    learned_classifier_free_sampling_embeddings=_lowercase							,    )
								snake_case_   : Any						=			pipe.to(_lowercase   )
								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : Tuple						=			"""teddy bear playing in the pool"""

								snake_case_   : str						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Tuple						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Dict						=			output.images

								snake_case_   : Union[str, Any]						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Any						=			pipe(
								    [prompt]							,    generator=_lowercase							,    output_type="""np"""							,    return_dict=_lowercase							,    num_inference_steps=2   )[0]

								snake_case_   : Optional[Any]						=			image[0, -3:, -3:, -1]
								snake_case_   : int						=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : int						=			np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2.0
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2




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





		"""simple docstring"""






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







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""   )

								snake_case_   : str						=			VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""   )
								snake_case_   : Optional[Any]						=			pipeline.to(_lowercase   )
								pipeline.set_progress_bar_config(disable=_lowercase   )

								# requires GPU generator for gumbel softmax
								# don't use GPU generator in tests though
								snake_case_   : Any						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Optional[int]						=			pipeline(
								    """teddy bear playing in the pool"""							,    num_images_per_prompt=1							,    generator=_lowercase							,    output_type="""np"""							,    )

								snake_case_   : Union[str, Any]						=			output.images[0]

								assert image.shape == (2_5_6, 2_5_6, 3)
								assert np.abs(expected_image - image   ).max() < 2.0

