

import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 			lowercase    (	unittest.TestCase      ):




				@property
				def    a							( self	):
											torch.manual_seed(0	)
											snake_case_          =   UNetaDModel(
											    block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,       up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,       )
											return model




				@property
				def    a							( self	):
											torch.manual_seed(0	)
											snake_case_          =   VQModel(
											    block_out_channels=[32, 64]				,       in_channels=3				,       out_channels=3				,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,       latent_channels=3				,       )
											return model




				@property
				def    a							( self	):
											torch.manual_seed(0	)
											snake_case_          =   CLIPTextConfig(
											    bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1e-0_5				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1000				,       )
											return CLIPTextModel(snake_case	)




				def    a							( self	):
											snake_case_          =   self.dummy_uncond_unet
											snake_case_          =   DDIMScheduler()
											snake_case_          =   self.dummy_vq_model

											snake_case_          =   LDMPipeline(unet=snake_case				,       vqvae=snake_case				,       scheduler=snake_case	)
											ldm.to(snake_case	)
											ldm.set_progress_bar_config(disable=snake_case	)

											snake_case_          =   torch.manual_seed(0	)
											snake_case_          =   ldm(generator=snake_case				,       num_inference_steps=2				,       output_type='numpy'	).images

											snake_case_          =   torch.manual_seed(0	)
											snake_case_          =   ldm(generator=snake_case				,       num_inference_steps=2				,       output_type='numpy'				,       return_dict=snake_case	)[0]

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

											assert image.shape == (1, 64, 64, 3)
											snake_case_          =   np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]	)
											snake_case_          =   1e-2 if torch_device != 'mps' else 3e-2

											assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance







@slow
@require_torch
class 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											snake_case_          =   LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'	)
											ldm.to(snake_case	)
											ldm.set_progress_bar_config(disable=snake_case	)

											snake_case_          =   torch.manual_seed(0	)
											snake_case_          =   ldm(generator=snake_case				,       num_inference_steps=5				,       output_type='numpy'	).images

											snake_case_          =   image[0, -3:, -3:, -1]

											assert image.shape == (1, 256, 256, 3)
											snake_case_          =   np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]	)
											snake_case_          =   1e-2 if torch_device != 'mps' else 3e-2

											assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel




