"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class    a_   (  unittest.TestCase      ):




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							torch.manual_seed(0  )
							lowerCAmelCase__   = 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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = self.dummy_uncond_unet
							lowerCAmelCase__   = KarrasVeScheduler()

							lowerCAmelCase__   = KarrasVePipeline(unet=snake_case__	,    scheduler=snake_case__  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = torch.manual_seed(0  )
							lowerCAmelCase__   = pipe(num_inference_steps=2	,    generator=snake_case__	,    output_type="""numpy"""  ).images

							lowerCAmelCase__   = torch.manual_seed(0  )
							lowerCAmelCase__   = pipe(num_inference_steps=2	,    generator=snake_case__	,    output_type="""numpy"""	,    return_dict=snake_case__  )[0]

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

							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase__   = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]  )

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

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = """google/ncsnpp-celebahq-256"""
							lowerCAmelCase__   = UNetaDModel.from_pretrained(snake_case__  )
							lowerCAmelCase__   = KarrasVeScheduler()

							lowerCAmelCase__   = KarrasVePipeline(unet=snake_case__	,    scheduler=snake_case__  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = torch.manual_seed(0  )
							lowerCAmelCase__   = pipe(num_inference_steps=20	,    generator=snake_case__	,    output_type="""numpy"""  ).images

							lowerCAmelCase__   = image[0, -3:, -3:, -1]
							assert image.shape == (1, 256, 256, 3)
							lowerCAmelCase__   = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]  )

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





"""simple docstring"""




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)  # pylint: disable=invalid-name

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  Optional[int]	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=768  ):
							super().__init__(snake_case__  )
							lowerCAmelCase__   = proj_size

							lowerCAmelCase__   = CLIPVisionModel(snake_case__  )
							lowerCAmelCase__   = PaintByExampleMapper(snake_case__  )
							lowerCAmelCase__   = nn.LayerNorm(config.hidden_size  )
							lowerCAmelCase__   = nn.Linear(config.hidden_size	,    self.proj_size  )

							# uncondition for scaling
							lowerCAmelCase__   = nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[Any]=False  ):
							lowerCAmelCase__   = self.model(pixel_values=snake_case__  )
							lowerCAmelCase__   = clip_output.pooler_output
							lowerCAmelCase__   = self.mapper(latent_states[:, None]  )
							lowerCAmelCase__   = self.final_layer_norm(snake_case__  )
							lowerCAmelCase__   = self.proj_out(snake_case__  )
							if return_uncond_vector:
									return latent_states, self.uncond_vector

							return latent_states

class    a_   (  nn.Module      ):




					def __init__(						self    :  int	,    snake_case__    :  Dict  ):
							super().__init__()
							lowerCAmelCase__   = (config.num_hidden_layers + 1) // 5
							lowerCAmelCase__   = config.hidden_size
							lowerCAmelCase__   = 1
							lowerCAmelCase__   = nn.ModuleList(
							    [
							        BasicTransformerBlock(snake_case__	,    snake_case__	,    snake_case__	,    activation_fn="""gelu"""	,    attention_bias=snake_case__  )
							        for _ in range(snake_case__  )
							    ]  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Optional[int]  ):
							for block in self.blocks:
									lowerCAmelCase__   = block(snake_case__  )

							return hidden_states




