






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




				@property
				def 				__a   ( self     :int     ):
					torch.manual_seed(0     )
					UpperCamelCase__ :Optional[int]						=      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 				__a   ( self     :Optional[int]     ):
					UpperCamelCase__ :Optional[Any]						=      self.dummy_uncond_unet
					UpperCamelCase__ :List[str]						=      PNDMScheduler()

					UpperCamelCase__ :Union[str, Any]						=      PNDMPipeline(unet=lowerCamelCase__  ,     scheduler=lowerCamelCase__     )
					pndm.to(lowerCamelCase__     )
					pndm.set_progress_bar_config(disable=lowerCamelCase__     )

					UpperCamelCase__ :List[Any]						=      torch.manual_seed(0     )
					UpperCamelCase__ :List[Any]						=      pndm(generator=lowerCamelCase__  ,     num_inference_steps=20  ,     output_type="""numpy"""     ).images

					UpperCamelCase__ :Tuple						=      torch.manual_seed(0     )
					UpperCamelCase__ :Tuple						=      pndm(generator=lowerCamelCase__  ,     num_inference_steps=20  ,     output_type="""numpy"""  ,     return_dict=lowerCamelCase__     )[0]

					UpperCamelCase__ :Optional[Any]						=      image[0, -3:, -3:, -1]
					UpperCamelCase__ :Dict						=      image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					UpperCamelCase__ :Optional[Any]						=      np.array([1.0, 1.0, 0.0, 1.0, 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        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :Tuple						=      """google/ddpm-cifar10-32"""

					UpperCamelCase__ :Optional[Any]						=      UNetaDModel.from_pretrained(lowerCamelCase__     )
					UpperCamelCase__ :Dict						=      PNDMScheduler()

					UpperCamelCase__ :Union[str, Any]						=      PNDMPipeline(unet=lowerCamelCase__  ,     scheduler=lowerCamelCase__     )
					pndm.to(lowerCamelCase__     )
					pndm.set_progress_bar_config(disable=lowerCamelCase__     )
					UpperCamelCase__ :int						=      torch.manual_seed(0     )
					UpperCamelCase__ :Tuple						=      pndm(generator=lowerCamelCase__  ,     output_type="""numpy"""     ).images

					UpperCamelCase__ :str						=      image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					UpperCamelCase__ :Dict						=      np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]     )

					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




