




"""simple docstring"""






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

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


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






class lowerCAmelCase__      (	SCREAMING_SNAKE_CASE__		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=768    ):
							super().__init__(__lowerCAmelCase    )
							_lowerCamelCase	:     Dict       =       proj_size

							_lowerCamelCase	:     Union[str, Any]       =       CLIPVisionModel(__lowerCAmelCase    )
							_lowerCamelCase	:     Optional[int]       =       PaintByExampleMapper(__lowerCAmelCase    )
							_lowerCamelCase	:     Optional[int]       =       nn.LayerNorm(config.hidden_size    )
							_lowerCamelCase	:     int       =       nn.Linear(config.hidden_size    , self.proj_size    )

							# uncondition for scaling
							_lowerCamelCase	:     List[str]       =       nn.Parameter(torch.randn((1, 1, self.proj_size)    )    )






			def    A_       (    self    , lowercase    , lowercase=False    ):
							_lowerCamelCase	:     Any       =       self.model(pixel_values=__lowerCAmelCase    )
							_lowerCamelCase	:     str       =       clip_output.pooler_output
							_lowerCamelCase	:     str       =       self.mapper(latent_states[:, None]    )
							_lowerCamelCase	:     Optional[int]       =       self.final_layer_norm(__lowerCAmelCase    )
							_lowerCamelCase	:     Optional[Any]       =       self.proj_out(__lowerCAmelCase    )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__      (	nn.Module		):






			'''simple docstring'''



			def __init__(    self    , lowercase    ):
							super().__init__()
							_lowerCamelCase	:     int       =       (config.num_hidden_layers + 1) // 5
							_lowerCamelCase	:     str       =       config.hidden_size
							_lowerCamelCase	:     int       =       1
							_lowerCamelCase	:     List[Any]       =       nn.ModuleList(
							    [
							        BasicTransformerBlock(__lowerCAmelCase    , __lowerCAmelCase    , __lowerCAmelCase    , activation_fn='gelu'    , attention_bias=__lowerCAmelCase    )
							        for _ in range(__lowerCAmelCase    )
							    ]    )






			def    A_       (    self    , lowercase    ):
							for block in self.blocks:
											_lowerCamelCase	:     Any       =       block(__lowerCAmelCase    )

							return hidden_states







'''simple docstring'''

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      A     (   unittest.TestCase    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: 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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_uncond_unet
       UpperCamelCase_					: List[str]            =				PNDMScheduler()

       UpperCamelCase_					: Dict            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: int            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Optional[int]            =				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      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				"""google/ddpm-cifar10-32"""

       UpperCamelCase_					: List[str]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: int            =				PNDMScheduler()

       UpperCamelCase_					: Any            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Tuple            =				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Union[str, Any]            =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

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






