





'''simple docstring'''


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       a_   (				unittest.TestCase							):





     @property
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        torch.manual_seed(0						)
        lowercase_			:Tuple    			= 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 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        torch.manual_seed(0						)
        lowercase_			:List[Any]    			= 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 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        torch.manual_seed(0						)
        lowercase_			:Optional[Any]    			= 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=1_000	,      )
        return CLIPTextModel(lowercase						)







     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= self.dummy_uncond_unet
        lowercase_			:int    			= DDIMScheduler()
        lowercase_			:Any    			= self.dummy_vq_model

        lowercase_			:int    			= LDMPipeline(unet=lowercase	,      vqvae=lowercase	,      scheduler=lowercase						)
        ldm.to(lowercase						)
        ldm.set_progress_bar_config(disable=lowercase						)

        lowercase_			:List[Any]    			= torch.manual_seed(0						)
        lowercase_			:List[str]    			= ldm(generator=lowercase	,      num_inference_steps=2	,      output_type="numpy"						).images

        lowercase_			:str    			= torch.manual_seed(0						)
        lowercase_			:str    			= ldm(generator=lowercase	,      num_inference_steps=2	,      output_type="numpy"	,      return_dict=lowercase						)[0]

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

        assert image.shape == (1, 64, 64, 3)
        lowercase_			:Optional[int]    			= 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]						)
        lowercase_			:List[str]    			= 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       a_   (				unittest.TestCase							):





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        lowercase_			:int    			= LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"						)
        ldm.to(lowercase						)
        ldm.set_progress_bar_config(disable=lowercase						)

        lowercase_			:Dict    			= torch.manual_seed(0						)
        lowercase_			:List[str]    			= ldm(generator=lowercase	,      num_inference_steps=5	,      output_type="numpy"						).images

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

        assert image.shape == (1, 256, 256, 3)
        lowercase_			:List[Any]    			= 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]						)
        lowercase_			:int    			= 1e-2 if torch_device != "mps" else 3e-2

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









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase					:     Optional[Any]												=logging.get_logger(__name__)

lowerCAmelCase					:     Tuple												={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class       a_   (				_lowerCAmelCase							):
     __A      =			"vit_mae"





     def __init__( self		:					Any	,      lowercase		:					int=768	,      lowercase		:					Tuple=12	,      lowercase		:					str=12	,      lowercase		:					Optional[Any]=3_072	,      lowercase		:					List[Any]="gelu"	,      lowercase		:					Tuple=0.0	,      lowercase		:					Union[str, Any]=0.0	,      lowercase		:					str=0.02	,      lowercase		:					Optional[int]=1e-1_2	,      lowercase		:					List[Any]=224	,      lowercase		:					str=16	,      lowercase		:					List[str]=3	,      lowercase		:					Optional[Any]=True	,      lowercase		:					int=16	,      lowercase		:					Optional[Any]=512	,      lowercase		:					Optional[Any]=8	,      lowercase		:					Optional[Any]=2_048	,      lowercase		:					List[str]=0.75	,      lowercase		:					str=False	,      **lowercase		:					Union[str, Any]	,      ):





        """simple docstring"""




        super().__init__(**lowercase						)

        lowercase_			:Any    			= hidden_size
        lowercase_			:Optional[Any]    			= num_hidden_layers
        lowercase_			:Optional[Any]    			= num_attention_heads
        lowercase_			:int    			= intermediate_size
        lowercase_			:Optional[int]    			= hidden_act
        lowercase_			:str    			= hidden_dropout_prob
        lowercase_			:Optional[Any]    			= attention_probs_dropout_prob
        lowercase_			:str    			= initializer_range
        lowercase_			:Optional[int]    			= layer_norm_eps
        lowercase_			:str    			= image_size
        lowercase_			:Union[str, Any]    			= patch_size
        lowercase_			:Dict    			= num_channels
        lowercase_			:Any    			= qkv_bias
        lowercase_			:Optional[int]    			= decoder_num_attention_heads
        lowercase_			:Optional[Any]    			= decoder_hidden_size
        lowercase_			:Union[str, Any]    			= decoder_num_hidden_layers
        lowercase_			:List[Any]    			= decoder_intermediate_size
        lowercase_			:Optional[Any]    			= mask_ratio
        lowercase_			:Optional[Any]    			= norm_pix_loss


