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 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       torch.manual_seed(0	)
       UpperCamelCase__										=      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	)		->      Optional[Any]:





       '''simple docstring'''





       torch.manual_seed(0	)
       UpperCamelCase__										=      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	)		->      Union[str, Any]:





       '''simple docstring'''





       torch.manual_seed(0	)
       UpperCamelCase__										=      CLIPTextConfig(
           bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=32   ,				intermediate_size=37   ,				layer_norm_eps=1E-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)
       return CLIPTextModel(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      self.dummy_uncond_unet
       UpperCamelCase__										=      DDIMScheduler()
       UpperCamelCase__										=      self.dummy_vq_model

       UpperCamelCase__										=      LDMPipeline(unet=SCREAMING_SNAKE_CASE_   ,				vqvae=SCREAMING_SNAKE_CASE_   ,				scheduler=SCREAMING_SNAKE_CASE_	)
       ldm.to(SCREAMING_SNAKE_CASE_	)
       ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

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

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

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase__										=      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
       UpperCamelCase__										=      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 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''	)
       ldm.to(SCREAMING_SNAKE_CASE_	)
       ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

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

       assert image.shape == (1, 256, 256, 3)
       UpperCamelCase__										=      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
       UpperCamelCase__										=      1E-2 if torch_device != '''mps''' else 3E-2

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




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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''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   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="vit_mae"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=768   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=3072   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=224   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=8   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=0.75   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       super().__init__(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      layer_norm_eps
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      qkv_bias
       UpperCamelCase__										=      decoder_num_attention_heads
       UpperCamelCase__										=      decoder_hidden_size
       UpperCamelCase__										=      decoder_num_hidden_layers
       UpperCamelCase__										=      decoder_intermediate_size
       UpperCamelCase__										=      mask_ratio
       UpperCamelCase__										=      norm_pix_loss



