code: 
  
  
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase   :str								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :Tuple								=    {
      '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
      '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
      '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
      '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
      '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
      '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
  }
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Tuple   					=       """opt"""
  		A_			:    List[Any]   					=       ["""past_key_values"""]
  
  
  
  
  
  
  
  		def __init__(      self	:		Union[str, Any]						,  _A	:		Dict=50272						,  _A	:		List[Any]=768						,  _A	:		Union[str, Any]=12						,  _A	:		Optional[Any]=3072						,  _A	:		List[Any]=2048						,  _A	:		List[str]=True						,  _A	:		List[Any]=False						,  _A	:		Optional[int]=None						,  _A	:		List[Any]=0.1						,  _A	:		List[Any]=0.0						,  _A	:		Optional[int]=12						,  _A	:		List[str]="relu"						,  _A	:		Union[str, Any]=0.0						,  _A	:		List[Any]=0.02						,  _A	:		List[Any]=True						,  _A	:		Union[str, Any]=1						,  _A	:		Optional[Any]=2						,  _A	:		List[str]=2						,  _A	:		Tuple=True						,  _A	:		Optional[Any]=True						,  **_A	:		Tuple						,  )						->				List[str]:
  			super().__init__(
  			    pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A						,  )
  			__magic_name__ :					Union[str, Any]  			=  vocab_size
  			__magic_name__ :					Optional[Any]  			=  max_position_embeddings
  			__magic_name__ :					Tuple  			=  num_attention_heads
  			__magic_name__ :					Optional[Any]  			=  word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
  			__magic_name__ :					List[Any]  			=  ffn_dim
  			__magic_name__ :					List[Any]  			=  hidden_size
  			__magic_name__ :					int  			=  num_hidden_layers
  			__magic_name__ :					Any  			=  dropout
  			__magic_name__ :					List[Any]  			=  attention_dropout
  			__magic_name__ :					Any  			=  activation_function
  			__magic_name__ :					List[Any]  			=  init_std
  			__magic_name__ :					List[Any]  			=  layerdrop
  			__magic_name__ :					Tuple  			=  use_cache
  			__magic_name__ :					Union[str, Any]  			=  do_layer_norm_before
  			# We keep these variables at `True` for backward compatibility.
  			__magic_name__ :					List[str]  			=  enable_bias
  			__magic_name__ :					Optional[int]  			=  layer_norm_elementwise_affine
  
  			# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
  			# with checkpoints that have been fine-tuned before transformers v4.20.1
  			# see https://github.com/facebookresearch/metaseq/pull/164
  			__magic_name__ :					str  			=  _remove_final_layer_norm
style_context: 
  
  
  '''simple docstring'''
  
  import random
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
  from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch
  
  
  enable_full_determinism()
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[Any]:
  			__magic_name__ :					Union[str, Any]  			=  1
  			__magic_name__ :					str  			=  3
  			__magic_name__ :					Optional[Any]  			=  (32, 32)
  
  			__magic_name__ :					Dict  			=  floats_tensor((batch_size, num_channels) + sizes						,  rng=random.Random(0    )    ).to(_A    )
  			return image
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
  			torch.manual_seed(0    )
  			__magic_name__ :					Dict  			=  UNetaDModel(
  			    block_out_channels=(32, 64)						,  layers_per_block=2						,  sample_size=32						,  in_channels=6						,  out_channels=3						,  down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,  up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,  )
  			return model
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
  			torch.manual_seed(0    )
  			__magic_name__ :					Union[str, 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
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				int:
  			__magic_name__ :					Dict  			=  'cpu'
  			__magic_name__ :					str  			=  self.dummy_uncond_unet
  			__magic_name__ :					Optional[int]  			=  DDIMScheduler()
  			__magic_name__ :					Optional[Any]  			=  self.dummy_vq_model
  
  			__magic_name__ :					Tuple  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
  			ldm.to(_A    )
  			ldm.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					Optional[int]  			=  self.dummy_image.to(_A    )
  
  			__magic_name__ :					List[Any]  			=  torch.Generator(device=_A    ).manual_seed(0    )
  			__magic_name__ :					str  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=2						,  output_type='numpy'    ).images
  
  			__magic_name__ :					List[Any]  			=  image[0, -3:, -3:, -1]
  
  			assert image.shape == (1, 64, 64, 3)
  			__magic_name__ :					Optional[Any]  			=  np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]    )
  
  			assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
  
  
  
  
  
  
  
  		@unittest.skipIf(torch_device != 'cuda'						,  'This test requires a GPU'    )
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[Any]:
  			__magic_name__ :					Union[str, Any]  			=  self.dummy_uncond_unet
  			__magic_name__ :					Tuple  			=  DDIMScheduler()
  			__magic_name__ :					Union[str, Any]  			=  self.dummy_vq_model
  
  			# put models in fp16
  			__magic_name__ :					Optional[int]  			=  unet.half()
  			__magic_name__ :					int  			=  vqvae.half()
  
  			__magic_name__ :					Optional[Any]  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
  			ldm.to(_A    )
  			ldm.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					str  			=  self.dummy_image.to(_A    )
  
  			__magic_name__ :					Union[str, Any]  			=  ldm(_A						,  num_inference_steps=2						,  output_type='numpy'    ).images
  
  			assert image.shape == (1, 64, 64, 3)
  
  
  
  
  
  
  
  @slow
  @require_torch
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
  			__magic_name__ :					Tuple  			=  load_image(
  			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
  			    '/vq_diffusion/teddy_bear_pool.png'    )
  			__magic_name__ :					Dict  			=  init_image.resize((64, 64)						,  resample=PIL_INTERPOLATION['lanczos']    )
  
  			__magic_name__ :					List[Any]  			=  LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'						,  device_map='auto'    )
  			ldm.set_progress_bar_config(disable=_A    )
  
  			__magic_name__ :					int  			=  torch.manual_seed(0    )
  			__magic_name__ :					Optional[Any]  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=20						,  output_type='numpy'    ).images
  
  			__magic_name__ :					List[str]  			=  image[0, -3:, -3:, -1]
  
  			assert image.shape == (1, 256, 256, 3)
  			__magic_name__ :					int  			=  np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]    )
  
  			assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2