code: 
  
  
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PretrainedConfig, PreTrainedTokenizer
  from ...onnx import OnnxConfig, PatchingSpec
  from ...utils import TensorType, is_torch_available, logging
  
  
  lowerCAmelCase   :int								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :List[Any]								=    {
      '''microsoft/layoutlm-base-uncased''': (
          '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
      ),
      '''microsoft/layoutlm-large-uncased''': (
          '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
      ),
  }
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       """layoutlm"""
  
  
  
  
  
  
  
  		def __init__(      self	:		List[Any]						,  _A	:		List[Any]=30522						,  _A	:		Dict=768						,  _A	:		Optional[Any]=12						,  _A	:		Any=12						,  _A	:		Optional[Any]=3072						,  _A	:		int="gelu"						,  _A	:		List[str]=0.1						,  _A	:		Dict=0.1						,  _A	:		str=512						,  _A	:		str=2						,  _A	:		Union[str, Any]=0.02						,  _A	:		Dict=1E-12						,  _A	:		Optional[Any]=0						,  _A	:		Optional[Any]="absolute"						,  _A	:		int=True						,  _A	:		Union[str, Any]=1024						,  **_A	:		Optional[Any]						,  )						->				Optional[int]:
  			super().__init__(pad_token_id=_A						,  **_A    )
  			__magic_name__ :					str  			=  vocab_size
  			__magic_name__ :					Optional[int]  			=  hidden_size
  			__magic_name__ :					Optional[Any]  			=  num_hidden_layers
  			__magic_name__ :					List[Any]  			=  num_attention_heads
  			__magic_name__ :					Any  			=  hidden_act
  			__magic_name__ :					List[str]  			=  intermediate_size
  			__magic_name__ :					Optional[int]  			=  hidden_dropout_prob
  			__magic_name__ :					Optional[int]  			=  attention_probs_dropout_prob
  			__magic_name__ :					Union[str, Any]  			=  max_position_embeddings
  			__magic_name__ :					Union[str, Any]  			=  type_vocab_size
  			__magic_name__ :					Dict  			=  initializer_range
  			__magic_name__ :					List[Any]  			=  layer_norm_eps
  			__magic_name__ :					Tuple  			=  position_embedding_type
  			__magic_name__ :					Union[str, Any]  			=  use_cache
  			__magic_name__ :					List[str]  			=  max_ad_position_embeddings
  
  
  
  
  
  
  
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		int						,  _A	:		PretrainedConfig						,  _A	:		str = "default"						,  _A	:		List[PatchingSpec] = None						,  )						->				List[Any]:
  			super().__init__(_A						,  task=_A						,  patching_specs=_A    )
  			__magic_name__ :					int  			=  config.max_ad_position_embeddings - 1
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Mapping[str, Mapping[int, str]]:
  			return OrderedDict(
  			    [
  			        ('input_ids', {0: 'batch', 1: 'sequence'}),
  			        ('bbox', {0: 'batch', 1: 'sequence'}),
  			        ('attention_mask', {0: 'batch', 1: 'sequence'}),
  			        ('token_type_ids', {0: 'batch', 1: 'sequence'}),
  			    ]    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  _A	:		PreTrainedTokenizer						,  _A	:		int = -1						,  _A	:		int = -1						,  _A	:		bool = False						,  _A	:		Optional[TensorType] = None						,  )						->				Mapping[str, Any]:
  			__magic_name__ :					List[str]  			=  super().generate_dummy_inputs(
  			    _A						,  batch_size=_A						,  seq_length=_A						,  is_pair=_A						,  framework=_A    )
  
  			# Generate a dummy bbox
  			__magic_name__ :					Tuple  			=  [48, 84, 73, 128]
  
  			if not framework == TensorType.PYTORCH:
  				raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'    )
  
  			if not is_torch_available():
  				raise ValueError('Cannot generate dummy inputs without PyTorch installed.'    )
  			import torch
  
  			__magic_name__			,       __magic_name__ :					Dict  			=  input_dict['input_ids'].shape
  			__magic_name__ :					List[Any]  			=  torch.tensor([*[box] * seq_length]    ).tile(_A						,  1						,  1    )
  			return input_dict
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