



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       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     @property
     def 	__snake_case    (       self :							str				)->Union[str, Any]:
      torch.manual_seed(0				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	UNetaDModel(
          block_out_channels=(3_2, 6_4)     ,  layers_per_block=2     ,  sample_size=3_2     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,  up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,  )
      return model





     @property
     def 	__snake_case    (       self :							Any				)->int:
      torch.manual_seed(0				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	VQModel(
          block_out_channels=[3_2, 6_4]     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,  latent_channels=3     ,  )
      return model





     @property
     def 	__snake_case    (       self :							Optional[int]				)->str:
      torch.manual_seed(0				)
      __SCREAMING_SNAKE_CASE       :    int       =	CLIPTextConfig(
          bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=3_2     ,  intermediate_size=3_7     ,  layer_norm_eps=1E-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=1_0_0_0     ,  )
      return CLIPTextModel(UpperCamelCase				)






     def 	__snake_case    (       self :							Dict				)->str:
      __SCREAMING_SNAKE_CASE       :    str       =	self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE       :    str       =	DDIMScheduler()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.dummy_vq_model

      __SCREAMING_SNAKE_CASE       :    Dict       =	LDMPipeline(unet=UpperCamelCase     ,  vqvae=UpperCamelCase     ,  scheduler=UpperCamelCase				)
      ldm.to(UpperCamelCase				)
      ldm.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.manual_seed(0				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	ldm(generator=UpperCamelCase     ,  num_inference_steps=2     ,  output_type="numpy"				).images

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.manual_seed(0				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	ldm(generator=UpperCamelCase     ,  num_inference_steps=2     ,  output_type="numpy"     ,  return_dict=UpperCamelCase				)[0]

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

      assert image.shape == (1, 6_4, 6_4, 3)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							Union[str, Any]				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Any       =	LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"				)
      ldm.to(UpperCamelCase				)
      ldm.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Any       =	torch.manual_seed(0				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	ldm(generator=UpperCamelCase     ,  num_inference_steps=5     ,  output_type="numpy"				).images

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image[0, -3:, -3:, -1]

      assert image.shape == (1, 2_5_6, 2_5_6, 3)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	1E-2 if torch_device != "mps" else 3E-2

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











import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_lowerCamelCase						    =  logging.get_logger(__name__)

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							int     ,  *UpperCamelCase :							Optional[int]     ,  **UpperCamelCase :							Optional[int]				)->None:
      warnings.warn(
          "The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use LayoutLMv2ImageProcessor instead."     ,  UpperCamelCase     ,  )
      super().__init__(*UpperCamelCase     ,  **UpperCamelCase				)






