






"""simple docstring"""



import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 			a	(  A__,							unittest.TestCase      ):
    UpperCAmelCase_					:	Any      	=RePaintPipeline
    UpperCAmelCase_					:	List[Any]      	=IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
    UpperCAmelCase_					:	List[Any]      	=PipelineTesterMixin.required_optional_params - {
        "latents",
        "num_images_per_prompt",
        "callback",
        "callback_steps",
    }
    UpperCAmelCase_					:	List[str]      	=IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase_					:	Tuple      	=False







    def      UpperCamelCase_ (     self			):
      torch.manual_seed(0			)
      torch.manual_seed(0			)
      lowercase				       =			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')				,						)
      lowercase				       =			RePaintScheduler()
      lowercase				       =			{"""unet""": unet, """scheduler""": scheduler}
      return components







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase=0			):
      if str(__A			).startswith('mps'			):
        lowercase				       =			torch.manual_seed(__A			)
      else:
        lowercase				       =			torch.Generator(device=__A			).manual_seed(__A			)
      lowercase				       =			np.random.RandomState(__A			).standard_normal((1, 3, 3_2, 3_2)			)
      lowercase				       =			torch.from_numpy(__A			).to(device=__A				,						dtype=torch.floataa			)
      lowercase				       =			(image > 0).to(device=__A				,						dtype=torch.floataa			)
      lowercase				       =			{
          """image""": image,
          """mask_image""": mask,
          """generator""": generator,
          """num_inference_steps""": 5,
          """eta""": 0.0,
          """jump_length""": 2,
          """jump_n_sample""": 2,
          """output_type""": """numpy""",
      }
      return inputs







    def      UpperCamelCase_ (     self			):
      lowercase				       =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowercase				       =			self.get_dummy_components()
      lowercase				       =			RePaintPipeline(**__A			)
      lowercase				       =			sd_pipe.to(__A			)
      sd_pipe.set_progress_bar_config(disable=__A			)

      lowercase				       =			self.get_dummy_inputs(__A			)
      lowercase				       =			sd_pipe(**__A			).images
      lowercase				       =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      lowercase				       =			np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







    @skip_mps
    def      UpperCamelCase_ (     self			):
      return super().test_save_load_local()







    @unittest.skip('non-deterministic pipeline'			)
    def      UpperCamelCase_ (     self			):
      return super().test_inference_batch_single_identical()







    @skip_mps
    def      UpperCamelCase_ (     self			):
      return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def      UpperCamelCase_ (     self			):
      return super().test_save_load_optional_components()







    @skip_mps
    def      UpperCamelCase_ (     self			):
      return super().test_attention_slicing_forward_pass()


@nightly
@require_torch_gpu
class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
          'repaint/celeba_hq_256.png'			)
      lowercase				       =			load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'			)
      lowercase				       =			load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
          'repaint/celeba_hq_256_result.npy'			)

      lowercase				       =			"""google/ddpm-ema-celebahq-256"""
      lowercase				       =			UNetaDModel.from_pretrained(__A			)
      lowercase				       =			RePaintScheduler.from_pretrained(__A			)

      lowercase				       =			RePaintPipeline(unet=__A				,						scheduler=__A			).to(__A			)
      repaint.set_progress_bar_config(disable=__A			)
      repaint.enable_attention_slicing()

      lowercase				       =			torch.manual_seed(0			)
      lowercase				       =			repaint(
          __A				,						__A				,						num_inference_steps=2_5_0				,						eta=0.0				,						jump_length=1_0				,						jump_n_sample=1_0				,						generator=__A				,						output_type='np'				,						)
      lowercase				       =			output.images[0]

      assert image.shape == (2_5_6, 2_5_6, 3)
      assert np.abs(expected_image - image			).mean() < 1e-2




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__UpperCAmelCase 					=    logging.get_logger(__name__)

__UpperCAmelCase 					=    {
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
				import detectrona




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Optional[int]												=		"layoutlmv2"






			def __init__(   self			,  __A=3_0522			,  __A=768			,  __A=12			,  __A=12			,  __A=3072			,  __A="gelu"			,  __A=0.1			,  __A=0.1			,  __A=512			,  __A=2			,  __A=0.0_2			,  __A=1E-12			,  __A=0			,  __A=1024			,  __A=128			,  __A=32			,  __A=True			,  __A=256			,  __A=64			,  __A=True			,  __A=[7, 7, 256]			,  __A=128			,  __A=128			,  __A=True			,  __A=True			,  __A=False			,  __A=None			,  **__A			,  )     ->	List[str]:
						super().__init__(
						    vocab_size=__A			,  hidden_size=__A			,  num_hidden_layers=__A			,  num_attention_heads=__A			,  intermediate_size=__A			,  hidden_act=__A			,  hidden_dropout_prob=__A			,  attention_probs_dropout_prob=__A			,  max_position_embeddings=__A			,  type_vocab_size=__A			,  initializer_range=__A			,  layer_norm_eps=__A			,  pad_token_id=__A			,  **__A			,  )
						lowerCAmelCase_       :Optional[Any]     	=    max_ad_position_embeddings
						lowerCAmelCase_       :Dict     	=    max_rel_pos
						lowerCAmelCase_       :str     	=    rel_pos_bins
						lowerCAmelCase_       :List[str]     	=    fast_qkv
						lowerCAmelCase_       :Union[str, Any]     	=    max_rel_ad_pos
						lowerCAmelCase_       :List[str]     	=    rel_ad_pos_bins
						lowerCAmelCase_       :Any     	=    convert_sync_batchnorm
						lowerCAmelCase_       :Dict     	=    image_feature_pool_shape
						lowerCAmelCase_       :str     	=    coordinate_size
						lowerCAmelCase_       :Tuple     	=    shape_size
						lowerCAmelCase_       :Tuple     	=    has_relative_attention_bias
						lowerCAmelCase_       :Union[str, Any]     	=    has_spatial_attention_bias
						lowerCAmelCase_       :int     	=    has_visual_segment_embedding
						lowerCAmelCase_       :Any     	=    (
						    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
						)






			@classmethod
			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						return {
						    "MODEL.MASK_ON": True,
						    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
						    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
						    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
						    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
						    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
						    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
						    "MODEL.POST_NMS_TOPK_TEST": 1000,
						    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
						    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
						    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
						    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
						    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
						    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
						    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
						    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
						    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
						    "MODEL.RESNETS.DEPTH": 101,
						    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
						    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.RESNETS.NUM_GROUPS": 32,
						    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
						    "MODEL.RESNETS.STRIDE_IN_1X1": False,
						}






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_       :Any     	=    detectrona.config.get_cfg()
						for k, v in self.detectrona_config_args.items():
									lowerCAmelCase_       :int     	=    k.split("""."""			)
									lowerCAmelCase_       :List[Any]     	=    detectrona_config
									for attribute in attributes[:-1]:
												lowerCAmelCase_       :List[Any]     	=    getattr(__A			,  __A			)
									setattr(__A			,  attributes[-1]			,  __A			)

						return detectrona_config






