




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class       UpperCAmelCase_   ( A		,   unittest.TestCase							):




							'''simple docstring'''



							a__    	=  PaintByExamplePipeline
							a__    	=  IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
							a__    	=  IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							a__    	=  frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       Tuple:
										torch.manual_seed(0      )
										SCREAMING_SNAKE_CASE     =       UNetaDConditionModel(
										    block_out_channels=(32, 64)							,  layers_per_block=2							,  sample_size=32							,  in_channels=9							,  out_channels=4							,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")							,  cross_attention_dim=32							,  )
										SCREAMING_SNAKE_CASE     =       PNDMScheduler(skip_prk_steps=a      )
										torch.manual_seed(0      )
										SCREAMING_SNAKE_CASE     =       AutoencoderKL(
										    block_out_channels=[32, 64]							,  in_channels=3							,  out_channels=3							,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,  latent_channels=4							,  )
										torch.manual_seed(0      )
										SCREAMING_SNAKE_CASE     =       CLIPVisionConfig(
										    hidden_size=32							,  projection_dim=32							,  intermediate_size=37							,  layer_norm_eps=1E-0_5							,  num_attention_heads=4							,  num_hidden_layers=5							,  image_size=32							,  patch_size=4							,  )
										SCREAMING_SNAKE_CASE     =       PaintByExampleImageEncoder(a							,  proj_size=32      )
										SCREAMING_SNAKE_CASE     =       CLIPImageProcessor(crop_size=32							,  size=32      )

										SCREAMING_SNAKE_CASE     =       {
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """image_encoder""": image_encoder,
										    """safety_checker""": None,
										    """feature_extractor""": feature_extractor,
										}
										return components





							def  _UpperCAmelCase  (       self							:						Optional[int]							,  a							:						Tuple      )      ->       Union[str, Any]:
										SCREAMING_SNAKE_CASE     =       np.array(image.convert("""RGB"""      )      )
										SCREAMING_SNAKE_CASE     =       image[None].transpose(0							,  3							,  1							,  2      )
										SCREAMING_SNAKE_CASE     =       torch.from_numpy(a      ).to(dtype=torch.floataa      ) / 127.5 - 1.0
										return image





							def  _UpperCAmelCase  (       self							:						Any							,  a							:						Tuple="cpu"							,  a							:						List[str]=0      )      ->       Tuple:
										# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
										SCREAMING_SNAKE_CASE     =       floats_tensor((1, 3, 32, 32)							,  rng=random.Random(a      )      ).to(a      )
										SCREAMING_SNAKE_CASE     =       image.cpu().permute(0							,  2							,  3							,  1      )[0]
										SCREAMING_SNAKE_CASE     =       Image.fromarray(np.uinta(a      )      ).convert("""RGB"""      ).resize((64, 64)      )
										SCREAMING_SNAKE_CASE     =       Image.fromarray(np.uinta(image + 4      )      ).convert("""RGB"""      ).resize((64, 64)      )
										SCREAMING_SNAKE_CASE     =       Image.fromarray(np.uinta(a      )      ).convert("""RGB"""      ).resize((32, 32)      )

										if str(a      ).startswith("""mps"""      ):
													SCREAMING_SNAKE_CASE     =       torch.manual_seed(a      )
										else:
													SCREAMING_SNAKE_CASE     =       torch.Generator(device=a      ).manual_seed(a      )
										SCREAMING_SNAKE_CASE     =       {
										    """example_image""": example_image,
										    """image""": init_image,
										    """mask_image""": mask_image,
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}
										return inputs





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       Optional[int]:
										SCREAMING_SNAKE_CASE     =       self.get_dummy_components()

										# make sure here that pndm scheduler skips prk
										SCREAMING_SNAKE_CASE     =       PaintByExamplePipeline(**a      )
										SCREAMING_SNAKE_CASE     =       pipe.to("""cpu"""      )
										pipe.set_progress_bar_config(disable=a      )

										SCREAMING_SNAKE_CASE     =       self.get_dummy_inputs()
										SCREAMING_SNAKE_CASE     =       pipe(**a      )
										SCREAMING_SNAKE_CASE     =       output.images

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

										assert image.shape == (1, 64, 64, 3)
										SCREAMING_SNAKE_CASE     =       np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2





							def  _UpperCAmelCase  (       self							:						int      )      ->       Any:
										SCREAMING_SNAKE_CASE     =       """cpu"""
										SCREAMING_SNAKE_CASE     =       self.get_dummy_inputs()
										inputs.pop("""mask_image"""      )
										SCREAMING_SNAKE_CASE     =       self.convert_to_pt(inputs.pop("""image"""      )      )
										SCREAMING_SNAKE_CASE     =       image.clamp(0							,  1      ) / 2

										# make sure here that pndm scheduler skips prk
										SCREAMING_SNAKE_CASE     =       PaintByExamplePipeline(**self.get_dummy_components()      )
										SCREAMING_SNAKE_CASE     =       pipe.to(a      )
										pipe.set_progress_bar_config(disable=a      )

										SCREAMING_SNAKE_CASE     =       pipe(image=a							,  mask_image=mask_image[:, 0]							,  **a      )
										SCREAMING_SNAKE_CASE     =       output.images

										SCREAMING_SNAKE_CASE     =       image.cpu().permute(0							,  2							,  3							,  1      )[0]
										SCREAMING_SNAKE_CASE     =       mask_image.cpu().permute(0							,  2							,  3							,  1      )[0]

										SCREAMING_SNAKE_CASE     =       Image.fromarray(np.uinta(a      )      ).convert("""RGB"""      )
										SCREAMING_SNAKE_CASE     =       Image.fromarray(np.uinta(a      )      ).convert("""RGB"""      )

										SCREAMING_SNAKE_CASE     =       pipe(**self.get_dummy_inputs()      )
										SCREAMING_SNAKE_CASE     =       output.images

										assert out_a.shape == (1, 64, 64, 3)
										assert np.abs(out_a.flatten() - out_a.flatten()      ).max() < 5E-2







							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       int:
										super().test_inference_batch_single_identical(expected_max_diff=3E-3      )





@slow
@require_torch_gpu
class       UpperCAmelCase_   ( unittest.TestCase							):




							'''simple docstring'''



							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       List[Any]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







							def  _UpperCAmelCase  (       self							:						Tuple      )      ->       Tuple:
										# make sure here that pndm scheduler skips prk
										SCREAMING_SNAKE_CASE     =       load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/paint_by_example/dog_in_bucket.png"""      )
										SCREAMING_SNAKE_CASE     =       load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/paint_by_example/mask.png"""      )
										SCREAMING_SNAKE_CASE     =       load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/paint_by_example/panda.jpg"""      )

										SCREAMING_SNAKE_CASE     =       PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""      )
										SCREAMING_SNAKE_CASE     =       pipe.to(a      )
										pipe.set_progress_bar_config(disable=a      )

										SCREAMING_SNAKE_CASE     =       torch.manual_seed(321      )
										SCREAMING_SNAKE_CASE     =       pipe(
										    image=a							,  mask_image=a							,  example_image=a							,  generator=a							,  guidance_scale=5.0							,  num_inference_steps=50							,  output_type="""np"""							,  )

										SCREAMING_SNAKE_CASE     =       output.images

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

										assert image.shape == (1, 512, 512, 3)
										SCREAMING_SNAKE_CASE     =       np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2












import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       ConvBertConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       ConvBertModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       load_tf_weights_in_convbert(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       TFConvBertModel.from_pretrained(SCREAMING_SNAKE_CASE       ,						from_pt=SCREAMING_SNAKE_CASE )
			tf_model.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		__A    :				str    						=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
		)
		parser.add_argument(
		    """--convbert_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained ConvBERT model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__A    :				List[Any]    						=	parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






