



import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()

def       A_   (						__a  :					Union[str, Any]			,		__a  :					Tuple			,		__a  :					Any			,		__a  :					Optional[Any]		):

				"""simple docstring"""
				# Initialise PyTorch model
				a__					   =		ElectraConfig.from_json_file(__a		)
				print(F'''Building PyTorch model from configuration: {config}'''		)

				if discriminator_or_generator == "discriminator":
								a__					   =		ElectraForPreTraining(__a		)
				elif discriminator_or_generator == "generator":
								a__					   =		ElectraForMaskedLM(__a		)
				else:
								raise ValueError("""The discriminator_or_generator argument should be either 'discriminator' or 'generator'"""		)

				# Load weights from tf checkpoint
				load_tf_weights_in_electra(
				    __a			,		__a			,		__a			,		discriminator_or_generator=__a		)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
				torch.save(model.state_dict()			,		__a		)


if __name__ == "__main__":
	UpperCAmelCase							=       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(
	    """--config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--discriminator_or_generator""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or """
	        """'generator'."""
	    ),
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
	)





'''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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					RePaintPipeline
     UpperCAmelCase			       =					IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {
         '''latents''',
         '''num_images_per_prompt''',
         '''callback''',
         '''callback_steps''',
     }
     UpperCAmelCase			       =					IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase			       =					False






     def 			a_       (      self		)							->     int:
          torch.manual_seed(0		)
          torch.manual_seed(0		)
          _a            =		UNetaDModel(
              block_out_channels=(32, 64)  ,				layers_per_block=2  ,				sample_size=32  ,				in_channels=3  ,				out_channels=3  ,				down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,				up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,				)
          _a            =		RePaintScheduler()
          _a            =		{"unet": unet, "scheduler": scheduler}
          return components






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Tuple:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
          _a            =		np.random.RandomState(__UpperCamelCase		).standard_normal((1, 3, 32, 32)		)
          _a            =		torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		(image > 0).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		{
              "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 			a_       (      self		)							->     Optional[Any]:
          _a            =		"cpu"  # ensure determinism for the device-dependent torch.Generator
          _a            =		self.get_dummy_components()
          _a            =		RePaintPipeline(**__UpperCamelCase		)
          _a            =		sd_pipe.to(__UpperCamelCase		)
          sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

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

          assert image.shape == (1, 32, 32, 3)
          _a            =		np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]		)

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






     @skip_mps
     def 			a_       (      self		)							->     Union[str, Any]:
          return super().test_save_load_local()






     @unittest.skip("non-deterministic pipeline"		)
     def 			a_       (      self		)							->     int:
          return super().test_inference_batch_single_identical()






     @skip_mps
     def 			a_       (      self		)							->     str:
          return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def 			a_       (      self		)							->     int:
          return super().test_save_load_optional_components()






     @skip_mps
     def 			a_       (      self		)							->     Optional[int]:
          return super().test_attention_slicing_forward_pass()







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






     def 			a_       (      self		)							->     List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Union[str, Any]:
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256.png"		)
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"		)
          _a            =		load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256_result.npy"		)

          _a            =		"google/ddpm-ema-celebahq-256"
          _a            =		UNetaDModel.from_pretrained(__UpperCamelCase		)
          _a            =		RePaintScheduler.from_pretrained(__UpperCamelCase		)

          _a            =		RePaintPipeline(unet=__UpperCamelCase  ,				scheduler=__UpperCamelCase		).to(__UpperCamelCase		)
          repaint.set_progress_bar_config(disable=__UpperCamelCase		)
          repaint.enable_attention_slicing()

          _a            =		torch.manual_seed(0		)
          _a            =		repaint(
              __UpperCamelCase  ,				__UpperCamelCase  ,				num_inference_steps=250  ,				eta=0.0  ,				jump_length=10  ,				jump_n_sample=10  ,				generator=__UpperCamelCase  ,				output_type="np"  ,				)
          _a            =		output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image		).mean() < 1e-2

