





import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__A      		=     False



class   lowercase_   (  unittest.TestCase					):



      def 						UpperCamelCase_		(      self				:    int      , A__				:    str=32		)   ->					str:
        set_seed(0		)
        _snake_case						=							UNetaDModel(sample_size=A__      , in_channels=3      , out_channels=3		)
        _snake_case						=							torch.optim.SGD(model.parameters()      , lr=0.0001		)
        return model, optimizer







      @slow
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        _snake_case						=							"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
        _snake_case						=							DDPMScheduler(
            num_train_timesteps=1000      , beta_start=0.0001      , beta_end=0.02      , beta_schedule='''linear'''      , clip_sample=A__      , )
        _snake_case						=							DDIMScheduler(
            num_train_timesteps=1000      , beta_start=0.0001      , beta_end=0.02      , beta_schedule='''linear'''      , clip_sample=A__      , )

        assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

        # shared batches for DDPM and DDIM
        set_seed(0		)
        _snake_case						=							[torch.randn((4, 3, 32, 32)		).clip(-1      , 1		).to(A__		) for _ in range(4		)]
        _snake_case						=							[torch.randn((4, 3, 32, 32)		).to(A__		) for _ in range(4		)]
        _snake_case						=							[torch.randint(0      , 1000      , (4,)		).long().to(A__		) for _ in range(4		)]

        # train with a DDPM scheduler
        _snake_case						=							self.get_model_optimizer(resolution=32		)
        model.train().to(A__		)
        for i in range(4		):
          optimizer.zero_grad()
          _snake_case						=							ddpm_scheduler.add_noise(clean_images[i]      , noise[i]      , timesteps[i]		)
          _snake_case						=							model(A__      , timesteps[i]		).sample
          _snake_case						=							torch.nn.functional.mse_loss(A__      , noise[i]		)
          loss.backward()
          optimizer.step()
        del model, optimizer

        # recreate the model and optimizer, and retry with DDIM
        _snake_case						=							self.get_model_optimizer(resolution=32		)
        model.train().to(A__		)
        for i in range(4		):
          optimizer.zero_grad()
          _snake_case						=							ddim_scheduler.add_noise(clean_images[i]      , noise[i]      , timesteps[i]		)
          _snake_case						=							model(A__      , timesteps[i]		).sample
          _snake_case						=							torch.nn.functional.mse_loss(A__      , noise[i]		)
          loss.backward()
          optimizer.step()
        del model, optimizer

        self.assertTrue(torch.allclose(A__      , A__      , atol=1e-5		)		)
        self.assertTrue(torch.allclose(A__      , A__      , atol=1e-5		)		)




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							AlbertConfig.from_json_file(_UpperCamelCase					)
  print(F"""Building PyTorch model from configuration: {config}"""					)
  _snake_case						=							AlbertForPreTraining(_UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)

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


if __name__ == "__main__":
 __A      		=     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(
     '''--albert_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained ALBERT 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      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)

