
"""simple docstring"""




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		(IPNDMScheduler,)
       UpperCamelCase_ =		(('''num_inference_steps''', 50),)
       def 				lowercase_				(  self			,					**A_							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Dict   			=  {'''num_train_timesteps''': 1000}
         config.update(**A_							)
         return config
       def 				lowercase_				(  self			,					A_=0			,					**A_							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  dict(self.forward_default_kwargs							)
         _lowercase:      Tuple   			=  kwargs.pop('''num_inference_steps'''			,					A_							)
         _lowercase:      List[Any]   			=  self.dummy_sample
         _lowercase:      List[str]   			=  0.1 * sample
         _lowercase:      int   			=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           _lowercase:      Union[str, Any]   			=  self.get_scheduler_config(**A_							)
           _lowercase:      List[str]   			=  scheduler_class(**A_							)
           scheduler.set_timesteps(A_							)
           # copy over dummy past residuals
           _lowercase:      Optional[int]   			=  dummy_past_residuals[:]

           if time_step is None:
             _lowercase:      List[str]   			=  scheduler.timesteps[len(scheduler.timesteps							) // 2]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(A_							)
             _lowercase:      Optional[Any]   			=  scheduler_class.from_pretrained(A_							)
             new_scheduler.set_timesteps(A_							)
             # copy over dummy past residuals
             _lowercase:      List[str]   			=  dummy_past_residuals[:]

           _lowercase:      str   			=  scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      Any   			=  new_scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

           _lowercase:      List[str]   			=  scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      Union[str, Any]   			=  new_scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         pass
       def 				lowercase_				(  self			,					A_=0			,					**A_							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Tuple   			=  dict(self.forward_default_kwargs							)
         _lowercase:      List[str]   			=  kwargs.pop('''num_inference_steps'''			,					A_							)
         _lowercase:      List[Any]   			=  self.dummy_sample
         _lowercase:      List[Any]   			=  0.1 * sample
         _lowercase:      Dict   			=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           _lowercase:      Any   			=  self.get_scheduler_config()
           _lowercase:      Any   			=  scheduler_class(**A_							)
           scheduler.set_timesteps(A_							)

           # copy over dummy past residuals (must be after setting timesteps)
           _lowercase:      Any   			=  dummy_past_residuals[:]

           if time_step is None:
             _lowercase:      List[str]   			=  scheduler.timesteps[len(scheduler.timesteps							) // 2]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(A_							)
             _lowercase:      Any   			=  scheduler_class.from_pretrained(A_							)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(A_							)

             # copy over dummy past residual (must be after setting timesteps)
             _lowercase:      int   			=  dummy_past_residuals[:]

           _lowercase:      str   			=  scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      int   			=  new_scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

           _lowercase:      Union[str, Any]   			=  scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      Union[str, Any]   			=  new_scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
       def 				lowercase_				(  self			,					**A_							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.scheduler_classes[0]
         _lowercase:      int   			=  self.get_scheduler_config(**A_							)
         _lowercase:      Tuple   			=  scheduler_class(**A_							)

         _lowercase:      Union[str, Any]   			=  10
         _lowercase:      Dict   			=  self.dummy_model()
         _lowercase:      str   			=  self.dummy_sample_deter
         scheduler.set_timesteps(A_							)

         for i, t in enumerate(scheduler.timesteps							):
           _lowercase:      Tuple   			=  model(A_			,					A_							)
           _lowercase:      str   			=  scheduler.step(A_			,					A_			,					A_							).prev_sample

         for i, t in enumerate(scheduler.timesteps							):
           _lowercase:      Tuple   			=  model(A_			,					A_							)
           _lowercase:      Union[str, Any]   			=  scheduler.step(A_			,					A_			,					A_							).prev_sample

         return sample
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Dict   			=  dict(self.forward_default_kwargs							)

         _lowercase:      str   			=  kwargs.pop('''num_inference_steps'''			,					A_							)

         for scheduler_class in self.scheduler_classes:
           _lowercase:      Optional[int]   			=  self.get_scheduler_config()
           _lowercase:      Optional[int]   			=  scheduler_class(**A_							)

           _lowercase:      int   			=  self.dummy_sample
           _lowercase:      Optional[Any]   			=  0.1 * sample

           if num_inference_steps is not None and hasattr(A_			,					'''set_timesteps'''							):
             scheduler.set_timesteps(A_							)
           elif num_inference_steps is not None and not hasattr(A_			,					'''set_timesteps'''							):
             _lowercase:      List[str]   			=  num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           _lowercase:      Optional[int]   			=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
           _lowercase:      List[str]   			=  dummy_past_residuals[:]

           _lowercase:      Tuple   			=  scheduler.timesteps[5]
           _lowercase:      Tuple   			=  scheduler.timesteps[6]

           _lowercase:      Union[str, Any]   			=  scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      Tuple   			=  scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample

           self.assertEqual(output_a.shape			,					sample.shape							)
           self.assertEqual(output_a.shape			,					output_a.shape							)

           _lowercase:      int   			=  scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample
           _lowercase:      str   			=  scheduler.step(A_			,					A_			,					A_			,					**A_							).prev_sample

           self.assertEqual(output_a.shape			,					sample.shape							)
           self.assertEqual(output_a.shape			,					output_a.shape							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         for timesteps in [100, 1000]:
           self.check_over_configs(num_train_timesteps=A_			,					time_step=A_							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         for t, num_inference_steps in zip([1, 5, 10]			,					[10, 50, 100]							):
           self.check_over_forward(num_inference_steps=A_			,					time_step=A_							)



       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      str   			=  self.full_loop()
         _lowercase:      Optional[int]   			=  torch.mean(torch.abs(A_							)							)

         assert abs(result_mean.item() - 254_0529							) < 10


"""simple docstring"""




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  BigBirdConfig.from_json_file(_UpperCamelCase    )
  print(f'''Building PyTorch model from configuration: {config}'''    )

  if is_trivia_qa:
    _lowercase:      Union[str, Any]   			=  BigBirdForQuestionAnswering(_UpperCamelCase    )
  else:
    _lowercase:      Dict   			=  BigBirdForPreTraining(_UpperCamelCase    )

  # Load weights from tf checkpoint
  load_tf_weights_in_big_bird(_UpperCamelCase			,					_UpperCamelCase			,					is_trivia_qa=_UpperCamelCase    )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(_UpperCamelCase    )


if __name__ == "__main__":
  A__ :       List[Any]				= 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(
      '--big_bird_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT 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.'
  )
  parser.add_argument(
      '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
  )
  A__ :       Optional[Any]				= parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
  )
