
"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_ =		10
       def 				lowercase_				(  self			,					**A_							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      str   			=  {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_							)
         return config
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=A_							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,					[0.00_02, 0.0_02, 0.02]							):
           self.check_over_configs(beta_start=A_			,					beta_end=A_							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_							)
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_							)
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      Any   			=  self.scheduler_classes[0]
         _lowercase:      Union[str, Any]   			=  self.get_scheduler_config(prediction_type='''v_prediction'''							)
         _lowercase:      Union[str, Any]   			=  scheduler_class(**A_							)

         scheduler.set_timesteps(self.num_inference_steps							)

         _lowercase:      str   			=  self.dummy_model()
         _lowercase:      Any   			=  self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:      Dict   			=  sample.to(A_							)

         for i, t in enumerate(scheduler.timesteps							):
           _lowercase:      str   			=  scheduler.scale_model_input(A_			,					A_							)

           _lowercase:      Dict   			=  model(A_			,					A_							)

           _lowercase:      Any   			=  scheduler.step(A_			,					A_			,					A_							)
           _lowercase:      Dict   			=  output.prev_sample

         _lowercase:      str   			=  torch.sum(torch.abs(A_							)							)
         _lowercase:      Dict   			=  torch.mean(torch.abs(A_							)							)

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07							) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10							) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07							) < 1E-2
           assert abs(result_mean.item() - 0.00_02							) < 1E-3
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:      Optional[Any]   			=  self.scheduler_classes[0]
         _lowercase:      str   			=  self.get_scheduler_config()
         _lowercase:      Optional[int]   			=  scheduler_class(**A_							)

         scheduler.set_timesteps(self.num_inference_steps							)

         _lowercase:      Optional[int]   			=  self.dummy_model()
         _lowercase:      Tuple   			=  self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:      List[str]   			=  sample.to(A_							)

         for i, t in enumerate(scheduler.timesteps							):
           _lowercase:      Optional[int]   			=  scheduler.scale_model_input(A_			,					A_							)

           _lowercase:      List[str]   			=  model(A_			,					A_							)

           _lowercase:      Tuple   			=  scheduler.step(A_			,					A_			,					A_							)
           _lowercase:      Any   			=  output.prev_sample

         _lowercase:      Tuple   			=  torch.sum(torch.abs(A_							)							)
         _lowercase:      Any   			=  torch.mean(torch.abs(A_							)							)

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25							) < 1E-2
           assert abs(result_mean.item() - 0.02_66							) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25							) < 1E-2
           assert abs(result_mean.item() - 0.02_66							) < 1E-3



       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:      Any   			=  self.scheduler_classes[0]
         _lowercase:      Any   			=  self.get_scheduler_config()
         _lowercase:      Dict   			=  scheduler_class(**A_							)

         scheduler.set_timesteps(self.num_inference_steps			,					device=A_							)

         _lowercase:      Tuple   			=  self.dummy_model()
         _lowercase:      Any   			=  self.dummy_sample_deter.to(A_							) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:      Dict   			=  scheduler.scale_model_input(A_			,					A_							)

           _lowercase:      Dict   			=  model(A_			,					A_							)

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

         _lowercase:      List[Any]   			=  torch.sum(torch.abs(A_							)							)
         _lowercase:      List[str]   			=  torch.mean(torch.abs(A_							)							)

         if str(A_							).startswith('''cpu'''							):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25							) < 1E-2
           assert abs(result_mean.item() - 0.02_66							) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25							) < 1E-2
           assert abs(result_mean.item() - 0.02_66							) < 1E-3


"""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
  )
