


'''simple docstring'''



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= (DDIMParallelScheduler,)
				__magic_name__       :   Optional[int]							= (("eta", 0.0), ("num_inference_steps", 50))




				def   a__(	self    :				str   ,					**lowerCAmelCase    :				Dict   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			{
										    '''num_train_timesteps''': 1000,
										    '''beta_start''': 0.0001,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''clip_sample''': True,
										}

										config.update(**lowerCAmelCase   )
										return config




				def   a__(	self    :				Dict   ,					**lowerCAmelCase    :				int   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config(**lowerCAmelCase   )
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase         =			10, 0.0

										UpperCAmelCase         =			self.dummy_model()
										UpperCAmelCase         =			self.dummy_sample_deter

										scheduler.set_timesteps(lowerCAmelCase   )

										for t in scheduler.timesteps:
																UpperCAmelCase         =			model(lowerCAmelCase   ,					lowerCAmelCase   )
																UpperCAmelCase         =			scheduler.step(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ).prev_sample

										return sample




				def   a__(	self    :				Dict   )->  Tuple:

										"""simple docstring"""




										for timesteps in [100, 500, 1000]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase   )




				def   a__(	self    :				int   )->  List[Any]:

										"""simple docstring"""




										for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=lowerCAmelCase   )

										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config(steps_offset=1   )
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )
										scheduler.set_timesteps(5   )
										assert torch.equal(scheduler.timesteps   ,					torch.LongTensor([801, 601, 401, 201, 1]   )   )




				def   a__(	self    :				Dict   )->  Tuple:

										"""simple docstring"""




										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,					[0.002, 0.02, 0.2, 2]   ):
																self.check_over_configs(beta_start=lowerCAmelCase   ,					beta_end=lowerCAmelCase   )




				def   a__(	self    :				int   )->  List[str]:

										"""simple docstring"""




										for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=lowerCAmelCase   )




				def   a__(	self    :				Any   )->  str:

										"""simple docstring"""




										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=lowerCAmelCase   )




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

										"""simple docstring"""




										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=lowerCAmelCase   )




				def   a__(	self    :				Dict   )->  Any:

										"""simple docstring"""




										for timestep_spacing in ["trailing", "leading"]:
																self.check_over_configs(timestep_spacing=lowerCAmelCase   )




				def   a__(	self    :				Optional[int]   )->  str:

										"""simple docstring"""




										for rescale_betas_zero_snr in [True, False]:
																self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase   )




				def   a__(	self    :				str   )->  int:

										"""simple docstring"""




										self.check_over_configs(thresholding=lowerCAmelCase   )
										for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=lowerCAmelCase   ,					prediction_type=lowerCAmelCase   ,					sample_max_value=lowerCAmelCase   ,					)




				def   a__(	self    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										for t in [1, 10, 49]:
																self.check_over_forward(time_step=lowerCAmelCase   )




				def   a__(	self    :				List[Any]   )->  List[Any]:

										"""simple docstring"""




										for t, num_inference_steps in zip([1, 10, 50]   ,					[10, 50, 500]   ):
																self.check_over_forward(time_step=lowerCAmelCase   ,					num_inference_steps=lowerCAmelCase   )




				def   a__(	self    :				List[Any]   )->  List[Any]:

										"""simple docstring"""




										for t, eta in zip([1, 10, 49]   ,					[0.0, 0.5, 1.0]   ):
																self.check_over_forward(time_step=lowerCAmelCase   ,					eta=lowerCAmelCase   )




				def   a__(	self    :				int   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config()
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										assert torch.sum(torch.abs(scheduler._get_variance(0   ,					0   ) - 0.0   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(420   ,					400   ) - 0.14771   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(980   ,					960   ) - 0.32460   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0   ,					0   ) - 0.0   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487   ,					486   ) - 0.00979   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999   ,					998   ) - 0.02   )   ) < 1E-5




				def   a__(	self    :				int   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.scheduler_classes[0]
										UpperCAmelCase         =			self.get_scheduler_config()
										UpperCAmelCase         =			scheduler_class(**lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase         =			10, 0.0
										scheduler.set_timesteps(lowerCAmelCase   )

										UpperCAmelCase         =			self.dummy_model()
										UpperCAmelCase         =			self.dummy_sample_deter
										UpperCAmelCase         =			self.dummy_sample_deter + 0.1
										UpperCAmelCase         =			self.dummy_sample_deter - 0.1

										UpperCAmelCase         =			samplea.shape[0]
										UpperCAmelCase         =			torch.stack([samplea, samplea, samplea]   ,					dim=0   )
										UpperCAmelCase         =			torch.arange(lowerCAmelCase   )[0:3, None].repeat(1   ,					lowerCAmelCase   )

										UpperCAmelCase         =			model(samples.flatten(0   ,					1   )   ,					timesteps.flatten(0   ,					1   )   )
										UpperCAmelCase         =			scheduler.batch_step_no_noise(lowerCAmelCase   ,					timesteps.flatten(0   ,					1   )   ,					samples.flatten(0   ,					1   )   ,					lowerCAmelCase   )

										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 1147.7904   ) < 1E-2
										assert abs(result_mean.item() - 0.4982   ) < 1E-3




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

										"""simple docstring"""




										UpperCAmelCase         =			self.full_loop()

										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 172.0067   ) < 1E-2
										assert abs(result_mean.item() - 0.223967   ) < 1E-3




				def   a__(	self    :				Optional[Any]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			self.full_loop(prediction_type='''v_prediction'''   )

										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 52.5302   ) < 1E-2
										assert abs(result_mean.item() - 0.0684   ) < 1E-3




				def   a__(	self    :				Optional[Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.full_loop(set_alpha_to_one=lowerCAmelCase   ,					beta_start=0.01   )
										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 149.8295   ) < 1E-2
										assert abs(result_mean.item() - 0.1951   ) < 1E-3





				def   a__(	self    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.full_loop(set_alpha_to_one=lowerCAmelCase   ,					beta_start=0.01   )
										UpperCAmelCase         =			torch.sum(torch.abs(lowerCAmelCase   )   )
										UpperCAmelCase         =			torch.mean(torch.abs(lowerCAmelCase   )   )

										assert abs(result_sum.item() - 149.0784   ) < 1E-2
										assert abs(result_mean.item() - 0.1941   ) < 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__   ( A     :    List[Any]   ,     A     :    int   ,     A     :    List[str]   ,     A     :    Optional[int]  ):




						'''simple docstring'''






						UpperCAmelCase         =			BigBirdConfig.from_json_file(A  )
						print(f"""Building PyTorch model from configuration: {config}"""  )

						if is_trivia_qa:
												UpperCAmelCase         =			BigBirdForQuestionAnswering(A  )
						else:
												UpperCAmelCase         =			BigBirdForPreTraining(A  )

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

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


if __name__ == "__main__":
	_lowercase    :    List[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(
	    """--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."""
	)
	_lowercase    :    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
	)





