
"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()




def __magic_name__  (							_lowerCamelCase:		Dict,				_lowerCamelCase:		int,				_lowerCamelCase:		Tuple )				->	Optional[Any]:

	'''simple docstring'''




	lowerCAmelCase				       =     RoFormerConfig.from_json_file(_lowerCamelCase )
	print(F"""Building PyTorch model from configuration: {config}""" )
	lowerCAmelCase				       =     RoFormerForMaskedLM(_lowerCamelCase )

	# Load weights from tf checkpoint
	load_tf_weights_in_roformer(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase )

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


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(
					    """--bert_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."""
					)
					UpperCAmelCase          =							parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




"""simple docstring"""






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  lowercase				(   lowercase__		):
			lowercase			     =    (KDPMaAncestralDiscreteScheduler,)
			lowercase			     =    10






			def 				UpperCAmelCase       (self     :  Optional[int]			,**SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     {
				    '''num_train_timesteps''': 1_100,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				}

				config.update(**SCREAMING_SNAKE_CASE_ )
				return config






			def 				UpperCAmelCase       (self     :  Any )					->  str:





				"""simple docstring"""


				for timesteps in [10, 50, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  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=SCREAMING_SNAKE_CASE_			,beta_end=SCREAMING_SNAKE_CASE_ )






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





				"""simple docstring"""


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






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





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

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

				assert abs(result_sum.item() - 1_38_49.38_77 ) < 1e-2
				assert abs(result_mean.item() - 18.03_31 ) < 5e-3






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





				"""simple docstring"""


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






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





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config(prediction_type='''v_prediction''' )
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

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

				assert abs(result_sum.item() - 3_28.99_70 ) < 1e-2
				assert abs(result_mean.item() - 0.42_84 ) < 1e-3






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





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps			,device=SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_ ) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

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

				assert abs(result_sum.item() - 1_38_49.38_18 ) < 1e-1
				assert abs(result_mean.item() - 18.03_31 ) < 1e-3


