

"""simple docstring"""




import torch

from diffusers import DiffusionPipeline





class  snake_case     (							__snake_case   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__()

									self.register_modules(unet=_lowercase       ,       scheduler=_lowercase    )







							def __call__(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = torch.randn(
									    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,       )
									SCREAMING_SNAKE_CASE_						 = 1

									SCREAMING_SNAKE_CASE_						 = self.unet(_lowercase       ,       _lowercase    ).sample
									SCREAMING_SNAKE_CASE_						 = self.scheduler.step(_lowercase       ,       _lowercase       ,       _lowercase    ).prev_sample

									SCREAMING_SNAKE_CASE_						 = scheduler_output - scheduler_output + torch.ones_like(_lowercase    )

									return result

"""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,							__a,							__a,							__a    ):
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE_						 = BigBirdConfig.from_json_file(__a    )
		print(F'Building PyTorch model from configuration: {config}'    )

		if is_trivia_qa:
				SCREAMING_SNAKE_CASE_						 = BigBirdForQuestionAnswering(__a    )
		else:
				SCREAMING_SNAKE_CASE_						 = 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__":
						lowerCAmelCase__							=				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.'
						)
						lowerCAmelCase__							=				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
						)