

"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def        _lowerCamelCase ( __a,							__a=False    ):
		SCREAMING_SNAKE_CASE_						 = OmegaConf.load(__a    )
		if display:
				print(yaml.dump(OmegaConf.to_container(__a    )    )    )
		return config



def        _lowerCamelCase ( __a,							__a=None,							__a=None    ):
		if conf_path is None:
				SCREAMING_SNAKE_CASE_						 = '''./model_checkpoints/vqgan_only.yaml'''
		SCREAMING_SNAKE_CASE_						 = load_config(__a,							display=__a    )
		SCREAMING_SNAKE_CASE_						 = VQModel(**config.model.params    )
		if ckpt_path is None:
				SCREAMING_SNAKE_CASE_						 = '''./model_checkpoints/vqgan_only.pt'''
		SCREAMING_SNAKE_CASE_						 = torch.load(__a,							map_location=__a    )
		if ".ckpt" in ckpt_path:
				SCREAMING_SNAKE_CASE_						 = sd['''state_dict''']
		model.load_state_dict(__a,							strict=__a    )
		model.to(__a    )
		del sd
		return model



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = model.encode(__a    )
		print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'    )
		SCREAMING_SNAKE_CASE_						 = model.decode(__a    )
		return xrec



def        _lowerCamelCase ( __a,							__a=False    ):
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = string.rsplit('''.''',							1    )
		if reload:
				SCREAMING_SNAKE_CASE_						 = importlib.import_module(__a    )
				importlib.reload(__a    )
		return getattr(importlib.import_module(__a,							package=__a    ),							cls    )



def        _lowerCamelCase ( __a    ):
		if "target" not in config:
				raise KeyError('''Expected key `target` to instantiate.'''    )
		return get_obj_from_str(config['''target''']    )(**config.get('''params''',							{}    )    )



def        _lowerCamelCase ( __a,							__a,							__a=True,							__a=True    ):
		SCREAMING_SNAKE_CASE_						 = instantiate_from_config(__a    )
		if sd is not None:
				model.load_state_dict(__a    )
		if gpu:
				model.cuda()
		if eval_mode:
				model.eval()
		return {"model": model}



def        _lowerCamelCase ( __a,							__a,							__a,							__a    ):
		# load the specified checkpoint
		if ckpt:
				SCREAMING_SNAKE_CASE_						 = torch.load(__a,							map_location='''cpu'''    )
				SCREAMING_SNAKE_CASE_						 = pl_sd['''global_step''']
				print(F'loaded model from global step {global_step}.'    )
		else:
				SCREAMING_SNAKE_CASE_						 = {'''state_dict''': None}
				SCREAMING_SNAKE_CASE_						 = None
		SCREAMING_SNAKE_CASE_						 = load_model_from_config(config.model,							pl_sd['''state_dict'''],							gpu=__a,							eval_mode=__a    )['''model''']
		return model, global_step

"""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
						)