


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__:		Dict      ,  a__:		Union[str, Any]      ,  a__:		Any      ,  a__:		List[str]	)  ->		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__":
		lowerCAmelCase__  :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.'''
		)
		lowerCAmelCase__  :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
		)




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available

@dataclass
class 		__a				(    UpperCAmelCase      ):
		_a     :  Union[List[np.ndarray], torch.FloatTensor]


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .pipeline_text_to_video_synth import TextToVideoSDPipeline
		from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
		from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
