




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase__		)
class 			_UpperCamelCase      (				UpperCamelCase__		):
						UpperCAmelCase_        =		field(default="""image-classification"""	,       metadata={"""include_in_asdict_even_if_is_default""": True}		)
						UpperCAmelCase_        =		Features({"""image""": Image()}		)
						UpperCAmelCase_        =		Features({"""labels""": ClassLabel}		)
						UpperCAmelCase_        =		"""image"""
						UpperCAmelCase_        =		"""labels"""
						def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Tuple      )     ->			Tuple:



							if self.label_column not in features:
								raise ValueError(f'''Column {self.label_column} is not present in features.'''      )
							if not isinstance(features[self.label_column] ,      __A      ):
								raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''      )
							UpperCAmelCase__							 =						copy.deepcopy(self      )
							UpperCAmelCase__							 =						self.label_schema.copy()
							UpperCAmelCase__							 =						features[self.label_column]
							UpperCAmelCase__							 =						label_schema
							return task_template







						@property
						def 	UpperCAmelCase_						(			self   :Dict      )     ->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}











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		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						BigBirdConfig.from_json_file(_lowerCAmelCase	)
 print(F'''Building PyTorch model from configuration: {config}'''	)

 if is_trivia_qa:
  UpperCAmelCase__							 =						BigBirdForQuestionAnswering(_lowerCAmelCase	)
 else:
  UpperCAmelCase__							 =						BigBirdForPreTraining(_lowerCAmelCase	)

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

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
 model.save_pretrained(_lowerCAmelCase	)


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						:   Dict		=   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
  )





