



'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> Union[str, Any]:
		_a	: Optional[Any]								=				0
		_a	: List[Any]								=				len(lowerCAmelCase_    ) - 1

		while left <= right:
				# avoid divided by 0 during interpolation
				if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
								return left
						else:
								return None

				_a	: List[Any]								=				left + ((item - sorted_collection[left]) * (right - left)) // (
				    sorted_collection[right] - sorted_collection[left]
				)

				# out of range check
				if point < 0 or point >= len(lowerCAmelCase_    ):
						return None

				_a	: List[str]								=				sorted_collection[point]
				if current_item == item:
						return point
				else:
						if point < left:
								_a	: List[str]								=				left
								_a	: List[str]								=				point
						elif point > right:
								_a	: Union[str, Any]								=				right
								_a	: Tuple								=				point
						else:
								if item < current_item:
										_a	: Optional[Any]								=				point - 1
								else:
										_a	: Optional[Any]								=				point + 1
		return None



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Optional[int]:

		# avoid divided by 0 during interpolation
		if sorted_collection[left] == sorted_collection[right]:
				if sorted_collection[left] == item:
						return left
				else:
						return None

		_a	: Optional[int]								=				left + ((item - sorted_collection[left]) * (right - left)) // (
		    sorted_collection[right] - sorted_collection[left]
		)

		# out of range check
		if point < 0 or point >= len(lowerCAmelCase_    ):
				return None

		if sorted_collection[point] == item:
				return point
		elif point < left:
				return interpolation_search_by_recursion(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
		elif point > right:
				return interpolation_search_by_recursion(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
		else:
				if sorted_collection[point] > item:
						return interpolation_search_by_recursion(
						    lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     point - 1    )
				else:
						return interpolation_search_by_recursion(
						    lowerCAmelCase_		,     lowerCAmelCase_		,     point + 1		,     lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> int:
		if collection != sorted(lowerCAmelCase_    ):
				raise ValueError('Collection must be ascending sorted'    )
		return True


if __name__ == "__main__":
							import sys
							__lowerCAmelCase		     =  0
							if debug == 1:
														__lowerCAmelCase		     =  [10, 30, 40, 45, 50, 66, 77, 93]
														try:
																					__assert_sorted(collection)
														except ValueError:
																					sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
														__lowerCAmelCase		     =  67

							__lowerCAmelCase		     =  interpolation_search(collection, target)
							if result is not None:
														print(f"""{target} found at positions: {result}""")
							else:
														print('''Not found''')









'''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	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		# Initialise PyTorch model
		_a	: int								=				BigBirdConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )

		if is_trivia_qa:
				_a	: Union[str, Any]								=				BigBirdForQuestionAnswering(lowerCAmelCase_    )
		else:
				_a	: Any								=				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		     =  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
							)




