
"""simple docstring"""




from __future__ import annotations

from typing import TypedDict





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42







def        _lowerCamelCase ( __a    ):
		if not isinstance(__a,							__a    ):
				raise TypeError('''The parameter s type must be str.'''    )

		return [s[i:] + s[:i] for i in range(len(__a    )    )]



def        _lowerCamelCase ( __a    ):
		if not isinstance(__a,							__a    ):
				raise TypeError('''The parameter s type must be str.'''    )
		if not s:
				raise ValueError('''The parameter s must not be empty.'''    )

		SCREAMING_SNAKE_CASE_						 = all_rotations(__a    )
		rotations.sort()  # sort the list of rotations in alphabetically order
		# make a string composed of the last char of each rotation
		SCREAMING_SNAKE_CASE_						 = {
		    "bwt_string": "".join([word[-1] for word in rotations]    ),
		    "idx_original_string": rotations.index(__a    ),
		}
		return response



def        _lowerCamelCase ( __a,							__a    ):
		if not isinstance(__a,							__a    ):
				raise TypeError('''The parameter bwt_string type must be str.'''    )
		if not bwt_string:
				raise ValueError('''The parameter bwt_string must not be empty.'''    )
		try:
				SCREAMING_SNAKE_CASE_						 = int(__a    )
		except ValueError:
				raise TypeError(
				    '''The parameter idx_original_string type must be int or passive'''
				    ''' of cast to int.'''    )
		if idx_original_string < 0:
				raise ValueError('''The parameter idx_original_string must not be lower than 0.'''    )
		if idx_original_string >= len(__a    ):
				raise ValueError(
				    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''    )

		SCREAMING_SNAKE_CASE_						 = [''''''] * len(__a    )
		for _ in range(len(__a    )    ):
				for i in range(len(__a    )    ):
						SCREAMING_SNAKE_CASE_						 = bwt_string[i] + ordered_rotations[i]
				ordered_rotations.sort()
		return ordered_rotations[idx_original_string]


if __name__ == "__main__":
						lowerCAmelCase__							=				'Provide a string that I will generate its BWT transform: '
						lowerCAmelCase__							=				input(entry_msg).strip()
						lowerCAmelCase__							=				bwt_transform(s)
						print(
						    f'''Burrows Wheeler transform for string \'{s}\' results '''
						    f'''in \'{result['bwt_string']}\''''
						)
						lowerCAmelCase__							=				reverse_bwt(result['bwt_string'], result['idx_original_string'])
						print(
						    f'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
						    f'''we get original string \'{original_string}\''''
						)

"""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
						)