import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							str      , UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							List[str]  ):
					# Initialise PyTorch model.
					# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
					# TapasConfig to False.

					# initialize configuration from json file
					SCREAMING_SNAKE_CASE    			=       TapasConfig.from_json_file(UpperCAmelCase__  )
					# set absolute/relative position embeddings parameter
					SCREAMING_SNAKE_CASE    			=       reset_position_index_per_cell

					# set remaining parameters of TapasConfig as well as the model based on the task
					if task == "SQA":
										SCREAMING_SNAKE_CASE    			=       TapasForQuestionAnswering(config=UpperCAmelCase__  )
					elif task == "WTQ":
										# run_task_main.py hparams
										SCREAMING_SNAKE_CASE    			=       4
										SCREAMING_SNAKE_CASE    			=       True
										# hparam_utils.py hparams
										SCREAMING_SNAKE_CASE    			=       0.66_4694
										SCREAMING_SNAKE_CASE    			=       0.20_7951
										SCREAMING_SNAKE_CASE    			=       0.12_1194
										SCREAMING_SNAKE_CASE    			=       True
										SCREAMING_SNAKE_CASE    			=       True
										SCREAMING_SNAKE_CASE    			=       False
										SCREAMING_SNAKE_CASE    			=       0.035_2513

										SCREAMING_SNAKE_CASE    			=       TapasForQuestionAnswering(config=UpperCAmelCase__  )
					elif task == "WIKISQL_SUPERVISED":
										# run_task_main.py hparams
										SCREAMING_SNAKE_CASE    			=       4
										SCREAMING_SNAKE_CASE    			=       False
										# hparam_utils.py hparams
										SCREAMING_SNAKE_CASE    			=       36.4519
										SCREAMING_SNAKE_CASE    			=       0.90_3421
										SCREAMING_SNAKE_CASE    			=       222.088
										SCREAMING_SNAKE_CASE    			=       True
										SCREAMING_SNAKE_CASE    			=       True
										SCREAMING_SNAKE_CASE    			=       True
										SCREAMING_SNAKE_CASE    			=       0.76_3141

										SCREAMING_SNAKE_CASE    			=       TapasForQuestionAnswering(config=UpperCAmelCase__  )
					elif task == "TABFACT":
										SCREAMING_SNAKE_CASE    			=       TapasForSequenceClassification(config=UpperCAmelCase__  )
					elif task == "MLM":
										SCREAMING_SNAKE_CASE    			=       TapasForMaskedLM(config=UpperCAmelCase__  )
					elif task == "INTERMEDIATE_PRETRAINING":
										SCREAMING_SNAKE_CASE    			=       TapasModel(config=UpperCAmelCase__  )
					else:
										raise ValueError(F"Task {task} not supported."  )

					print(F"Building PyTorch model from configuration: {config}"  )
					# Load weights from tf checkpoint
					load_tf_weights_in_tapas(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )

					# Save pytorch-model (weights and configuration)
					print(F"Save PyTorch model to {pytorch_dump_path}"  )
					model.save_pretrained(UpperCAmelCase__  )

					# Save tokenizer files
					print(F"Save tokenizer files to {pytorch_dump_path}"  )
					SCREAMING_SNAKE_CASE    			=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + "vocab.txt"      , model_max_length=5_1_2  )
					tokenizer.save_pretrained(UpperCAmelCase__  )

					print("Used relative position embeddings:"      , model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
			_lowerCamelCase					:   Dict							=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
			)
			parser.add_argument(
			    '''--reset_position_index_per_cell''',
			    default=False,
			    action='''store_true''',
			    help='''Whether to use relative position embeddings or not. Defaults to True.''',
			)
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--tapas_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained TAPAS 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.'''
			)
			_lowerCamelCase					:   Union[str, Any]							=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.task,
			    args.reset_position_index_per_cell,
			    args.tf_checkpoint_path,
			    args.tapas_config_file,
			    args.pytorch_dump_path,
			)





import functools





def 	__lowerCamelCase							(UpperCAmelCase__     :							list[int]      , UpperCAmelCase__     :							list[int]  ):

					# Validation
					if not isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for day in days  ):
										raise ValueError("The parameter days should be a list of integers"  )

					if len(UpperCAmelCase__  ) != 3 or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for cost in costs  ):
										raise ValueError("The parameter costs should be a list of three integers"  )

					if len(UpperCAmelCase__  ) == 0:
										return 0

					if min(UpperCAmelCase__  ) <= 0:
										raise ValueError("All days elements should be greater than 0"  )

					if max(UpperCAmelCase__  ) >= 3_6_6:
										raise ValueError("All days elements should be less than 366"  )

					SCREAMING_SNAKE_CASE    			=       set(UpperCAmelCase__  )

					@functools.cache
					def dynamic_programming(UpperCAmelCase__     :							int  ) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1  )

										return min(
										    costs[0] + dynamic_programming(index + 1  )      , costs[1] + dynamic_programming(index + 7  )      , costs[2] + dynamic_programming(index + 3_0  )      , )

					return dynamic_programming(1  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




