




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 		snake_case_							(_a			:							int					,     _a			:							Optional[int]					,     _a			:							Any					,     _a			:							int					,     _a			:							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
    UpperCAmelCase     =	TapasConfig.from_json_file(__A  )
    # set absolute/relative position embeddings parameter
    UpperCAmelCase     =	reset_position_index_per_cell

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

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

        UpperCAmelCase     =	TapasForQuestionAnswering(config=__A  )
    elif task == "TABFACT":
        UpperCAmelCase     =	TapasForSequenceClassification(config=__A  )
    elif task == "MLM":
        UpperCAmelCase     =	TapasForMaskedLM(config=__A  )
    elif task == "INTERMEDIATE_PRETRAINING":
        UpperCAmelCase     =	TapasModel(config=__A  )
    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(__A					,     __A					,     __A  )

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

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

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


if __name__ == "__main__":
   A									=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.'
   )
   A									=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,
   )










'''simple docstring'''







def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							int					,     _a			:							int					,     _a			:							list[int]  ):

    # 1. Validate that path exists between current and next vertices
    if graph[path[curr_ind - 1]][next_ver] == 0:
        return False

    # 2. Validate that next vertex is not already in path
    return not any(vertex == next_ver for vertex in path  )





def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							list[int]					,     _a			:							int  ):

    # Base Case
    if curr_ind == len(_a  ):
        # return whether path exists between current and starting vertices
        return graph[path[curr_ind - 1]][path[0]] == 1

    # Recursive Step
    for next_ver in range(0					,     len(_a  )  ):
        if valid_connection(_a					,     _a					,     _a					,     _a  ):
            # Insert current vertex  into path as next transition
            UpperCAmelCase     =	next_ver
            # Validate created path
            if util_hamilton_cycle(_a					,     _a					,     curr_ind + 1  ):
                return True
            # Backtrack
            UpperCAmelCase     =	-1
    return False







def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							int = 0  ):
    UpperCAmelCase     =	[-1] * (len(_a  ) + 1)
    # initialize start and end of path with starting index
    UpperCAmelCase     =	UpperCAmelCase     =	start_index
    # evaluate and if we find answer return path either return empty array
    return path if util_hamilton_cycle(_a					,     _a					,     1  ) else []




