
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


UpperCamelCase_						:     str	       =							{
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()



def 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   Tuple ,			_UpperCAmelCase  :   List[str]=None  )   ->			Optional[int]:






						'''simple docstring'''

						_lowercase       :	Optional[int]           =   XLNetConfig.from_json_file(_UpperCAmelCase  )

						_lowercase       :	Dict           =   finetuning_task.lower() if finetuning_task is not None else ""
						if finetuning_task in GLUE_TASKS_NUM_LABELS:
												print(f"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""  )
												_lowercase       :	int           =   finetuning_task
												_lowercase       :	int           =   GLUE_TASKS_NUM_LABELS[finetuning_task]
												_lowercase       :	Dict           =   XLNetForSequenceClassification(_UpperCAmelCase  )
						elif "squad" in finetuning_task:
												_lowercase       :	Tuple           =   finetuning_task
												_lowercase       :	Any           =   XLNetForQuestionAnswering(_UpperCAmelCase  )
						else:
												_lowercase       :	Tuple           =   XLNetLMHeadModel(_UpperCAmelCase  )

						# Load weights from tf checkpoint
						load_tf_weights_in_xlnet(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

						# Save pytorch-model
						_lowercase       :	List[str]           =   os.path.join(_UpperCAmelCase ,			_UpperCAmelCase  )
						_lowercase       :	Optional[int]           =   os.path.join(_UpperCAmelCase ,			_UpperCAmelCase  )
						print(f"""Save PyTorch model to {os.path.abspath(_UpperCAmelCase  )}"""  )
						torch.save(model.state_dict() ,			_UpperCAmelCase  )
						print(f"""Save configuration file to {os.path.abspath(_UpperCAmelCase  )}"""  )
						with open(_UpperCAmelCase ,			"w" ,			encoding="utf-8"  ) as f:
												f.write(config.to_json_string()  )


if __name__ == "__main__":
							UpperCamelCase_						:     int	       =							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(
							    """--xlnet_config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained XLNet model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
							)
							parser.add_argument(
							    """--finetuning_task""",
							    default=None,
							    type=str,
							    help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
							)
							UpperCamelCase_						:     Any	       =							parser.parse_args()
							print(args)

							convert_xlnet_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
							)






from __future__ import annotations



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str] | None = None  )   ->			list[list[str]]:






						'''simple docstring'''

						_lowercase       :	Dict           =   word_bank or []
						# create a table
						_lowercase       :	int           =   len(_UpperCAmelCase  ) + 1

						_lowercase       :	list[list[list[str]]]           =   []
						for _ in range(_UpperCAmelCase  ):
												table.append([]  )
						# seed value
						_lowercase       :	Optional[int]           =   [[]]  # because empty string has empty combination

						# iterate through the indices
						for i in range(_UpperCAmelCase  ):
												# condition
												if table[i] != []:
																		for word in word_bank:
																								# slice condition
																								if target[i : i + len(_UpperCAmelCase  )] == word:
																														_lowercase       :	list[list[str]]           =   [
																														    [word, *way] for way in table[i]
																														]
																														# adds the word to every combination the current position holds
																														# now,push that combination to the table[i+len(word)]
																														table[i + len(_UpperCAmelCase  )] += new_combinations

    # combinations are in reverse order so reverse for better output
						for combination in table[len(_UpperCAmelCase  )]:
												combination.reverse()

						return table[len(_UpperCAmelCase  )]


if __name__ == "__main__":
							print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
							print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
							print(
							    all_construct(
							        """hexagonosaurus""",
							        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
							    )
							)




