


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


lowerCAmelCase__			:      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 						a__			(				A__,      A__,      A__,      A__=None				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					XLNetConfig.from_json_file(_UpperCAmelCase				)

			SCREAMING_SNAKE_CASE_		:     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}'''				)
						SCREAMING_SNAKE_CASE_		:     int					=					finetuning_task
						SCREAMING_SNAKE_CASE_		:     int					=					GLUE_TASKS_NUM_LABELS[finetuning_task]
						SCREAMING_SNAKE_CASE_		:     Dict					=					XLNetForSequenceClassification(_UpperCAmelCase				)
			elif "squad" in finetuning_task:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					finetuning_task
						SCREAMING_SNAKE_CASE_		:     Any					=					XLNetForQuestionAnswering(_UpperCAmelCase				)
			else:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					XLNetLMHeadModel(_UpperCAmelCase				)

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

			# Save pytorch-model
			SCREAMING_SNAKE_CASE_		:     List[str]					=					os.path.join(_UpperCAmelCase,      _UpperCAmelCase				)
			SCREAMING_SNAKE_CASE_		:     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__":
				lowerCAmelCase__			:      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',
				)
				lowerCAmelCase__			:      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
				)







def 		UpperCamelCase     (       _UpperCAmelCase  :   list[int] ,			_UpperCAmelCase  :   list[int]  )   ->			tuple[float, float]:






						'''simple docstring'''

						if not len(_UpperCAmelCase  ) == len(_UpperCAmelCase  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_lowercase      ,    _lowercase      ,    _lowercase       :	List[str]           =   equationa
						_lowercase      ,    _lowercase      ,    _lowercase       :	List[str]           =   equationa

						# Calculate the determinants of the matrices
						_lowercase       :	List[Any]           =   aa * ba - aa * ba
						_lowercase       :	Tuple           =   ca * ba - ca * ba
						_lowercase       :	Union[str, Any]           =   aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_lowercase       :	str           =   determinant_x / determinant
																		_lowercase       :	Union[str, Any]           =   determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)




