






"""simple docstring"""



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


__A		:       Tuple  				=		{
    '''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 						lowercase    (				__snake_case				: Optional[int]		,					__snake_case				: Dict		,					__snake_case				: int		,					__snake_case				: Optional[int]=None	):
		# Initialise PyTorch model
		lowercase_ : List[Any]							       =   XLNetConfig.from_json_file(__snake_case	)

		lowercase_ : List[str]							       =   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_ : Any							       =   finetuning_task
				lowercase_ : Optional[Any]							       =   GLUE_TASKS_NUM_LABELS[finetuning_task]
				lowercase_ : Tuple							       =   XLNetForSequenceClassification(__snake_case	)
		elif "squad" in finetuning_task:
				lowercase_ : Union[str, Any]							       =   finetuning_task
				lowercase_ : Optional[int]							       =   XLNetForQuestionAnswering(__snake_case	)
		else:
				lowercase_ : Optional[Any]							       =   XLNetLMHeadModel(__snake_case	)

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

		# Save pytorch-model
		lowercase_ : Tuple							       =   os.path.join(__snake_case		,					__snake_case	)
		lowercase_ : Dict							       =   os.path.join(__snake_case		,					__snake_case	)
		print(F'''Save PyTorch model to {os.path.abspath(__snake_case	)}'''	)
		torch.save(model.state_dict()		,					__snake_case	)
		print(F'''Save configuration file to {os.path.abspath(__snake_case	)}'''	)
		with open(__snake_case		,					'''w'''		,					encoding='''utf-8'''	) as f:
				f.write(config.to_json_string()	)


if __name__ == "__main__":
						__A		:       List[Any]  				=		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''',
						)
						__A		:       List[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
						)











"""simple docstring"""



def 						lowercase    (				__snake_case				: Optional[int]	):
		lowercase_ : int							       =   0
		lowercase_ : Optional[Any]							       =   len(__snake_case	)

		for i in range(n - 1	):
				for j in range(i + 1		,					__snake_case	):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions



def 						lowercase    (				__snake_case				: str	):
		if len(__snake_case	) <= 1:
				return arr, 0
		lowercase_ : Optional[Any]							       =   len(__snake_case	) // 2
		lowercase_ : List[Any]							       =   arr[0:mid]
		lowercase_ : Union[str, Any]							       =   arr[mid:]

		lowercase_       ,					lowercase_ : Tuple							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   _count_cross_inversions(__snake_case		,					__snake_case	)

		lowercase_ : List[Any]							       =   inversion_p + inversions_q + cross_inversions
		return c, num_inversions



def 						lowercase    (				__snake_case				: str		,					__snake_case				: Optional[int]	):
		lowercase_ : Optional[Any]							       =   []
		lowercase_ : Any							       =   0
		while i < len(__snake_case	) and j < len(__snake_case	):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(__snake_case	) - i
						r.append(q[j]	)
						j += 1
				else:
						r.append(p[i]	)
						i += 1

		if i < len(__snake_case	):
				r.extend(p[i:]	)
		else:
				r.extend(q[j:]	)

		return r, num_inversion



def 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   [1_0, 2, 1, 5, 5, 2, 1_1]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		lowercase_ : int							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = '''		,					__snake_case	)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		lowercase_ : Dict							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)

		# an empty list should also have zero inversions
		lowercase_ : List[Any]							       =   []
		lowercase_ : Any							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : List[str]							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)


if __name__ == "__main__":
						main()



