


'''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


__snake_case											=					{
    '''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       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			XLNetConfig.from_json_file(__a       )

					UpperCamelCase__       :int							 =			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}'''       )
										UpperCamelCase__       :Dict							 =			finetuning_task
										UpperCamelCase__       :str							 =			GLUE_TASKS_NUM_LABELS[finetuning_task]
										UpperCamelCase__       :Optional[Any]							 =			XLNetForSequenceClassification(__a       )
					elif "squad" in finetuning_task:
										UpperCamelCase__       :List[str]							 =			finetuning_task
										UpperCamelCase__       :Optional[Any]							 =			XLNetForQuestionAnswering(__a       )
					else:
										UpperCamelCase__       :str							 =			XLNetLMHeadModel(__a       )

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

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


if __name__ == "__main__":
		__snake_case											=					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''',
		)
		__snake_case											=					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'''
from collections import Counter
from timeit import timeit


def       a				(      __a = ""			, )  ->     bool:



					'''simple docstring'''






					return sum(c % 2 for c in Counter(input_str.replace(''' '''			, ''''''       ).lower()       ).values()       ) < 2


def       a				(      __a = ""       )  ->     bool:



					'''simple docstring'''






					if len(__a       ) == 0:
										return True
					UpperCamelCase__       :List[Any]							 =			input_str.replace(''' '''			, ''''''       ).lower()
					# character_freq_dict: Stores the frequency of every character in the input string
					UpperCamelCase__       :dict[str, int]							 =			{}

					for character in lower_case_input_str:
										UpperCamelCase__       :Optional[int]							 =			character_freq_dict.get(__a			, 0       ) + 1
					UpperCamelCase__       :List[str]							 =			0

					for character_count in character_freq_dict.values():
										if character_count % 2:
															odd_char += 1
					if odd_char > 1:
										return False
					return True


def       a				(      __a = ""       )  ->     None:



					'''simple docstring'''






					print('''\nFor string = '''			, __a			, ''':'''       )
					print(
					    '''> can_string_be_rearranged_as_palindrome_counter()'''			, '''\tans ='''			, can_string_be_rearranged_as_palindrome_counter(__a       )			, '''\ttime ='''			, timeit(
					        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''			, setup='''import __main__ as z'''			, )			, '''seconds'''			, )
					print(
					    '''> can_string_be_rearranged_as_palindrome()'''			, '''\tans ='''			, can_string_be_rearranged_as_palindrome(__a       )			, '''\ttime ='''			, timeit(
					        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''			, setup='''import __main__ as z'''			, )			, '''seconds'''			, )


if __name__ == "__main__":
		__snake_case											=					input(
		    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
		).strip()
		benchmark(check_str)
		__snake_case											=					can_string_be_rearranged_as_palindrome_counter(check_str)
		print(F"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")