






'''simple docstring'''
import unittest

from knapsack import greedy_knapsack as kp


class        _UpperCAmelCase						(		unittest.TestCase			):




				def 				lowerCAmelCase__				(			self  :       Optional[int]			):






							'''simple docstring'''






							lowercase_	:    Dict           =     [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
							lowercase_	:    str           =     [2, 4, 6, 8, 1_0, 1_2]
							lowercase_	:    Union[str, Any]           =     1_0_0
							self.assertEqual(kp.calc_profit(a  ,     a  ,     a			)  ,     2_1_0			)




				def 				lowerCAmelCase__				(			self  :       List[Any]			):






							'''simple docstring'''






							self.assertRaisesRegex(a  ,     "max_weight must greater than zero."			)




				def 				lowerCAmelCase__				(			self  :       Dict			):






							'''simple docstring'''






							self.assertRaisesRegex(a  ,     "Weight can not be negative."			)




				def 				lowerCAmelCase__				(			self  :       List[str]			):






							'''simple docstring'''






							self.assertRaisesRegex(a  ,     "Profit can not be negative."			)




				def 				lowerCAmelCase__				(			self  :       Optional[int]			):






							'''simple docstring'''






							self.assertRaisesRegex(a  ,     "max_weight must greater than zero."			)





				def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






							'''simple docstring'''






							self.assertRaisesRegex(
							    a  ,     "The length of profit and weight must be same."			)


if __name__ == "__main__":
	unittest.main()












'''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


UpperCamelCase__ =			{
    '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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None							):


   """simple docstring"""




   lowercase_	:    Tuple           =     XLNetConfig.from_json_file(_UpperCamelCase							)

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

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

   # Save pytorch-model
   lowercase_	:    Optional[Any]           =     os.path.join(_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Dict           =     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__ =			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__ =			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
 )




