
'''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


__lowerCamelCase			      =      {
    "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__							:							Tuple       ,							UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Optional[int]=None):
				lowerCamelCase					:			Union[str, Any]									= XLNetConfig.from_json_file(lowerCamelCase__)

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

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

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


if __name__ == "__main__":
					__lowerCamelCase			      =      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			      =      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 __future__ import annotations

A			      =      '#'




class     __snake_case		:
					def __init__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			dict									= {}
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self._trie
									for char in text:
													if char not in trie:
																	lowerCamelCase					:			Dict									= {}
													lowerCamelCase					:			Optional[int]									= trie[char]
									lowerCamelCase					:			Optional[Any]									= True
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self._trie
									for char in prefix:
													if char in trie:
																	lowerCamelCase					:			int									= trie[char]
													else:
																	return []
									return self._elements(A      )




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= []
									for c, v in d.items():
													lowerCamelCase					:			Optional[Any]									= [' '] if c == END else [(c + s) for s in self._elements(A      )]
													result.extend(A      )
									return tuple(A      )





A			      =      Trie()
A			      =      ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
					trie.insert_word(word)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Any									= trie.find_word(UpperCAmelCase__)
				return tuple(string + word for word in suffixes)





def    UpperCAmelCase							(  ):
				print(autocomplete_using_trie('de'))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
