






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''ChineseCLIPImageProcessor'''
	lowerCAmelCase													= ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(       self       ,			a=None       ,			a=None       ,			**a					)       ->    Dict:
								snake_case_				=							None
								if "feature_extractor" in kwargs:
															warnings.warn(
															    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
															    ' instead.'       ,			a       ,			)
															snake_case_				=							kwargs.pop('feature_extractor'					)

								snake_case_				=							image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
															raise ValueError('You need to specify an `image_processor`.'					)
								if tokenizer is None:
															raise ValueError('You need to specify a `tokenizer`.'					)

								super().__init__(a       ,			a					)
								snake_case_				=							self.image_processor




	def __call__(       self       ,			a=None       ,			a=None       ,			a=None       ,			**a					)       ->    str:

								if text is None and images is None:
															raise ValueError('You have to specify either text or images. Both cannot be none.'					)

								if text is not None:
															snake_case_				=							self.tokenizer(a       ,			return_tensors=a       ,			**a					)

								if images is not None:
															snake_case_				=							self.image_processor(a       ,			return_tensors=a       ,			**a					)

								if text is not None and images is not None:
															snake_case_				=							image_features.pixel_values
															return encoding
								elif text is not None:
															return encoding
								else:
															return BatchEncoding(data=dict(**a					)       ,			tensor_type=a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    List[str]:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    str:
								return self.tokenizer.decode(*a       ,			**a					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								snake_case_				=							self.tokenizer.model_input_names
								snake_case_				=							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






	@property
	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,			a       ,			)
								return self.image_processor_class













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


lowercase  							=					{
    "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			(     a_							,     a_							,     a_							,     a_=None):
							# Initialise PyTorch model
							snake_case_				=							XLNetConfig.from_json_file(a_)

							snake_case_				=							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}''')
														snake_case_				=							finetuning_task
														snake_case_				=							GLUE_TASKS_NUM_LABELS[finetuning_task]
														snake_case_				=							XLNetForSequenceClassification(a_)
							elif "squad" in finetuning_task:
														snake_case_				=							finetuning_task
														snake_case_				=							XLNetForQuestionAnswering(a_)
							else:
														snake_case_				=							XLNetLMHeadModel(a_)

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

							# Save pytorch-model
							snake_case_				=							os.path.join(a_							,     a_)
							snake_case_				=							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__":
	lowercase  							=					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",
	)
	lowercase  							=					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
	)





