
'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


lowercase__											=logging.get_logger(__name__)







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			List[str]       =    'upernet'




			def __init__(       self					,   UpperCAmelCase=None					,   UpperCAmelCase=5_12					,   UpperCAmelCase=0.02					,   UpperCAmelCase=[1, 2, 3, 6]					,   UpperCAmelCase=True					,   UpperCAmelCase=0.4					,   UpperCAmelCase=3_84					,   UpperCAmelCase=2_56					,   UpperCAmelCase=1					,   UpperCAmelCase=False					,   UpperCAmelCase=2_55					,   **UpperCAmelCase					,   ):
				super().__init__(**UpperCAmelCase	)

				if backbone_config is None:
					logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""	)
					a_      				=						CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	)
				elif isinstance(UpperCAmelCase					,   UpperCAmelCase	):
					a_      				=						backbone_config.get("""model_type"""	)
					a_      				=						CONFIG_MAPPING[backbone_model_type]
					a_      				=						config_class.from_dict(UpperCAmelCase	)

				a_      				=						backbone_config
				a_      				=						hidden_size
				a_      				=						initializer_range
				a_      				=						pool_scales
				a_      				=						use_auxiliary_head
				a_      				=						auxiliary_loss_weight
				a_      				=						auxiliary_in_channels
				a_      				=						auxiliary_channels
				a_      				=						auxiliary_num_convs
				a_      				=						auxiliary_concat_input
				a_      				=						loss_ignore_index






			def 					lowerCAmelCase__					(       self	):
				a_      				=						copy.deepcopy(self.__dict__	)
				a_      				=						self.backbone_config.to_dict()
				a_      				=						self.__class__.model_type
				return output


'''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


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
	a_      				=						XLNetConfig.from_json_file(A__						)

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

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

	# Save pytorch-model
	a_      				=						os.path.join(A__  ,      A__						)
	a_      				=						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
		)
