


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


A			:						Dict        =  {
    '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   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				List[Any]       ,       __magic_name__	:				str       ,       __magic_name__	:				Union[str, Any]=None	)  ->    Any:
		"""simple docstring"""




		lowercase__	     =						XLNetConfig.from_json_file(__magic_name__	)

		lowercase__	     =						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__	     =						finetuning_task
				lowercase__	     =						GLUE_TASKS_NUM_LABELS[finetuning_task]
				lowercase__	     =						XLNetForSequenceClassification(__magic_name__	)
		elif "squad" in finetuning_task:
				lowercase__	     =						finetuning_task
				lowercase__	     =						XLNetForQuestionAnswering(__magic_name__	)
		else:
				lowercase__	     =						XLNetLMHeadModel(__magic_name__	)

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

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


if __name__ == "__main__":
	A			:						Optional[Any]        =  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',
	)
	A			:						Any        =  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
	)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A			:						Union[str, Any]        =  {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  ['ConvNextFeatureExtractor']
	A			:						Optional[Any]        =  ['ConvNextImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  [
	    'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConvNextForImageClassification',
	    'ConvNextModel',
	    'ConvNextPreTrainedModel',
	    'ConvNextBackbone',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'TFConvNextForImageClassification',
	    'TFConvNextModel',
	    'TFConvNextPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_convnext import ConvNextFeatureExtractor
		from .image_processing_convnext import ConvNextImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnext import (
		    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextBackbone,
		    ConvNextForImageClassification,
		    ConvNextModel,
		    ConvNextPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
	import sys

	A			:						int        =  _LazyModule(__name__, globals()['__file__'], _import_structure)

