


'''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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: List[Any]=None ):






			"""simple docstring"""




			__A=	XLNetConfig.from_json_file(_SCREAMING_SNAKE_CASE )

			__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(_SCREAMING_SNAKE_CASE )
			elif "squad" in finetuning_task:
						__A=	finetuning_task
						__A=	XLNetForQuestionAnswering(_SCREAMING_SNAKE_CASE )
			else:
						__A=	XLNetLMHeadModel(_SCREAMING_SNAKE_CASE )

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

			# Save pytorch-model
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			print(f"""Save PyTorch model to {os.path.abspath(_SCREAMING_SNAKE_CASE )}""" )
			torch.save(model.state_dict(),_SCREAMING_SNAKE_CASE )
			print(f"""Save configuration file to {os.path.abspath(_SCREAMING_SNAKE_CASE )}""" )
			with open(_SCREAMING_SNAKE_CASE,'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
					)







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Union[str, Any]					      =			'''roberta'''
			def __init__(  self							:			Any			,    lowerCAmelCase_							:			int=50_265			,    lowerCAmelCase_							:			int=768			,    lowerCAmelCase_							:			Optional[Any]=12			,    lowerCAmelCase_							:			Tuple=12			,    lowerCAmelCase_							:			Optional[int]=3_072			,    lowerCAmelCase_							:			List[str]="gelu"			,    lowerCAmelCase_							:			List[str]=0.1			,    lowerCAmelCase_							:			Tuple=0.1			,    lowerCAmelCase_							:			List[Any]=512			,    lowerCAmelCase_							:			Union[str, Any]=2			,    lowerCAmelCase_							:			List[Any]=0.02			,    lowerCAmelCase_							:			Optional[int]=1E-12			,    lowerCAmelCase_							:			str=1			,    lowerCAmelCase_							:			Dict=0			,    lowerCAmelCase_							:			Any=2			,    lowerCAmelCase_							:			List[str]="absolute"			,    lowerCAmelCase_							:			Any=True			,    lowerCAmelCase_							:			Dict=None			,    **lowerCAmelCase_							:			Any			,    )      ->		int:
						super().__init__(pad_token_id=lowerCAmelCase_			,    bos_token_id=lowerCAmelCase_			,    eos_token_id=lowerCAmelCase_			,    **lowerCAmelCase_	)

						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	hidden_act
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	type_vocab_size
						__A=	initializer_range
						__A=	layer_norm_eps
						__A=	position_embedding_type
						__A=	use_cache
						__A=	classifier_dropout


class 						a__					(					a_   ):





			'''simple docstring'''

			@property
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=	{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]	)



