



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__	      =       {
    """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				(      snake_case	,						snake_case	,						snake_case	,						snake_case=None			):





					"""simple docstring"""

					_lowerCAmelCase						=    XLNetConfig.from_json_file(snake_case			)

					_lowerCAmelCase						=    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						=    finetuning_task
										_lowerCAmelCase						=    GLUE_TASKS_NUM_LABELS[finetuning_task]
										_lowerCAmelCase						=    XLNetForSequenceClassification(snake_case			)
					elif "squad" in finetuning_task:
										_lowerCAmelCase						=    finetuning_task
										_lowerCAmelCase						=    XLNetForQuestionAnswering(snake_case			)
					else:
										_lowerCAmelCase						=    XLNetLMHeadModel(snake_case			)

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

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


if __name__ == "__main__":
	A__	      =       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__	      =       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 __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase	:     Dict   ={
    """susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
    """susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							"""ernie_m"""
						SCREAMING_SNAKE_CASE__					:     Dict[str, str]    =							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




						def __init__( self      ,  __lowercase = 2_5_0_0_0_2      ,  __lowercase = 7_6_8      ,  __lowercase = 1_2      ,  __lowercase = 1_2      ,  __lowercase = 3_0_7_2      ,  __lowercase = "gelu"      ,  __lowercase = 0.1      ,  __lowercase = 0.1      ,  __lowercase = 5_1_4      ,  __lowercase = 0.02      ,  __lowercase = 1      ,  __lowercase = 1e-05      ,  __lowercase=None      ,  __lowercase=False      ,  __lowercase=0.0      ,  **__lowercase      ,  )    ->			Tuple:
										super().__init__(pad_token_id=__lowercase      ,  **__lowercase							)
										lowerCAmelCase_       :					Tuple			 = vocab_size
										lowerCAmelCase_       :					Dict			 = hidden_size
										lowerCAmelCase_       :					Tuple			 = num_hidden_layers
										lowerCAmelCase_       :					int			 = num_attention_heads
										lowerCAmelCase_       :					Dict			 = intermediate_size
										lowerCAmelCase_       :					int			 = hidden_act
										lowerCAmelCase_       :					Union[str, Any]			 = hidden_dropout_prob
										lowerCAmelCase_       :					Any			 = attention_probs_dropout_prob
										lowerCAmelCase_       :					Union[str, Any]			 = max_position_embeddings
										lowerCAmelCase_       :					str			 = initializer_range
										lowerCAmelCase_       :					List[str]			 = layer_norm_eps
										lowerCAmelCase_       :					List[Any]			 = classifier_dropout
										lowerCAmelCase_       :					Any			 = is_decoder
										lowerCAmelCase_       :					List[Any]			 = act_dropout