



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
	)










'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase					:     Optional[int]												=logging.getLogger()
def 							UpperCAmelCase_  (						):
   lowercase_			:Union[str, Any]    			= argparse.ArgumentParser()
   parser.add_argument("-f" )
   lowercase_			:Any    			= parser.parse_args()
   return args.f


class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= logging.StreamHandler(sys.stdout						)
        logger.addHandler(lowercase						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Dict    			= get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0	,      "run_glue_deebert.py"						)
           with patch.object(lowercase	,      "argv"	,      lowercase						):
              lowercase_			:int    			= run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(lowercase	,      0.6_66						)







     @slow
     @require_torch_non_multi_gpu
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:List[Any]    			= "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:List[str]    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:Any    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)


