


'''simple docstring'''
import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
       # Initialise PyTorch model
       A_ 						=		ElectraConfig.from_json_file(UpperCamelCase_   )
       print(f"Building PyTorch model from configuration: {config}"   )

       if discriminator_or_generator == "discriminator":
              A_ 						=		ElectraForPreTraining(UpperCamelCase_   )
       elif discriminator_or_generator == "generator":
              A_ 						=		ElectraForMaskedLM(UpperCamelCase_   )
       else:
              raise ValueError("The discriminator_or_generator argument should be either 'discriminator' or 'generator'"   )

       # Load weights from tf checkpoint
       load_tf_weights_in_electra(
           UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     discriminator_or_generator=UpperCamelCase_   )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"   )
       torch.save(model.state_dict()	,     UpperCamelCase_   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE				:							List[str]		       =		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(
           '''--config_file''',
           default=None,
           type=str,
           required=True,
           help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       parser.add_argument(
           '''--discriminator_or_generator''',
           default=None,
           type=str,
           required=True,
           help=(
               '''Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '''
               '''\'generator\'.'''
           ),
       )
       __SCREAMING_SNAKE_CASE				:							Dict		       =		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
       )









'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE				:							str		       =		logging.getLogger()







def 			a_							(  ):
							A_ 						=		argparse.ArgumentParser()
							parser.add_argument("-f"   )
							A_ 						=		parser.parse_args()
							return args.f







class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		logging.StreamHandler(sys.stdout  )
													logger.addHandler(lowerCAmelCase  )

													A_ 						=		self.get_auto_remove_tmp_dir()
													A_ 						=		F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

													with patch.object(lowerCAmelCase   ,     "argv"   ,     lowerCAmelCase  ):
																				A_ 						=		run_glue_with_pabee.main()
																				for value in result.values():
																											self.assertGreaterEqual(lowerCAmelCase   ,     0.7_5  )





