


"""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 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: Tuple	,  _snake_case		: Union[str, Any]	,  _snake_case		: Tuple	)	->Optional[Any]:
	"""simple docstring"""





	__snake_case      :		Union[str, Any]         =							ElectraConfig.from_json_file(_snake_case	)
	print(f"""Building PyTorch model from configuration: {config}"""	)

	if discriminator_or_generator == "discriminator":
		__snake_case      :		Optional[Any]         =							ElectraForPreTraining(_snake_case	)
	elif discriminator_or_generator == "generator":
		__snake_case      :		Optional[int]         =							ElectraForMaskedLM(_snake_case	)
	else:
		raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''	)

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

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


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					:       str							   =							parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
			)












import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@patch('sys.argv'		, ['fakeprogrampath', 'env']						)
						def UpperCAmelCase_					(   self   :   Tuple						)				->     Dict:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							with CaptureStd() as cs:
								transformers.commands.transformers_cli.main()
							self.assertIn('Python version'		, cs.out						)
							self.assertIn('Platform'		, cs.out						)
							self.assertIn('Using distributed or parallel set-up in script?'		, cs.out						)






						@is_pt_tf_cross_test
						@patch(
						    'sys.argv'		, ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']						)
						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     Optional[Any]:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'		, ignore_errors=_A						)  # cleans potential past runs
							transformers.commands.transformers_cli.main()

							# The original repo has no TF weights -- if they exist, they were created by the CLI
							self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'						)						)






						@require_torch
						@patch('sys.argv'		, ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']						)
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     str:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							# # remove any previously downloaded model to start clean
							shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'		, ignore_errors=_A						)

							# run the command
							transformers.commands.transformers_cli.main()

							# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'						)						)






						@require_torch
						@patch(
						    'sys.argv'		, [
						        'fakeprogrampath',
						        'download',
						        'hf-internal-testing/test_dynamic_model_with_tokenizer',
						        '--trust-remote-code',
						        '--cache-dir',
						        '/tmp',
						    ]		, )
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     str:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							# # remove any previously downloaded model to start clean
							shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'		, ignore_errors=_A						)

							# run the command
							transformers.commands.transformers_cli.main()

							# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'						)						)
							self.assertTrue(
							    os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'						)						)

