




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 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)  ->Any:






					"""simple docstring"""






					__magic_name__ :							Optional[int]		       =							ElectraConfig.from_json_file(UpperCAmelCase	)
					print(F'''Building PyTorch model from configuration: {config}'''	)

					if discriminator_or_generator == "discriminator":
										__magic_name__ :							int		       =							ElectraForPreTraining(UpperCAmelCase	)
					elif discriminator_or_generator == "generator":
										__magic_name__ :							Optional[Any]		       =							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__":
				lowercase_		=							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\'.'''
				    ),
				)
				lowercase_		=							parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
				)









import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
				from PIL import Image





class 		A__    (       unittest.TestCase		,					__SCREAMING_SNAKE_CASE			):






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							load_tool('''image-question-answering'''						)
						self.tool.setup()
						__magic_name__ :							Any		       =							load_tool('''image-question-answering'''    ,					remote=lowerCamelCase						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.remote_tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							int		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.remote_tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)



