






"""simple docstring"""







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     __UpperCAmelCase(   unittest.TestCase  ,  SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      int=       load_tool("image-question-answering"			)
												self.tool.setup()
												lowercase__				:      Union[str, Any]=       load_tool("image-question-answering"       ,  remote=snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
												lowercase__				:      str=       self.tool(snake_case__       ,  "How many cats are sleeping on the couch?"			)
												self.assertEqual(snake_case__       ,  "2"			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      str=       Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
												lowercase__				:      List[str]=       self.remote_tool(snake_case__       ,  "How many cats are sleeping on the couch?"			)
												self.assertEqual(snake_case__       ,  "2"			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      str=       Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
												lowercase__				:      int=       self.tool(image=snake_case__       ,  question="How many cats are sleeping on the couch?"			)
												self.assertEqual(snake_case__       ,  "2"			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Any=       Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
												lowercase__				:      Union[str, Any]=       self.remote_tool(image=snake_case__       ,  question="How many cats are sleeping on the couch?"			)
												self.assertEqual(snake_case__       ,  "2"			)














"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				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(
			    """--bert_config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained BERT model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






