


import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class   A__							(						unittest.TestCase      ,   SCREAMING_SNAKE_CASE__     ):




						def 							__UpperCamelCase (				self    :					List[Any]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =load_tool('''document-question-answering'''     )
										self.tool.setup()
										_SCREAMING_SNAKE_CASE	     =load_tool('''document-question-answering''' ,			remote=snake_case__     )




						def 							__UpperCamelCase (				self    :					List[str]     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =load_dataset('''hf-internal-testing/example-documents''' ,			split='''test'''     )
										_SCREAMING_SNAKE_CASE	     =dataset[0]["image"]

										_SCREAMING_SNAKE_CASE	     =self.tool(snake_case__ ,			'''When is the coffee break?'''     )
										self.assertEqual(snake_case__ ,			'''11-14 to 11:39 a.m.'''     )




						def 							__UpperCamelCase (				self    :					str     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =load_dataset('''hf-internal-testing/example-documents''' ,			split='''test'''     )
										_SCREAMING_SNAKE_CASE	     =dataset[0]["image"]

										_SCREAMING_SNAKE_CASE	     =self.remote_tool(snake_case__ ,			'''When is the coffee break?'''     )
										self.assertEqual(snake_case__ ,			'''11-14 to 11:39 a.m.'''     )




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =load_dataset('''hf-internal-testing/example-documents''' ,			split='''test'''     )
										_SCREAMING_SNAKE_CASE	     =dataset[0]["image"]

										self.tool(document=snake_case__ ,			question='''When is the coffee break?'''     )




						def 							__UpperCamelCase (				self    :					str     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =load_dataset('''hf-internal-testing/example-documents''' ,			split='''test'''     )
										_SCREAMING_SNAKE_CASE	     =dataset[0]["image"]

										_SCREAMING_SNAKE_CASE	     =self.remote_tool(document=snake_case__ ,			question='''When is the coffee break?'''     )
										self.assertEqual(snake_case__ ,			'''11-14 to 11:39 a.m.'''     )







"""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)






