






"""simple docstring"""


import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types


class    snake_case						(				unittest.TestCase  ,      _lowerCAmelCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					load_tool('''translation'''    )
     self.tool.setup()
     __A		     =					load_tool('''translation''',			remote=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					self.tool('''Hey, what\'s up?''',			src_lang='''English''',			tgt_lang='''French'''    )
     self.assertEqual(_lowerCamelCase,			'''- Hé, comment ça va?'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					self.remote_tool('''Hey, what\'s up?''',			src_lang='''English''',			tgt_lang='''French'''    )
     self.assertEqual(_lowerCamelCase,			'''- Hé, comment ça va?'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.tool(text='''Hey, what\'s up?''',			src_lang='''English''',			tgt_lang='''French'''    )
     self.assertEqual(_lowerCamelCase,			'''- Hé, comment ça va?'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.remote_tool(text='''Hey, what\'s up?''',			src_lang='''English''',			tgt_lang='''French'''    )
     self.assertEqual(_lowerCamelCase,			'''- Hé, comment ça va?'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					['''Hey, what\'s up?''', '''English''', '''Spanish''']
     __A		     =					self.tool(*_lowerCamelCase    )

     # There is a single output
     if len(self.tool.outputs    ) == 1:
       __A		     =					[outputs]

     self.assertListEqual(output_types(_lowerCamelCase    ),			self.tool.outputs    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					['''Hey, what\'s up?''', '''English''', '''Spanish''']
     __A		     =					self.tool(*_lowerCamelCase    )

     if not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					[outputs]

     self.assertEqual(len(_lowerCamelCase    ),			len(self.tool.outputs    )    )

     for output, output_type in zip(_lowerCamelCase,			self.tool.outputs    ):
       __A		     =					AGENT_TYPE_MAPPING[output_type]
       self.assertTrue(isinstance(_lowerCamelCase,			_lowerCamelCase    )    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					['''Hey, what\'s up?''', '''English''', '''Spanish''']

     __A		     =					[]

     for _input, input_type in zip(_lowerCamelCase,			self.tool.inputs    ):
       if isinstance(_lowerCamelCase,			_lowerCamelCase    ):
         _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
       else:
         _inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
     __A		     =					self.tool(*_lowerCamelCase    )

     if not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					[outputs]

     self.assertEqual(len(_lowerCamelCase    ),			len(self.tool.outputs    )    )












"""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    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					RoFormerForMaskedLM(__UpperCamelCase					)

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

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'					)
  torch.save(model.state_dict()						,			__UpperCamelCase						,			_use_new_zipfile_serialization=__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(
           '--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.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




