
'''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 	_UpperCAmelCase				(  _lowerCamelCase						:       Union[str, Any]  ,      _lowerCamelCase						:       List[Any]  ,      _lowerCamelCase						:       int		)    ->						List[Any]:
  # Initialise PyTorch model
  _lowerCAmelCase       :  Optional[Any]   				=					RoFormerConfig.from_json_file(_lowerCamelCase		)
  print(f'Building PyTorch model from configuration: {config}'		)
  _lowerCAmelCase       :  List[Any]   				=					RoFormerForMaskedLM(_lowerCamelCase		)

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

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


if __name__ == "__main__":
     UpperCamelCase_     			=       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."""
     )
     UpperCamelCase_     			=       parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




'''simple docstring'''

import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


class   a_  (TensorFormatter[Mapping, np.ndarray, Mapping]    ):




       def __init__(     self   ,   snake_case_=None   ,   **snake_case_  ):
         super().__init__(features=snake_case_  )
         _lowerCAmelCase       :  Any   				=					np_array_kwargs




       def 							__UpperCamelCase       (     self   ,   snake_case_  ):
         if isinstance(snake_case_   ,   snake_case_  ):
           if column and all(
               isinstance(snake_case_   ,   np.ndarray  ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column  ):
             return np.stack(snake_case_  )
           else:
             # don't use np.array(column, dtype=object)
             # since it fails in certain cases
             # see https://stackoverflow.com/q/51005699
             _lowerCAmelCase       :  str   				=					np.empty(len(snake_case_  )   ,   dtype=snake_case_  )
             _lowerCAmelCase       :  List[Any]   				=					column
             return out
         return column




       def 							__UpperCamelCase       (     self   ,   snake_case_  ):
         if isinstance(snake_case_   ,   (str, bytes, type(snake_case_  ))  ):
           return value
         elif isinstance(snake_case_   ,   (np.character, np.ndarray)  ) and np.issubdtype(value.dtype   ,   np.character  ):
           return value
         elif isinstance(snake_case_   ,   np.number  ):
           return value

         _lowerCAmelCase       :  List[Any]   				=					{}

         if isinstance(snake_case_   ,   np.ndarray  ) and np.issubdtype(value.dtype   ,   np.integer  ):
           _lowerCAmelCase       :  Union[str, Any]   				=					{"""dtype""": np.intaa}
         elif isinstance(snake_case_   ,   np.ndarray  ) and np.issubdtype(value.dtype   ,   np.floating  ):
           _lowerCAmelCase       :  Tuple   				=					{"""dtype""": np.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(snake_case_   ,   PIL.Image.Image  ):
             return np.asarray(snake_case_   ,   **self.np_array_kwargs  )

         return np.asarray(snake_case_   ,   **{**default_dtype, **self.np_array_kwargs}  )




       def 							__UpperCamelCase       (     self   ,   snake_case_  ):
         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(snake_case_   ,   torch.Tensor  ):
             return self._tensorize(data_struct.detach().cpu().numpy()[()]  )
         if hasattr(snake_case_   ,   """__array__"""  ) and not isinstance(snake_case_   ,   (np.ndarray, np.character, np.number)  ):
           _lowerCAmelCase       :  List[Any]   				=					data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(snake_case_   ,   np.ndarray  ):
           if data_struct.dtype == object:
             return self._consolidate([self.recursive_tensorize(snake_case_  ) for substruct in data_struct]  )
         if isinstance(snake_case_   ,   (list, tuple)  ):
           return self._consolidate([self.recursive_tensorize(snake_case_  ) for substruct in data_struct]  )
         return self._tensorize(snake_case_  )




       def 							__UpperCamelCase       (     self   ,   snake_case_  ):
         return map_nested(self._recursive_tensorize   ,   snake_case_   ,   map_list=snake_case_  )




       def 							__UpperCamelCase       (     self   ,   snake_case_  ):
         _lowerCAmelCase       :  Optional[Any]   				=					self.numpy_arrow_extractor().extract_row(snake_case_  )
         _lowerCAmelCase       :  Any   				=					self.python_features_decoder.decode_row(snake_case_  )
         return self.recursive_tensorize(snake_case_  )




       def 							__UpperCamelCase       (     self   ,   snake_case_  ):
         _lowerCAmelCase       :  List[Any]   				=					self.numpy_arrow_extractor().extract_column(snake_case_  )
         _lowerCAmelCase       :  Tuple   				=					self.python_features_decoder.decode_column(snake_case_   ,   pa_table.column_names[0]  )
         _lowerCAmelCase       :  Optional[int]   				=					self.recursive_tensorize(snake_case_  )
         _lowerCAmelCase       :  Optional[Any]   				=					self._consolidate(snake_case_  )
         return column







       def 							__UpperCamelCase       (     self   ,   snake_case_  ):
         _lowerCAmelCase       :  List[Any]   				=					self.numpy_arrow_extractor().extract_batch(snake_case_  )
         _lowerCAmelCase       :  str   				=					self.python_features_decoder.decode_batch(snake_case_  )
         _lowerCAmelCase       :  Dict   				=					self.recursive_tensorize(snake_case_  )
         for column_name in batch:
           _lowerCAmelCase       :  int   				=					self._consolidate(batch[column_name]  )
         return batch


