






"""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				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      super().__init__(features=_lowerCamelCase			)
      lowercase				       =			np_array_kwargs







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







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

      lowercase				       =			{}

      if isinstance(_lowerCamelCase				,						np.ndarray			) and np.issubdtype(value.dtype				,						np.integer			):
        lowercase				       =			{'dtype': np.intaa}
      elif isinstance(_lowerCamelCase				,						np.ndarray			) and np.issubdtype(value.dtype				,						np.floating			):
        lowercase				       =			{'dtype': np.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

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

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







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

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







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      return map_nested(self._recursive_tensorize				,						_lowerCamelCase				,						map_list=_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.numpy_arrow_extractor().extract_row(_lowerCamelCase			)
      lowercase				       =			self.python_features_decoder.decode_row(_lowerCamelCase			)
      return self.recursive_tensorize(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.numpy_arrow_extractor().extract_column(_lowerCamelCase			)
      lowercase				       =			self.python_features_decoder.decode_column(_lowerCamelCase				,						pa_table.column_names[0]			)
      lowercase				       =			self.recursive_tensorize(_lowerCamelCase			)
      lowercase				       =			self._consolidate(_lowerCamelCase			)
      return column







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.numpy_arrow_extractor().extract_batch(_lowerCamelCase			)
      lowercase				       =			self.python_features_decoder.decode_batch(_lowerCamelCase			)
      lowercase				       =			self.recursive_tensorize(_lowerCamelCase			)
      for column_name in batch:
        lowercase				       =			self._consolidate(batch[column_name]			)
      return batch







"""simple docstring"""






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			CanineConfig()
  _SCREAMING_SNAKE_CASE        =			CanineModel(UpperCamelCase__    )
  model.eval()

  print(F'''Building PyTorch model from configuration: {config}'''    )

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    )

  # Save pytorch-model (weights and configuration)
  print(F'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(UpperCamelCase__    )

  # Save tokenizer files
  _SCREAMING_SNAKE_CASE        =			CanineTokenizer()
  print(F'''Save tokenizer files to {pytorch_dump_path}'''    )
  tokenizer.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
    lowerCamelCase				:     Dict								=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_checkpoint_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
    )
    parser.add_argument(
        """--pytorch_dump_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to a folder where the PyTorch model will be placed.""",
    )
    lowerCamelCase				:     str								=  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
