





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
 import tensorflow as tf


class 					__magic_name__  (							TensorFormatter[Mapping, '''tf.Tensor''', Mapping]							):





     """simple docstring"""



     def __init__(							self       :List[Any]			,      snake_case       :Optional[Any]=None			,      **snake_case       :str  ):





        '''simple docstring'''




        super().__init__(features=lowerCamelCase__  )
        A_					:				Dict          =			tf_tensor_kwargs
        import tensorflow as tf  # noqa: import tf at initialization



     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :List[Any]  ):





        '''simple docstring'''




        import tensorflow as tf

        if isinstance(lowerCamelCase__			,      lowerCamelCase__  ) and column:
           if all(
               isinstance(lowerCamelCase__			,      tf.Tensor  ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column  ):
              return tf.stack(lowerCamelCase__  )
           elif all(
               isinstance(lowerCamelCase__			,      (tf.Tensor, tf.RaggedTensor)  ) and x.ndim == 1 and x.dtype == column[0].dtype
               for x in column  ):
              # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
              return tf.ragged.stack(lowerCamelCase__  )

        return column



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :Union[str, Any]  ):





        '''simple docstring'''




        import tensorflow as tf

        if value is None:
           return value

        A_					:				str          =			{}

        if isinstance(lowerCamelCase__			,      (np.number, np.ndarray)  ) and np.issubdtype(value.dtype			,      np.integer  ):
           A_					:				Any          =			{"dtype": tf.intaa}
        elif isinstance(lowerCamelCase__			,      (np.number, np.ndarray)  ) and np.issubdtype(value.dtype			,      np.floating  ):
           A_					:				int          =			{"dtype": tf.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(lowerCamelCase__			,      PIL.Image.Image  ):
              A_					:				List[str]          =			np.asarray(lowerCamelCase__  )

        return tf.convert_to_tensor(lowerCamelCase__			,      **{**default_dtype, **self.tf_tensor_kwargs}  )



     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        import tensorflow as tf

        # 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__			,      tf.Tensor  ):
           A_					:				Union[str, Any]          =			data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(lowerCamelCase__			,      np.ndarray  ):
           if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
              return self._consolidate([self.recursive_tensorize(lowerCamelCase__  ) for substruct in data_struct]  )
        elif isinstance(lowerCamelCase__			,      (list, tuple)  ):
           return self._consolidate([self.recursive_tensorize(lowerCamelCase__  ) for substruct in data_struct]  )
        return self._tensorize(lowerCamelCase__  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :Optional[int]  ):





        '''simple docstring'''




        return map_nested(self._recursive_tensorize			,      lowerCamelCase__			,      map_list=lowerCamelCase__  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Any			,      snake_case       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				List[Any]          =			self.numpy_arrow_extractor().extract_row(lowerCamelCase__  )
        A_					:				int          =			self.python_features_decoder.decode_row(lowerCamelCase__  )
        return self.recursive_tensorize(lowerCamelCase__  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Any			,      snake_case       :List[str]  ):





        '''simple docstring'''




        A_					:				List[str]          =			self.numpy_arrow_extractor().extract_column(lowerCamelCase__  )
        A_					:				List[Any]          =			self.python_features_decoder.decode_column(lowerCamelCase__			,      pa_table.column_names[0]  )
        A_					:				Tuple          =			self.recursive_tensorize(lowerCamelCase__  )
        A_					:				Optional[Any]          =			self._consolidate(lowerCamelCase__  )
        return column





     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]			,      snake_case       :Tuple  ):





        '''simple docstring'''




        A_					:				List[str]          =			self.numpy_arrow_extractor().extract_batch(lowerCamelCase__  )
        A_					:				List[Any]          =			self.python_features_decoder.decode_batch(lowerCamelCase__  )
        A_					:				Optional[Any]          =			self.recursive_tensorize(lowerCamelCase__  )
        for column_name in batch:
           A_					:				Union[str, Any]          =			self._consolidate(batch[column_name]  )
        return batch







import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def      __snake_case						(					_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : List[str]				,				_lowerCAmelCase : List[Any]	) ->						Any:
   # Initialise PyTorch model
   A_					:				Tuple          =			FNetConfig.from_json_file(_lowerCAmelCase	)
   print(f"Building PyTorch model from configuration: {config}"	)
   A_					:				Tuple          =			FNetForPreTraining(_lowerCAmelCase	)

   A_					:				List[str]          =			restore_checkpoint(_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Any          =			checkpoint_dict["target"]

   # Embeddings
   # Position IDs
   A_					:				Optional[Any]          =			fnet_pretraining_model.state_dict()

   A_					:				Optional[int]          =			state_dict["fnet.embeddings.position_ids"]
   A_					:				Union[str, Any]          =			{"fnet.embeddings.position_ids": position_ids}
   # Embedding Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]	)
   A_					:				int          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]	).T
   A_					:				Dict          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]	)
   A_					:				int          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]	)
   A_					:				Any          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]	)

   # Encoder Layers
   for layer in range(config.num_hidden_layers	):
      A_					:				List[str]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["scale"]	)
      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["bias"]	)

      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]	).T
      A_					:				List[Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]	)

      A_					:				Optional[int]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"]	).T
      A_					:				int          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"]	)

      A_					:				Any          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["scale"]	)
      A_					:				Tuple          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["bias"]	)

   # Pooler Layers
   A_					:				Optional[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]	).T
   A_					:				List[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]	)

   # Masked LM Layers
   A_					:				Optional[Any]          =			torch.tensor(
       pretrained_model_params["predictions_dense"]["kernel"]	).T
   A_					:				int          =			torch.tensor(
       pretrained_model_params["predictions_dense"]["bias"]	)
   A_					:				str          =			torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["scale"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["bias"]	)
   A_					:				Tuple          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
   A_					:				Any          =			torch.tensor(
       pretrained_model_params["predictions_output"]["output_bias"]	)
   A_					:				List[str]          =			torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]	)

   # Seq Relationship Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_kernel"]	)
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_bias"]	)

   # Load State Dict
   fnet_pretraining_model.load_state_dict(_lowerCAmelCase	)

   # Save PreTrained
   print(f"Saving pretrained model to {save_path}"	)
   fnet_pretraining_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
 _lowerCAmelCase							:      Tuple						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--fnet_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained FNet model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 _lowerCAmelCase							:      Optional[int]						=	parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
