






'''simple docstring'''


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   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 # Initialise PyTorch model
 __a    :	Tuple									=						FNetConfig.from_json_file(SCREAMING_SNAKE_CASE__		)
 print(f'''Building PyTorch model from configuration: {config}'''		)
 __a    :	Tuple									=						FNetForPreTraining(SCREAMING_SNAKE_CASE__		)

 __a    :	int									=						restore_checkpoint(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
 __a    :	List[Any]									=						checkpoint_dict['target']

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

 __a    :	List[str]									=						state_dict['fnet.embeddings.position_ids']
 __a    :	List[str]									=						{'fnet.embeddings.position_ids': position_ids}
 # Embedding Layers
 __a    :	Dict									=						torch.tensor(
     pretrained_model_params['encoder']['embedder']['word']['embedding']		)
 __a    :	int									=						torch.tensor(
     pretrained_model_params['encoder']['embedder']['position']['embedding'][0]		)
 __a    :	Optional[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    :	str									=						torch.tensor(
     pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']		)
 __a    :	List[Any]									=						torch.tensor(
     pretrained_model_params['encoder']['embedder']['layer_norm']['scale']		)
 __a    :	Union[str, 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    :	int									=						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    :	int									=						torch.tensor(
      pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['intermediate']['bias']		)

  __a    :	Any									=						torch.tensor(
      pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['output']['kernel']		).T
  __a    :	Union[str, Any]									=						torch.tensor(
      pretrained_model_params['encoder'][f'''feed_forward_{layer}''']['output']['bias']		)

  __a    :	List[Any]									=						torch.tensor(
      pretrained_model_params['encoder'][f'''encoder_{layer}''']['output_layer_norm']['scale']		)
  __a    :	Optional[Any]									=						torch.tensor(
      pretrained_model_params['encoder'][f'''encoder_{layer}''']['output_layer_norm']['bias']		)

 # Pooler Layers
 __a    :	Dict									=						torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']		).T
 __a    :	Union[str, 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    :	Union[str, Any]									=						torch.tensor(
     pretrained_model_params['predictions_dense']['bias']		)
 __a    :	Optional[int]									=						torch.tensor(
     pretrained_model_params['predictions_layer_norm']['scale']		)
 __a    :	Tuple									=						torch.tensor(
     pretrained_model_params['predictions_layer_norm']['bias']		)
 __a    :	List[Any]									=						torch.tensor(
     pretrained_model_params['encoder']['embedder']['word']['embedding']		)
 __a    :	str									=						torch.tensor(
     pretrained_model_params['predictions_output']['output_bias']		)
 __a    :	str									=						torch.tensor(pretrained_model_params['predictions_output']['output_bias']		)

 # Seq Relationship Layers
 __a    :	Tuple									=						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(SCREAMING_SNAKE_CASE__		)

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_					=							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.")
  SCREAMING_SNAKE_CASE_					=							parser.parse_args()
  convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)












'''simple docstring'''


def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if len(SCREAMING_SNAKE_CASE__		) <= 1:
  return data
 else:
  return [
      *quick_sort([e for e in data[1:] if e <= data[0]]		),
      data[0],
      *quick_sort([e for e in data[1:] if e > data[0]]		),
  ]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




