





"""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  a_       (							_lowercase ,		_lowercase ,		_lowercase     ):
						# Initialise PyTorch model
						_UpperCamelCase    :      str						     =					FNetConfig.from_json_file(_lowercase     )
						print(F"""Building PyTorch model from configuration: {config}"""     )
						_UpperCamelCase    :      Optional[Any]						     =					FNetForPreTraining(_lowercase     )

						_UpperCamelCase    :      str						     =					restore_checkpoint(_lowercase ,		_lowercase     )
						_UpperCamelCase    :      Tuple						     =					checkpoint_dict['''target''']

						# Embeddings
						# Position IDs
						_UpperCamelCase    :      Dict						     =					fnet_pretraining_model.state_dict()

						_UpperCamelCase    :      int						     =					state_dict['''fnet.embeddings.position_ids''']
						_UpperCamelCase    :      int						     =					{'''fnet.embeddings.position_ids''': position_ids}
						# Embedding Layers
						_UpperCamelCase    :      Dict						     =					torch.tensor(
						    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']     )
						_UpperCamelCase    :      int						     =					torch.tensor(
						    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]     )
						_UpperCamelCase    :      Dict						     =					torch.tensor(
						    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']     )
						_UpperCamelCase    :      Any						     =					torch.tensor(
						    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']     ).T
						_UpperCamelCase    :      Any						     =					torch.tensor(
						    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']     )
						_UpperCamelCase    :      List[Any]						     =					torch.tensor(
						    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']     )
						_UpperCamelCase    :      int						     =					torch.tensor(
						    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']     )

						# Encoder Layers
						for layer in range(config.num_hidden_layers     ):
												_UpperCamelCase    :      List[Any]						     =					torch.tensor(
												    pretrained_model_params['''encoder'''][F"""encoder_{layer}"""]['''mixing_layer_norm''']['''scale''']     )
												_UpperCamelCase    :      Optional[int]						     =					torch.tensor(
												    pretrained_model_params['''encoder'''][F"""encoder_{layer}"""]['''mixing_layer_norm''']['''bias''']     )

												_UpperCamelCase    :      Union[str, Any]						     =					torch.tensor(
												    pretrained_model_params['''encoder'''][F"""feed_forward_{layer}"""]['''intermediate''']['''kernel''']     ).T
												_UpperCamelCase    :      Tuple						     =					torch.tensor(
												    pretrained_model_params['''encoder'''][F"""feed_forward_{layer}"""]['''intermediate''']['''bias''']     )

												_UpperCamelCase    :      str						     =					torch.tensor(
												    pretrained_model_params['''encoder'''][F"""feed_forward_{layer}"""]['''output''']['''kernel''']     ).T
												_UpperCamelCase    :      Dict						     =					torch.tensor(
												    pretrained_model_params['''encoder'''][F"""feed_forward_{layer}"""]['''output''']['''bias''']     )

												_UpperCamelCase    :      Union[str, Any]						     =					torch.tensor(
												    pretrained_model_params['''encoder'''][F"""encoder_{layer}"""]['''output_layer_norm''']['''scale''']     )
												_UpperCamelCase    :      Dict						     =					torch.tensor(
												    pretrained_model_params['''encoder'''][F"""encoder_{layer}"""]['''output_layer_norm''']['''bias''']     )

						# Pooler Layers
						_UpperCamelCase    :      Optional[int]						     =					torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']     ).T
						_UpperCamelCase    :      Optional[int]						     =					torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']     )

						# Masked LM Layers
						_UpperCamelCase    :      Optional[Any]						     =					torch.tensor(
						    pretrained_model_params['''predictions_dense''']['''kernel''']     ).T
						_UpperCamelCase    :      Optional[Any]						     =					torch.tensor(
						    pretrained_model_params['''predictions_dense''']['''bias''']     )
						_UpperCamelCase    :      Any						     =					torch.tensor(
						    pretrained_model_params['''predictions_layer_norm''']['''scale''']     )
						_UpperCamelCase    :      str						     =					torch.tensor(
						    pretrained_model_params['''predictions_layer_norm''']['''bias''']     )
						_UpperCamelCase    :      int						     =					torch.tensor(
						    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']     )
						_UpperCamelCase    :      int						     =					torch.tensor(
						    pretrained_model_params['''predictions_output''']['''output_bias''']     )
						_UpperCamelCase    :      List[str]						     =					torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']     )

						# Seq Relationship Layers
						_UpperCamelCase    :      str						     =					torch.tensor(
						    pretrained_model_params['''classification''']['''output_kernel''']     )
						_UpperCamelCase    :      Any						     =					torch.tensor(
						    pretrained_model_params['''classification''']['''output_bias''']     )

						# Load State Dict
						fnet_pretraining_model.load_state_dict(_lowercase     )

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


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










"""simple docstring"""




def  a_       (							_lowercase     ):
						_UpperCamelCase    :      str						     =					len(_lowercase     )

						# Set the gap value to be decreased by a factor of 1.3
						# after each iteration
						_UpperCamelCase    :      str						     =					1.3

						# Continue sorting until the gap is 1
						while gap > 1:
												# Decrease the gap value
												_UpperCamelCase    :      Union[str, Any]						     =					int(gap / shrink     )

												# Sort the elements using insertion sort
												for i in range(_lowercase ,		len(_lowercase     )     ):
																		_UpperCamelCase    :      Optional[Any]						     =					collection[i]
																		_UpperCamelCase    :      int						     =					i
																		while j >= gap and collection[j - gap] > temp:
																								_UpperCamelCase    :      List[str]						     =					collection[j - gap]
																								j -= gap
																		_UpperCamelCase    :      Union[str, Any]						     =					temp

						return collection


if __name__ == "__main__":
						import doctest

						doctest.testmod()



