





'''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  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int				)->   str:






					"""simple docstring"""

					_UpperCamelCase											=   FNetConfig.from_json_file(_UpperCamelCase				)
					print(f"Building PyTorch model from configuration: {config}"				)
					_UpperCamelCase											=   FNetForPreTraining(_UpperCamelCase				)

					_UpperCamelCase											=   restore_checkpoint(_UpperCamelCase ,   _UpperCamelCase				)
					_UpperCamelCase											=   checkpoint_dict["target"]

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

					_UpperCamelCase											=   state_dict["fnet.embeddings.position_ids"]
					_UpperCamelCase											=   {"fnet.embeddings.position_ids": position_ids}
					# Embedding Layers
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]				)
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]				)
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]				)
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]				).T
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]				)
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]				)
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]				)

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

										_UpperCamelCase											=   torch.tensor(
										    pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]				).T
										_UpperCamelCase											=   torch.tensor(
										    pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]				)

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

										_UpperCamelCase											=   torch.tensor(
										    pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["scale"]				)
										_UpperCamelCase											=   torch.tensor(
										    pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["bias"]				)

					# Pooler Layers
					_UpperCamelCase											=   torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]				).T
					_UpperCamelCase											=   torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]				)

					# Masked LM Layers
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["predictions_dense"]["kernel"]				).T
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["predictions_dense"]["bias"]				)
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["predictions_layer_norm"]["scale"]				)
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["predictions_layer_norm"]["bias"]				)
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]				)
					_UpperCamelCase											=   torch.tensor(
					    pretrained_model_params["predictions_output"]["output_bias"]				)
					_UpperCamelCase											=   torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]				)

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

					# Load State Dict
					fnet_pretraining_model.load_state_dict(_UpperCamelCase				)

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


if __name__ == "__main__":
			snake_case_   :  str								=      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.''')
			snake_case_   :  Union[str, Any]								=      parser.parse_args()
			convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)










'''simple docstring'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[int]				)->   list[int]:






					"""simple docstring"""

					if not list_of_ints:
										return []

					if min(_UpperCamelCase				) < 0:
										raise ValueError("All numbers must be positive"				)

					_UpperCamelCase											=   max(len(bin(_UpperCamelCase				)[2:]				) for x in list_of_ints				)
					return _msd_radix_sort(_UpperCamelCase ,   _UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : list[int] ,   _UpperCamelCase  : int				)->   list[int]:






					"""simple docstring"""

					if bit_position == 0 or len(_UpperCamelCase				) in [0, 1]:
										return list_of_ints

					_UpperCamelCase											=   []
					_UpperCamelCase											=   []
					# Split numbers based on bit at bit_position from the right
					for number in list_of_ints:
										if (number >> (bit_position - 1)) & 1:
															# number has a one at bit bit_position
															ones.append(_UpperCamelCase				)
										else:
															# number has a zero at bit bit_position
															zeros.append(_UpperCamelCase				)

    # recursively split both lists further
					_UpperCamelCase											=   _msd_radix_sort(_UpperCamelCase ,   bit_position - 1				)
					_UpperCamelCase											=   _msd_radix_sort(_UpperCamelCase ,   bit_position - 1				)

					# recombine lists
					_UpperCamelCase											=   zeros
					res.extend(_UpperCamelCase				)

					return res



def  lowercase__(		_UpperCamelCase  : list[int]				)->   Union[str, Any]:






					"""simple docstring"""

					_UpperCamelCase											=   len(_UpperCamelCase				)
					if not list_of_ints or length == 1:
										return

					if min(_UpperCamelCase				) < 0:
										raise ValueError("All numbers must be positive"				)

					_UpperCamelCase											=   max(len(bin(_UpperCamelCase				)[2:]				) for x in list_of_ints				)
					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   0 ,   _UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : list[int] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int				)->   Tuple:






					"""simple docstring"""

					if bit_position == 0 or end_index - begin_index <= 1:
										return

					bit_position -= 1

					_UpperCamelCase											=   begin_index
					_UpperCamelCase											=   end_index - 1
					while i <= j:
										_UpperCamelCase											=   False
										if not (list_of_ints[i] >> bit_position) & 1:
															# found zero at the beginning
															i += 1
															_UpperCamelCase											=   True
										if (list_of_ints[j] >> bit_position) & 1:
															# found one at the end
															j -= 1
															_UpperCamelCase											=   True

										if changed:
															continue

										_UpperCamelCase				,	_UpperCamelCase											=   list_of_ints[j], list_of_ints[i]
										j -= 1
										if j != i:
															i += 1

					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)
					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



