




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 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


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

						_UpperCAmelCase													=					restore_checkpoint(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__						)
						_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(SCREAMING_SNAKE_CASE__						)

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


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










class    __lowerCAmelCase			:







			def __init__(	self   :      Dict   ,   snake_case__   :      List[str]=None				):




									"""simple docstring"""


									_UpperCAmelCase													=					data
									_UpperCAmelCase													=					None







			def __repr__(	self   :      Union[str, Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					""
									_UpperCAmelCase													=					self
									while temp:
															string_rep += F"""<{temp.data}> ---> """
															_UpperCAmelCase													=					temp.next
									string_rep += "<END>"
									return string_rep




def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						if not elements_list:
												raise Exception("The Elements List is empty"						)

						# Set first element as Head
						_UpperCAmelCase													=					Node(elements_list[0]						)
						_UpperCAmelCase													=					head
						# Loop through elements from position 1
						for data in elements_list[1:]:
												_UpperCAmelCase													=					Node(snake_case_						)
												_UpperCAmelCase													=					current.next
						return head


lowercase_     :    str      =			[1, 3, 5, 32, 44, 12, 43]
print(f"""List: {list_data}""")
print('Creating Linked List from List.')
lowercase_     :    Dict      =			make_linked_list(list_data)
print('Linked List:')





print(linked_list)




