



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  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   str					     =					FNetConfig.from_json_file(SCREAMING_SNAKE_CASE__  )
						print(F"""Building PyTorch model from configuration: {config}"""  )
						lowerCAmelCase	:   Optional[int]					     =					FNetForPreTraining(SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Optional[Any]					     =					restore_checkpoint(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )
						lowerCAmelCase	:   Tuple					     =					checkpoint_dict["""target"""]

						# Embeddings
						# Position IDs
						lowerCAmelCase	:   Union[str, Any]					     =					fnet_pretraining_model.state_dict()

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

						# Encoder Layers
						for layer in range(config.num_hidden_layers  ):
												lowerCAmelCase	:   List[str]					     =					torch.tensor(
												    pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""mixing_layer_norm"""]["""scale"""]  )
												lowerCAmelCase	:   Dict					     =					torch.tensor(
												    pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""mixing_layer_norm"""]["""bias"""]  )

												lowerCAmelCase	:   Tuple					     =					torch.tensor(
												    pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""kernel"""]  ).T
												lowerCAmelCase	:   Dict					     =					torch.tensor(
												    pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""bias"""]  )

												lowerCAmelCase	:   Dict					     =					torch.tensor(
												    pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""kernel"""]  ).T
												lowerCAmelCase	:   str					     =					torch.tensor(
												    pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""bias"""]  )

												lowerCAmelCase	:   Optional[Any]					     =					torch.tensor(
												    pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""scale"""]  )
												lowerCAmelCase	:   Optional[Any]					     =					torch.tensor(
												    pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""bias"""]  )

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

						# Masked LM Layers
						lowerCAmelCase	:   Dict					     =					torch.tensor(
						    pretrained_model_params["""predictions_dense"""]["""kernel"""]  ).T
						lowerCAmelCase	:   Dict					     =					torch.tensor(
						    pretrained_model_params["""predictions_dense"""]["""bias"""]  )
						lowerCAmelCase	:   Union[str, Any]					     =					torch.tensor(
						    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]  )
						lowerCAmelCase	:   int					     =					torch.tensor(
						    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]  )
						lowerCAmelCase	:   Dict					     =					torch.tensor(
						    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]  )
						lowerCAmelCase	:   int					     =					torch.tensor(
						    pretrained_model_params["""predictions_output"""]["""output_bias"""]  )
						lowerCAmelCase	:   Union[str, Any]					     =					torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]  )

						# Seq Relationship Layers
						lowerCAmelCase	:   Tuple					     =					torch.tensor(
						    pretrained_model_params["""classification"""]["""output_kernel"""]  )
						lowerCAmelCase	:   List[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__":
		lowerCAmelCase					:				Optional[int]   	=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					:				Dict   	=parser.parse_args()
		convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)






import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCAmelCase					:				Union[str, Any]   	=2 << 20  # 2mb
lowerCAmelCase					:				Union[str, Any]   	=get_logger(__name__)






class 	_a			(      snake_case_  ):






			def 							_snake_case   (    self )	->					Dict:
									lowerCAmelCase	:   Optional[int]					     =					self._options.get_all_options().get("""runner""" )
									return runner in [None, "DirectRunner", "PortableRunner"]







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Union[str, Any]					     =					FileSystems
						if fs.exists(SCREAMING_SNAKE_CASE__  ):
												if force_upload:
																		logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""  )
												else:
																		logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""  )
																		return
						with fs.create(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""rb"""  ) as local_file:
																		lowerCAmelCase	:   Any					     =					local_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								remote_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   List[str]					     =					local_file.read(SCREAMING_SNAKE_CASE__  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					FileSystems
						if os.path.exists(SCREAMING_SNAKE_CASE__  ):
												if force_download:
																		logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""  )
												else:
																		logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""  )
																		return
						with fs.open(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""wb"""  ) as local_file:
																		lowerCAmelCase	:   Optional[int]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								local_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   Union[str, Any]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )

