



'''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       (				lowerCamelCase :    Optional[Any]					,		lowerCamelCase :    List[Any]					,		lowerCamelCase :    Any       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	FNetConfig.from_json_file(lowerCamelCase       )
	print(f'''Building PyTorch model from configuration: {config}'''       )
	__lowerCAmelCase       				=	FNetForPreTraining(lowerCamelCase       )

	__lowerCAmelCase       				=	restore_checkpoint(lowerCamelCase					,		lowerCamelCase       )
	__lowerCAmelCase       				=	checkpoint_dict["target"]

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

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

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

		__lowerCAmelCase       				=	torch.tensor(
		    pretrained_model_params["encoder"][f'''feed_forward_{layer}''']["intermediate"]["kernel"]       ).T
		__lowerCAmelCase       				=	torch.tensor(
		    pretrained_model_params["encoder"][f'''feed_forward_{layer}''']["intermediate"]["bias"]       )

		__lowerCAmelCase       				=	torch.tensor(
		    pretrained_model_params["encoder"][f'''feed_forward_{layer}''']["output"]["kernel"]       ).T
		__lowerCAmelCase       				=	torch.tensor(
		    pretrained_model_params["encoder"][f'''feed_forward_{layer}''']["output"]["bias"]       )

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

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

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

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

	# Load State Dict
	fnet_pretraining_model.load_state_dict(lowerCamelCase       )

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


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




'''simple docstring'''



def 	__lowerCAmelCase       (				lowerCamelCase :    float					,		lowerCamelCase :    float       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	float()
	try:
		__lowerCAmelCase       				=	mass * acceleration
	except Exception:
		return -0.0
	return force


if __name__ == "__main__":
	import doctest

	# run doctest
	doctest.testmod()

	# demo
	lowerCAmelCase						:		List[Any]						      = 12.5
	lowerCAmelCase						:		Optional[Any]						      = 1_0
	lowerCAmelCase						:		str						      = newtons_second_law_of_motion(mass, acceleration)
	print('''The force is ''', force, '''N''')