
'''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 					__magic_name__(       UpperCAmelCase__ :       Tuple							,				UpperCAmelCase__ :       Dict							,				UpperCAmelCase__ :       Dict	):
						# Initialise PyTorch model
						UpperCamelCase_     :	List[Any]					     =     FNetConfig.from_json_file(UpperCAmelCase__	)
						print(f"""Building PyTorch model from configuration: {config}"""	)
						UpperCamelCase_     :	Union[str, Any]					     =     FNetForPreTraining(UpperCAmelCase__	)

						UpperCamelCase_     :	int					     =     restore_checkpoint(UpperCAmelCase__							,				UpperCAmelCase__	)
						UpperCamelCase_     :	Tuple					     =     checkpoint_dict["""target"""]

						# Embeddings
						# Position IDs
						UpperCamelCase_     :	int					     =     fnet_pretraining_model.state_dict()

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

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

												UpperCamelCase_     :	int					     =     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_     :	Tuple					     =     torch.tensor(
												    pretrained_model_params["""encoder"""][f"""feed_forward_{layer}"""]["""output"""]["""kernel"""]	).T
												UpperCamelCase_     :	Any					     =     torch.tensor(
												    pretrained_model_params["""encoder"""][f"""feed_forward_{layer}"""]["""output"""]["""bias"""]	)

												UpperCamelCase_     :	Optional[Any]					     =     torch.tensor(
												    pretrained_model_params["""encoder"""][f"""encoder_{layer}"""]["""output_layer_norm"""]["""scale"""]	)
												UpperCamelCase_     :	List[Any]					     =     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_     :	Union[str, Any]					     =     torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]	)

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

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






'''simple docstring'''


__SCREAMING_SNAKE_CASE						=      {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'
def 					__magic_name__(       UpperCAmelCase__ :       str	):
						UpperCamelCase_     :	str					     =     hex_string.strip().lower()
						if not hex_string:
												raise ValueError("""Empty string was passed to the function"""	)
						UpperCamelCase_     :	Dict					     =     hex_string[0] == """-"""
						if is_negative:
												UpperCamelCase_     :	List[str]					     =     hex_string[1:]
						if not all(char in hex_table for char in hex_string	):
												raise ValueError("""Non-hexadecimal value was passed to the function"""	)
						UpperCamelCase_     :	int					     =     0
						for char in hex_string:
												UpperCamelCase_     :	Any					     =     16 * decimal_number + hex_table[char]
						return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()




