





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      a    (   _lowercase :  int			,    _lowercase :  Union[str, Any]			,    _lowercase :  Optional[int]							)  ->List[str]:
		# Initialise PyTorch model
		UpperCamelCase__								=						FNetConfig.from_json_file(_lowercase							)
		print(F"""Building PyTorch model from configuration: {config}"""							)
		UpperCamelCase__								=						FNetForPreTraining(_lowercase							)

		UpperCamelCase__								=						restore_checkpoint(_lowercase			,    _lowercase							)
		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(_lowercase							)

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


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












from __future__ import annotations

def      a    (   _lowercase :  float | int			,    _lowercase :  float | int			,    _lowercase :  float | int			,    )  ->list[float | int]:
		if not all((nth_term, start_term_a, common_ratio_r)							):
				return []
		UpperCamelCase__								=						[]
		UpperCamelCase__								=						1
		UpperCamelCase__								=						common_ratio_r
		for _ in range(int(_lowercase							)							):
				if not series:
						series.append(_lowercase							)
				else:
						power += 1
						series.append(float(start_term_a * multiple							)							)
						UpperCamelCase__								=						pow(float(_lowercase							)			,    _lowercase							)
		return series


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__lowerCAmelCase 	=      float(input("Enter the last number (n term) of the Geometric Series"))
				__lowerCAmelCase 	=      float(input("Enter the starting term (a) of the Geometric Series"))
				__lowerCAmelCase 	=      float(
				    input("Enter the common ratio between two terms (r) of the Geometric Series")
				)
				print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
				print(geometric_series(nth_term, start_term_a, common_ratio_r))





