





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__							(				UpperCamelCase__:							Union[str, Any]    ,      UpperCamelCase__:							str    ,      UpperCamelCase__:							List[str]			):
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE__								=					FNetConfig.from_json_file(UpperCamelCase__			)
		print(f'''Building PyTorch model from configuration: {config}'''			)
		SCREAMING_SNAKE_CASE__								=					FNetForPreTraining(UpperCamelCase__			)

		SCREAMING_SNAKE_CASE__								=					restore_checkpoint(UpperCamelCase__    ,      UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					checkpoint_dict["""target"""]

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

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

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

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

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

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

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

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

		# Seq Relationship Layers
		SCREAMING_SNAKE_CASE__								=					torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		SCREAMING_SNAKE_CASE__								=					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__":
	_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

from collections import deque
from queue import Queue
from timeit import timeit

_lowerCamelCase       =	{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					Queue()
		queue.put(UpperCamelCase__			)
		while not queue.empty():
				SCREAMING_SNAKE_CASE__								=					queue.get()
				for w in graph[v]:
						if w not in explored:
								explored.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.put(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					deque([start]			)
		while queue:
				SCREAMING_SNAKE_CASE__								=					queue.popleft()
				for child in graph[v]:
						if child not in visited:
								visited.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.append(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					f'''from __main__ import G, {name}'''
		SCREAMING_SNAKE_CASE__								=					10_000
		SCREAMING_SNAKE_CASE__								=					timeit(f'''{name}(G, \'A\')'''    ,      setup=UpperCamelCase__    ,      number=UpperCamelCase__			)
		print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	benchmark_function('breadth_first_search')
	benchmark_function('breadth_first_search_with_deque')
	# breadth_first_search                finished 10000 runs in 0.20999 seconds
	# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds