



'''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 A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							):
						# Initialise PyTorch model
						_UpperCamelCase      :	str				       =     FNetConfig.from_json_file(UpperCAmelCase_							)
						print(f'Building PyTorch model from configuration: {config}'							)
						_UpperCamelCase      :	Tuple				       =     FNetForPreTraining(UpperCAmelCase_							)

						_UpperCamelCase      :	Optional[int]				       =     restore_checkpoint(UpperCAmelCase_						,						UpperCAmelCase_							)
						_UpperCamelCase      :	Union[str, Any]				       =     checkpoint_dict['target']

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

						_UpperCamelCase      :	Any				       =     state_dict['fnet.embeddings.position_ids']
						_UpperCamelCase      :	Union[str, Any]				       =     {'fnet.embeddings.position_ids': position_ids}
						# Embedding Layers
						_UpperCamelCase      :	str				       =     torch.tensor(
						    pretrained_model_params['encoder']['embedder']['word']['embedding']							)
						_UpperCamelCase      :	str				       =     torch.tensor(
						    pretrained_model_params['encoder']['embedder']['position']['embedding'][0]							)
						_UpperCamelCase      :	int				       =     torch.tensor(
						    pretrained_model_params['encoder']['embedder']['type']['embedding']							)
						_UpperCamelCase      :	Any				       =     torch.tensor(
						    pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']							).T
						_UpperCamelCase      :	Union[str, Any]				       =     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      :	Optional[int]				       =     torch.tensor(
						    pretrained_model_params['encoder']['embedder']['layer_norm']['bias']							)

						# Encoder Layers
						for layer in range(config.num_hidden_layers							):
												_UpperCamelCase      :	Union[str, Any]				       =     torch.tensor(
												    pretrained_model_params['encoder'][f'encoder_{layer}']['mixing_layer_norm']['scale']							)
												_UpperCamelCase      :	Tuple				       =     torch.tensor(
												    pretrained_model_params['encoder'][f'encoder_{layer}']['mixing_layer_norm']['bias']							)

												_UpperCamelCase      :	Tuple				       =     torch.tensor(
												    pretrained_model_params['encoder'][f'feed_forward_{layer}']['intermediate']['kernel']							).T
												_UpperCamelCase      :	List[str]				       =     torch.tensor(
												    pretrained_model_params['encoder'][f'feed_forward_{layer}']['intermediate']['bias']							)

												_UpperCamelCase      :	int				       =     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      :	List[str]				       =     torch.tensor(
												    pretrained_model_params['encoder'][f'encoder_{layer}']['output_layer_norm']['scale']							)
												_UpperCamelCase      :	Optional[int]				       =     torch.tensor(
												    pretrained_model_params['encoder'][f'encoder_{layer}']['output_layer_norm']['bias']							)

						# Pooler Layers
						_UpperCamelCase      :	Union[str, Any]				       =     torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']							).T
						_UpperCamelCase      :	int				       =     torch.tensor(pretrained_model_params['encoder']['pooler']['bias']							)

						# Masked LM Layers
						_UpperCamelCase      :	Union[str, Any]				       =     torch.tensor(
						    pretrained_model_params['predictions_dense']['kernel']							).T
						_UpperCamelCase      :	Union[str, Any]				       =     torch.tensor(
						    pretrained_model_params['predictions_dense']['bias']							)
						_UpperCamelCase      :	Tuple				       =     torch.tensor(
						    pretrained_model_params['predictions_layer_norm']['scale']							)
						_UpperCamelCase      :	str				       =     torch.tensor(
						    pretrained_model_params['predictions_layer_norm']['bias']							)
						_UpperCamelCase      :	Optional[int]				       =     torch.tensor(
						    pretrained_model_params['encoder']['embedder']['word']['embedding']							)
						_UpperCamelCase      :	int				       =     torch.tensor(
						    pretrained_model_params['predictions_output']['output_bias']							)
						_UpperCamelCase      :	Dict				       =     torch.tensor(pretrained_model_params['predictions_output']['output_bias']							)

						# Seq Relationship Layers
						_UpperCamelCase      :	List[str]				       =     torch.tensor(
						    pretrained_model_params['classification']['output_kernel']							)
						_UpperCamelCase      :	Any				       =     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__":
					snake_case_				:			Dict						 =						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.')
					snake_case_				:			Dict						 =						parser.parse_args()
					convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)







'''simple docstring'''
import math






def A__					(  UpperCAmelCase_							):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5						,						int(math.sqrt(UpperCAmelCase_							) + 1							)						,						6							):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True






def A__					(  UpperCAmelCase_ = 1_0_0_0						,						UpperCAmelCase_ = 1_0_0_0							):
						_UpperCamelCase      :	str				       =     [0, 0, 0]  # length, a, b
						for a in range((a_limit * -1) + 1						,						UpperCAmelCase_							):
												for b in range(2						,						UpperCAmelCase_							):
																		if is_prime(UpperCAmelCase_							):
																								_UpperCamelCase      :	List[str]				       =     0
																								_UpperCamelCase      :	Union[str, Any]				       =     0
																								while is_prime((n**2) + (a * n) + b							):
																														count += 1
																														n += 1
																								if count > longest[0]:
																														_UpperCamelCase      :	Any				       =     [count, a, b]
						_UpperCamelCase      :	Dict				       =     longest[1] * longest[2]
						return ans


if __name__ == "__main__":
					print(solution(1000, 1000))


