






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 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    List[Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  FNetConfig.from_json_file(UpperCamelCase_						)
						print(F"Building PyTorch model from configuration: {config}"						)
						lowerCAmelCase__			  =  FNetForPreTraining(UpperCamelCase_						)

						lowerCAmelCase__			  =  restore_checkpoint(UpperCamelCase_			,				UpperCamelCase_						)
						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(UpperCamelCase_						)

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


if __name__ == "__main__":
					__snake_case			:			Tuple			   =   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			:			Optional[int]			   =   parser.parse_args()
					convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)













import numpy as np
import requests







def 		_UpperCamelCase      (			)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  requests.get(
						    'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
						    'master/Week1/ADRvsRating.csv'						)
						lowerCAmelCase__			  =  response.text.splitlines()
						lowerCAmelCase__			  =  []
						for item in lines:
												lowerCAmelCase__			  =  item.split(','						)
												data.append(UpperCamelCase_						)
						data.pop(0						)  # This is for removing the labels from the list
						lowerCAmelCase__			  =  np.matrix(UpperCamelCase_						)
						return dataset







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len_data

						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta - (alpha / n) * sum_grad
						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str			,				UpperCamelCase_				:    List[Any]						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.sum(np.square(UpperCamelCase_						)						)
						lowerCAmelCase__			  =  sum_elem / (2 * len_data)
						return error







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    List[Any]						)     ->      str:

						"""simple docstring"""



						lowerCAmelCase__			  =  10_0000
						lowerCAmelCase__			  =  0.000_1550

						lowerCAmelCase__			  =  data_x.shape[1]
						lowerCAmelCase__			  =  data_x.shape[0] - 1

						lowerCAmelCase__			  =  np.zeros((1, no_features)						)

						for i in range(0			,				UpperCamelCase_						):
												lowerCAmelCase__			  =  run_steep_gradient_descent(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												lowerCAmelCase__			  =  sum_of_square_error(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												print(F"At Iteration {i + 1} - Error is {error:.5f}"						)

						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Any						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  sum(abs(y - predicted_y[i]						) for i, y in enumerate(UpperCamelCase_						)						)
						return total / len(UpperCamelCase_						)







def 		_UpperCamelCase      (			)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  collect_dataset()

						lowerCAmelCase__			  =  data.shape[0]
						lowerCAmelCase__			  =  np.c_[np.ones(UpperCamelCase_						), data[:, :-1]].astype(UpperCamelCase_						)
						lowerCAmelCase__			  =  data[:, -1].astype(UpperCamelCase_						)

						lowerCAmelCase__			  =  run_linear_regression(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta.shape[1]
						print('Resultant Feature vector : '						)
						for i in range(0			,				UpperCamelCase_						):
												print(F"{theta[0, i]:.5f}"						)


if __name__ == "__main__":
					main()





