


import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__snake_case     =		get_logger(__name__)






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase = None			,       )			->				Optional[Any]:


     """simple docstring"""


     for i, state in enumerate(_lowercase				):
          __UpperCamelCase				=	f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
          __UpperCamelCase				=	os.path.join(_lowercase			,       _lowercase				)
          save(_lowercase			,       _lowercase				)
          logger.info(f'''Model weights saved in {output_model_file}'''				)
     # Optimizer states
     for i, opt in enumerate(_lowercase				):
          __UpperCamelCase				=	opt.state_dict()
          __UpperCamelCase				=	f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
          __UpperCamelCase				=	os.path.join(_lowercase			,       _lowercase				)
          save(_lowercase			,       _lowercase				)
          logger.info(f'''Optimizer state saved in {output_optimizer_file}'''				)
     # Scheduler states
     for i, scheduler in enumerate(_lowercase				):
          __UpperCamelCase				=	scheduler.state_dict()
          __UpperCamelCase				=	f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
          __UpperCamelCase				=	os.path.join(_lowercase			,       _lowercase				)
          save(_lowercase			,       _lowercase				)
          logger.info(f'''Scheduler state saved in {output_scheduler_file}'''				)
     # GradScaler state
     if scaler is not None:
          __UpperCamelCase				=	scaler.state_dict()
          __UpperCamelCase				=	os.path.join(_lowercase			,       _lowercase				)
          torch.save(_lowercase			,       _lowercase				)
          logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''				)
     # Random number generator states
     __UpperCamelCase				=	{}
     __UpperCamelCase				=	f'''{RNG_STATE_NAME}_{process_index}.pkl'''
     __UpperCamelCase				=	random.getstate()
     __UpperCamelCase				=	np.random.get_state()
     __UpperCamelCase				=	torch.get_rng_state()
     if is_xpu_available():
          __UpperCamelCase				=	torch.xpu.get_rng_state_all()
     else:
          __UpperCamelCase				=	torch.cuda.get_rng_state_all()
     if is_tpu_available():
          __UpperCamelCase				=	xm.get_rng_state()
     __UpperCamelCase				=	os.path.join(_lowercase			,       _lowercase				)
     torch.save(_lowercase			,       _lowercase				)
     logger.info(f'''Random states saved in {output_states_file}'''				)
     return output_dir






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       **_lowercase			,       )			->				Dict:


     """simple docstring"""


     if map_location not in [None, "cpu", "on_device"]:
          raise TypeError(
              'Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'				)
     if map_location is None:
          __UpperCamelCase				=	'cpu'
     elif map_location == "on_device":
          __UpperCamelCase				=	PartialState().device
     # Model states
     for i, model in enumerate(_lowercase				):
          __UpperCamelCase				=	f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
          __UpperCamelCase				=	os.path.join(_lowercase			,       _lowercase				)
          models[i].load_state_dict(torch.load(_lowercase			,       map_location=_lowercase				)			,       **_lowercase				)
     logger.info('All model weights loaded successfully'				)

     # Optimizer states
     for i, opt in enumerate(_lowercase				):
          __UpperCamelCase				=	f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
          __UpperCamelCase				=	os.path.join(_lowercase			,       _lowercase				)
          __UpperCamelCase				=	torch.load(_lowercase			,       map_location=_lowercase				)
          optimizers[i].load_state_dict(_lowercase				)
     logger.info('All optimizer states loaded successfully'				)

     # Scheduler states
     for i, scheduler in enumerate(_lowercase				):
          __UpperCamelCase				=	f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
          __UpperCamelCase				=	os.path.join(_lowercase			,       _lowercase				)
          scheduler.load_state_dict(torch.load(_lowercase				)				)
     logger.info('All scheduler states loaded successfully'				)

     # GradScaler state
     if scaler is not None:
          __UpperCamelCase				=	os.path.join(_lowercase			,       _lowercase				)
          scaler.load_state_dict(torch.load(_lowercase				)				)
          logger.info('GradScaler state loaded successfully'				)

     # Random states
     try:
          __UpperCamelCase				=	torch.load(os.path.join(_lowercase			,       f'''{RNG_STATE_NAME}_{process_index}.pkl'''				)				)
          random.setstate(states['random_state']				)
          np.random.set_state(states['numpy_random_seed']				)
          torch.set_rng_state(states['torch_manual_seed']				)
          if is_xpu_available():
               torch.xpu.set_rng_state_all(states['torch_xpu_manual_seed']				)
          else:
               torch.cuda.set_rng_state_all(states['torch_cuda_manual_seed']				)
          if is_tpu_available():
               xm.set_rng_state(states['xm_seed']				)
          logger.info('All random states loaded successfully'				)
     except Exception:
          logger.info('Could not load random states'				)






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase = 0				)			->				Any:


     """simple docstring"""


     __UpperCamelCase				=	Path(_lowercase				) / f'''custom_checkpoint_{index}.pkl'''
     logger.info(f'''Saving the state of {get_pretty_name(_lowercase				)} to {save_location}'''				)
     torch.save(obj.state_dict()			,       _lowercase				)






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase = 0				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	f'''{path}/custom_checkpoint_{index}.pkl'''
     logger.info(f'''Loading the state of {get_pretty_name(_lowercase				)} from {load_location}'''				)
     obj.load_state_dict(torch.load(_lowercase			,       map_location='cpu'				)				)






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)  # Number of training samples
     __UpperCamelCase				=	np.eye(_lowercase				)  # Initialize weights as identity matrix
     for j in range(_lowercase				):
          __UpperCamelCase				=	point - x_train[j]
          __UpperCamelCase				=	np.exp(diff @ diff.T / (-2.0 * tau**2)				)

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

     # pairing elements of one and x_data
     __UpperCamelCase				=	np.column_stack((one, x_data)				)

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       color='blue'				)
     plt.plot(
         x_train_sorted[:, 1]			,       preds[x_train[:, 1].argsort(0				)]			,       color='yellow'			,       linewidth=5			,       )
     plt.title('Local Weighted Regression'				)
     plt.xlabel(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


