


'''simple docstring'''
import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


lowerCAmelCase  =				logging.get_logger(__name__)

if is_tf_available():
      import tensorflow as tf






@dataclass
class 					lowerCAmelCase_     (    UpperCAmelCase  ):
       __UpperCAmelCase				='tf'
       __UpperCAmelCase				=field(
           default=UpperCAmelCase			,       metadata={'help': 'Name of TPU'}			,       )

       __UpperCAmelCase				=field(
           default=UpperCAmelCase			,       metadata={'help': 'Zone of TPU'}			,       )

       __UpperCAmelCase				=field(
           default=UpperCAmelCase			,       metadata={'help': 'Name of Cloud TPU-enabled project'}			,       )

       __UpperCAmelCase				=field(
           default=1.0			,       metadata={'help': 'Power for the Polynomial decay LR scheduler.'}			,       )

       __UpperCAmelCase				=field(default=UpperCAmelCase			,       metadata={'help': 'Whether to activate the XLA compilation or not'}  )






       @cached_property
       def      UpperCamelCase	(   self      )->			Tuple["tf.distribute.Strategy", int]:
            requires_backends(self ,    ['tf']      )
            logger.info('Tensorflow: setting up strategy'      )

            _A				=       tf.config.list_physical_devices('GPU'      )

            # Set to float16 at first
            if self.fpaa:
                 tf.keras.mixed_precision.set_global_policy('mixed_float16'      )

            if self.no_cuda:
                 _A				=       tf.distribute.OneDeviceStrategy(device='/cpu:0'      )
            else:
                 try:
                      if self.tpu_name:
                           _A				=       tf.distribute.cluster_resolver.TPUClusterResolver(
                               self.tpu_name ,    zone=self.tpu_zone ,    project=self.gcp_project      )
                      else:
                           _A				=       tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                      if self.tpu_name:
                           raise RuntimeError(F'Couldn\'t connect to TPU {self.tpu_name}!'      )
                      else:
                           _A				=       None

                 if tpu:
                      # Set to bfloat16 in case of TPU
                      if self.fpaa:
                           tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'      )

                      tf.config.experimental_connect_to_cluster(_UpperCamelCase      )
                      tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase      )

                      _A				=       tf.distribute.TPUStrategy(_UpperCamelCase      )

                 elif len(_UpperCamelCase      ) == 0:
                      _A				=       tf.distribute.OneDeviceStrategy(device='/cpu:0'      )
                 elif len(_UpperCamelCase      ) == 1:
                      _A				=       tf.distribute.OneDeviceStrategy(device='/gpu:0'      )
                 elif len(_UpperCamelCase      ) > 1:
                      # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                      _A				=       tf.distribute.MirroredStrategy()
                 else:
                      raise ValueError('Cannot find the proper strategy, please check your environment properties.'      )

            return strategy






       @property
       def      UpperCamelCase	(   self      )->			"tf.distribute.Strategy":
            requires_backends(self ,    ['tf']      )
            return self._setup_strategy






       @property
       def      UpperCamelCase	(   self      )->			int:
            requires_backends(self ,    ['tf']      )
            return self._setup_strategy.num_replicas_in_sync






       @property
       def      UpperCamelCase	(   self      )->			List[str]:
            return False  # TF Logging is handled by Keras not the Trainer






       @property
       def      UpperCamelCase	(   self      )->			int:
            if self.per_gpu_train_batch_size:
                 logger.warning(
                     'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
                     'version. Using `--per_device_train_batch_size` is preferred.'      )
            _A				=       self.per_gpu_train_batch_size or self.per_device_train_batch_size
            return per_device_batch_size * self.n_replicas






       @property
       def      UpperCamelCase	(   self      )->			int:
            if self.per_gpu_eval_batch_size:
                 logger.warning(
                     'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
                     'version. Using `--per_device_eval_batch_size` is preferred.'      )
            _A				=       self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
            return per_device_batch_size * self.n_replicas






       @property
       def      UpperCamelCase	(   self      )->			int:
            requires_backends(self ,    ['tf']      )
            warnings.warn(
                'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.' ,    _UpperCamelCase ,    )
            return self._setup_strategy.num_replicas_in_sync









'''simple docstring'''
import numpy as np
import requests

def 				lowerCamelCase_   (   )				->  int:







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

def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[str, Any]       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					int       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Dict		)				->  int:







     """simple docstring"""
     _A				=       len_data

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

def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[int]       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					Optional[int]		)				->  Dict:







     """simple docstring"""
     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.sum(np.square(__UpperCamelCase		)		)
     _A				=       sum_elem / (2 * len_data)
     return error

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]		)				->  int:







     """simple docstring"""
     _A				=       1_0_0_0_0_0
     _A				=       0.0_00_15_50

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

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

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

     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					Tuple		)				->  Union[str, Any]:







     """simple docstring"""
     _A				=       sum(abs(y - predicted_y[i]		) for i, y in enumerate(__UpperCamelCase		)		)
     return total / len(__UpperCamelCase		)

def 				lowerCamelCase_   (   )				->  Optional[Any]:







     """simple docstring"""
     _A				=       collect_dataset()

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

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


if __name__ == "__main__":
    main()





