






import numpy as np
import requests



def      UpperCAmelCase						( ):


		'''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(lowerCAmelCase__	)
		data.pop(0	)  # This is for removing the labels from the list
		__A    =							np.matrix(lowerCAmelCase__	)
		return dataset



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							len_data

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							10_0000
		__A    =							0.0_001_550

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

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

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

		return theta



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							collect_dataset()

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

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


if __name__ == "__main__":
					main()


import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
    import torch_xla.core.xla_model as xm  # noqa: F401

    _lowerCAmelCase        =       True
except ImportError:
    _lowerCAmelCase        =       False


# Cache this result has it's a C FFI call which can be pretty time-consuming
_lowerCAmelCase        =       torch.distributed.is_available()


def       a__      (					a							)		->	Optional[Any]:
       # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
       A_							:       str	      =				importlib.util.find_spec(a							) is not None
       if package_exists:
              try:
                     A_							:       str	      =				importlib.metadata.metadata(a							)
                     return True
              except importlib.metadata.PackageNotFoundError:
                     return False


def       a__      (					)		->	bool:
       return _torch_distributed_available


def       a__      (					)		->	int:
       try:
              pass
       except ImportError:
              print(
                  '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
                  ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
                  ''' Bindings for PyTorch*.'''							)
       return (
           importlib.util.find_spec('''torch_ccl'''							) is not None
           or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''							) is not None
       )


def       a__      (					)		->	List[str]:
       return importlib.metadata.version('''oneccl_bind_pt'''							)


def       a__      (					)		->	List[str]:
       return _is_package_available('''transformer_engine'''							)


@lru_cache
def       a__      (					a=True							)		->	Union[str, Any]:
       # Due to bugs on the amp series GPUs, we disable torch-xla on them
       if torch.cuda.is_available():
              return False
       if _tpu_available and check_device:
              try:
                     # Will raise a RuntimeError if no XLA configuration is found
                     A_							:       Tuple	      =				xm.xla_device()
                     return True
              except RuntimeError:
                     return False
       return _tpu_available


def       a__      (					)		->	Dict:
       return _is_package_available('''deepspeed'''							)


def       a__      (					a=False							)		->	Optional[int]:
       if is_tpu_available():
              return not ignore_tpu
       if torch.cuda.is_available():
              return torch.cuda.is_bfaa_supported()
       if is_npu_available():
              return False
       return True


def       a__      (					)		->	Optional[Any]:
       A_							:       Optional[int]	      =				_is_package_available('''bitsandbytes'''							)
       if package_exists:
              A_							:       Any	      =				version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
              return compare_versions(a  ,   '''>='''  ,   '''0.39.0'''							)
       return False


def       a__      (					)		->	Any:
       A_							:       List[str]	      =				_is_package_available('''bitsandbytes'''							)
       if package_exists:
              A_							:       Tuple	      =				version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
              return compare_versions(a  ,   '''>='''  ,   '''0.37.2'''							)
       return False


def       a__      (					)		->	List[str]:
       return _is_package_available('''bitsandbytes'''							)


def       a__      (					)		->	List[str]:
       if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''  ,   '''False'''							)							) == 1:
              A_							:       str	      =				importlib.util.find_spec('''megatron'''							) is not None
              if package_exists:
                     try:
                            A_							:       Dict	      =				parse(importlib.metadata.version('''megatron-lm'''							)							)
                            return compare_versions(a  ,   '''>='''  ,   '''2.2.0'''							)
                     except Exception as e:
                            warnings.warn(f"""Parse Megatron version failed. Exception:{e}"""							)
                            return False


def       a__      (					)		->	Tuple:
       return _is_package_available('''safetensors'''							)


def       a__      (					)		->	Tuple:
       return _is_package_available('''transformers'''							)


def       a__      (					)		->	int:
       return _is_package_available('''datasets'''							)


def       a__      (					)		->	Optional[int]:
       return _is_package_available('''aim'''							)


def       a__      (					)		->	Optional[Any]:
       return _is_package_available('''tensorboard'''							) or _is_package_available('''tensorboardX'''							)


def       a__      (					)		->	Optional[int]:
       return _is_package_available('''wandb'''							)


def       a__      (					)		->	Tuple:
       return _is_package_available('''comet_ml'''							)


def       a__      (					)		->	Tuple:
       return _is_package_available('''boto3'''							)


def       a__      (					)		->	Tuple:
       if _is_package_available('''rich'''							):
              if "ACCELERATE_DISABLE_RICH" in os.environ:
                     warnings.warn(
                         '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''							)
                     return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''  ,   a							)
              return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''  ,   a							)
       return False


def       a__      (					)		->	Any:
       return _is_package_available('''sagemaker'''							)


def       a__      (					)		->	Union[str, Any]:
       return _is_package_available('''tqdm'''							)


def       a__      (					)		->	List[str]:
       return _is_package_available('''mlflow'''							)


def       a__      (					)		->	Tuple:
       return is_torch_version('''>='''  ,   '''1.12'''							) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def       a__      (					)		->	Tuple:
       def get_major_and_minor_from_version(a							):
              return str(version.parse(a							).major							) + "." + str(version.parse(a							).minor							)

       A_							:       Tuple	      =				importlib.metadata.version('''torch'''							)
       if importlib.util.find_spec('''intel_extension_for_pytorch'''							) is None:
              return False
       A_							:       Optional[int]	      =				'''N/A'''
       try:
              A_							:       List[Any]	      =				importlib.metadata.version('''intel_extension_for_pytorch'''							)
       except importlib.metadata.PackageNotFoundError:
              return False
       A_							:       Tuple	      =				get_major_and_minor_from_version(_torch_version							)
       A_							:       int	      =				get_major_and_minor_from_version(_ipex_version							)
       if torch_major_and_minor != ipex_major_and_minor:
              warnings.warn(
                  f"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
                  f""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""							)
              return False
       return True


@lru_cache
def       a__      (					a=False							)		->	Optional[int]:
       if importlib.util.find_spec('''torch'''							) is None or importlib.util.find_spec('''torch_npu'''							) is None:
              return False

       import torch
       import torch_npu  # noqa: F401

       if check_device:
              try:
                     # Will raise a RuntimeError if no NPU is found
                     A_							:       Dict	      =				torch.npu.device_count()
                     return torch.npu.is_available()
              except RuntimeError:
                     return False
       return hasattr(a  ,   '''npu'''							) and torch.npu.is_available()


@lru_cache
def       a__      (					a=False							)		->	Optional[Any]:
       if not parse_flag_from_env('''ACCELERATE_USE_XPU'''  ,   default=a							):
              return False
       if is_ipex_available():
              import torch

              if is_torch_version('''<='''  ,   '''1.12'''							):
                     return False
       else:
              return False

       import intel_extension_for_pytorch  # noqa: F401

       if check_device:
              try:
                     # Will raise a RuntimeError if no XPU  is found
                     A_							:       Tuple	      =				torch.xpu.device_count()
                     return torch.xpu.is_available()
              except RuntimeError:
                     return False
       return hasattr(a  ,   '''xpu'''							) and torch.xpu.is_available()


