





import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


__A       : Optional[Any]						    = 2 << 20  # 2mb
__A       : int						    = get_logger(__name__)







class     lowerCamelCase(	lowercase_  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         _A    =       self._options.get_all_options().get('runner'       )
         return runner in [None, "DirectRunner", "PortableRunner"]





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=False							)       ->   Any:




    """simple docstring"""




    _A    =       FileSystems
    if fs.exists(_SCREAMING_SNAKE_CASE							):
        if force_upload:
            logger.info(F"Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."							)
        else:
            logger.info(F"Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."							)
            return
    with fs.create(_SCREAMING_SNAKE_CASE							) as remote_file:
        with open(_SCREAMING_SNAKE_CASE       ,    'rb'							) as local_file:
            _A    =       local_file.read(_SCREAMING_SNAKE_CASE							)
            while chunk:
                remote_file.write(_SCREAMING_SNAKE_CASE							)
                _A    =       local_file.read(_SCREAMING_SNAKE_CASE							)





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=False							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       FileSystems
    if os.path.exists(_SCREAMING_SNAKE_CASE							):
        if force_download:
            logger.info(F"Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."							)
        else:
            logger.info(F"Local path already exist: {remote_file_path}. Skipping it as force_upload=False."							)
            return
    with fs.open(_SCREAMING_SNAKE_CASE							) as remote_file:
        with open(_SCREAMING_SNAKE_CASE       ,    'wb'							) as local_file:
            _A    =       remote_file.read(_SCREAMING_SNAKE_CASE							)
            while chunk:
                local_file.write(_SCREAMING_SNAKE_CASE							)
                _A    =       remote_file.read(_SCREAMING_SNAKE_CASE							)












import numpy as np





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       abs(predict - actual							)
    _A    =       difference.mean()

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)

    _A    =       square_diff.mean()
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()
    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       np.log(predict + 1							)
    _A    =       np.log(actual + 1							)

    _A    =       log_predict - log_actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()

    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    # print(numerator, denumerator)
    _A    =       float(_SCREAMING_SNAKE_CASE							) / denumerator * 100

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Dict:




    """simple docstring"""




    return np.mean(np.array(_SCREAMING_SNAKE_CASE							) == np.array(_SCREAMING_SNAKE_CASE							)							)





