






'''simple docstring'''







import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


UpperCamelCase    :      int  			=		importlib.util.find_spec('s3fs') is not None

if _has_safs:
							from .safilesystem import SaFileSystem  # noqa: F401

UpperCamelCase    :      List[compression.BaseCompressedFileFileSystem]  			=		[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
							if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
														warnings.warn(F'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
							fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def 						A__			(				__lowerCAmelCase		:			str						):
						if "://" in dataset_path:
												lowerCamelCase__         =  dataset_path.split("""://"""						)[1]
						return dataset_path
def 						A__			(				__lowerCAmelCase		:			fsspec.AbstractFileSystem						):
						if fs is not None and fs.protocol != "file":
												return True
						else:
												return False
def 						A__			(				__lowerCAmelCase		:			fsspec.AbstractFileSystem   ,    __lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  not is_remote_filesystem(__lowerCAmelCase						)
						if is_local:
												# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
												shutil.move(fs._strip_protocol(__lowerCAmelCase						)   ,    fs._strip_protocol(__lowerCAmelCase						)						)
						else:
												fs.mv(__lowerCAmelCase   ,    __lowerCAmelCase   ,    recursive=__lowerCAmelCase						)





def 						A__			(				):
						if hasattr(fsspec.asyn   ,    """reset_lock"""						):
												# for future fsspec>2022.05.0
												fsspec.asyn.reset_lock()
						else:
												lowerCamelCase__         =  None
												lowerCamelCase__         =  None
												lowerCamelCase__         =  threading.Lock()









'''simple docstring'''







import numpy

# List of input, output pairs
UpperCamelCase    :      List[Any]  			=		(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCamelCase    :      Optional[int]  			=		(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
UpperCamelCase    :      int  			=		[2, 4, 1, 5]
UpperCamelCase    :      int  			=		len(train_data)
UpperCamelCase    :      Dict  			=		0.009
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			str="train"						):
						return calculate_hypothesis_value(__lowerCAmelCase   ,    __lowerCAmelCase						) - output(
						    __lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Any						):
						lowerCamelCase__         =  0
						for i in range(len(__lowerCAmelCase						) - 1						):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val
def 						A__			(				__lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			Tuple						):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Dict						):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]						)
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]						)
						return None
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Optional[Any]=m						):
						lowerCamelCase__         =  0
						for i in range(__lowerCAmelCase						):
												if index == -1:
																		summation_value += _error(__lowerCAmelCase						)
												else:
																		summation_value += _error(__lowerCAmelCase						) * train_data[i][0][index]
						return summation_value
def 						A__			(				__lowerCAmelCase		:			List[Any]						):
						lowerCamelCase__         =  summation_of_cost_derivative(__lowerCAmelCase   ,    __lowerCAmelCase						) / m
						return cost_derivative_value
def 						A__			(				):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						lowerCamelCase__         =  0.00_0002
						lowerCamelCase__         =  0
						lowerCamelCase__         =  0
						while True:
												j += 1
												lowerCamelCase__         =  [0, 0, 0, 0]
												for i in range(0   ,    len(__lowerCAmelCase						)						):
																		lowerCamelCase__         =  get_cost_derivative(i - 1						)
																		lowerCamelCase__         =  (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    __lowerCAmelCase   ,    __lowerCAmelCase   ,    atol=__lowerCAmelCase   ,    rtol=__lowerCAmelCase   ,    ):
																		break
												lowerCamelCase__         =  temp_parameter_vector
						print(("""Number of iterations:""", j)						)
def 						A__			(				):
						for i in range(len(__lowerCAmelCase						)						):
												print(("""Actual output value:""", output(__lowerCAmelCase   ,    """test"""						))						)
												print(("""Hypothesis output:""", calculate_hypothesis_value(__lowerCAmelCase   ,    """test"""						))						)


if __name__ == "__main__":
							run_gradient_descent()
							print('\nTesting gradient descent for a linear hypothesis function.\n')
							test_gradient_descent()

