






"""simple docstring"""
import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard

def        snake_case   (     A__		):
			assert "mock" in _fsspec_registry
			assert "bz2" in _fsspec_registry

def        snake_case   (     ):
			assert "mock" not in _fsspec_registry
			assert "bz2" in _fsspec_registry

def        snake_case   (     ):
			UpperCAmelCase_  :				Dict      =      "mock-s3-bucket"
			UpperCAmelCase_  :				Optional[int]      =      F"""s3://{mock_bucket}"""
			UpperCAmelCase_  :				Tuple      =      extract_path_from_uri(lowerCamelCase__		)
			assert dataset_path.startswith("s3://"		) is False

			UpperCAmelCase_  :				Tuple      =      "./local/path"
			UpperCAmelCase_  :				List[Any]      =      extract_path_from_uri(lowerCamelCase__		)
			assert dataset_path == new_dataset_path

def        snake_case   (     A__		):
			UpperCAmelCase_  :				List[str]      =      is_remote_filesystem(lowerCamelCase__		)
			assert is_remote is True

			UpperCAmelCase_  :				int      =      fsspec.filesystem("file"		)

			UpperCAmelCase_  :				str      =      is_remote_filesystem(lowerCamelCase__		)
			assert is_remote is False

@pytest.mark.parametrize("compression_fs_class"			,lowerCamelCase__		)
def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				Any      =      {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
			UpperCAmelCase_  :				Optional[int]      =      input_paths[compression_fs_class.protocol]
			if input_path is None:
						UpperCAmelCase_  :				str      =      F"""for '{compression_fs_class.protocol}' compression protocol, """
						if compression_fs_class.protocol == "lz4":
									reason += require_lza.kwargs["reason"]
						elif compression_fs_class.protocol == "zstd":
									reason += require_zstandard.kwargs["reason"]
						pytest.skip(lowerCamelCase__		)
			UpperCAmelCase_  :				str      =      fsspec.filesystem(compression_fs_class.protocol			,fo=lowerCamelCase__		)
			assert isinstance(lowerCamelCase__			,lowerCamelCase__		)
			UpperCAmelCase_  :				Optional[Any]      =      os.path.basename(lowerCamelCase__		)
			UpperCAmelCase_  :				Union[str, Any]      =      expected_filename[: expected_filename.rindex("."		)]
			assert fs.glob("*"		) == [expected_filename]
			with fs.open(lowerCamelCase__			,"r"			,encoding="utf-8"		) as f, open(lowerCamelCase__			,encoding="utf-8"		) as expected_file:
						assert f.read() == expected_file.read()

@pytest.mark.parametrize("protocol"			,["zip", "gzip"]		)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[Any]      =      {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
			UpperCAmelCase_  :				int      =      compressed_file_paths[protocol]
			UpperCAmelCase_  :				List[Any]      =      "dataset.jsonl"
			UpperCAmelCase_  :				str      =      F"""{protocol}://{member_file_path}::{compressed_file_path}"""
			UpperCAmelCase_  :				Any      =      fsspec.get_fs_token_paths(lowerCamelCase__		)
			assert fs.isfile(lowerCamelCase__		)
			assert not fs.isfile("non_existing_" + member_file_path		)

@pytest.mark.integration
def        snake_case   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				Dict      =      hf_api.dataset_info(lowerCamelCase__			,token=lowerCamelCase__		)
			UpperCAmelCase_  :				int      =      HfFileSystem(repo_info=lowerCamelCase__			,token=lowerCamelCase__		)
			assert sorted(hffs.glob("*"		)		) == [".gitattributes", "data"]
			assert hffs.isdir("data"		)
			assert hffs.isfile(".gitattributes"		) and hffs.isfile("data/text_data.txt"		)
			with open(lowerCamelCase__		) as f:
						assert hffs.open("data/text_data.txt"			,"r"		).read() == f.read()




def        snake_case   (     ):
			UpperCAmelCase_  :				List[Any]      =      "bz2"

			# Import module
			import datasets.filesystems

			# Overwrite protocol and reload
			register_implementation(lowerCamelCase__			,lowerCamelCase__			,clobber=lowerCamelCase__		)
			with pytest.warns(lowerCamelCase__		) as warning_info:
						importlib.reload(datasets.filesystems		)

			assert len(lowerCamelCase__		) == 1
			assert (
			    str(warning_info[0].message		)
			    == F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
			)











"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize

def        snake_case   (     A__		):
			return np.dot(A__			,A__		)







class     UpperCamelCase_ :





	def __init__(   self      :				int			,			*,
	    lowerCAmelCase_      :				float = np.inf			,			lowerCAmelCase_      :				str = "linear"			,			lowerCAmelCase_      :				float = 0.0			,			)		->    None:
				UpperCAmelCase_  :				List[str]      =      regularization
				UpperCAmelCase_  :				Tuple      =      gamma
				if kernel == "linear":
							UpperCAmelCase_  :				List[Any]      =      self.__linear
				elif kernel == "rbf":
							if self.gamma == 0:
										raise ValueError("rbf kernel requires gamma"		)
							if not isinstance(self.gamma			,			(float, int)		):
										raise ValueError("gamma must be float or int"		)
							if not self.gamma > 0:
										raise ValueError("gamma must be > 0"		)
							UpperCAmelCase_  :				Union[str, Any]      =      self.__rbf
							# in the future, there could be a default value like in sklearn
							# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
							# previously it was 1/(n_features)
				else:
							UpperCAmelCase_  :				Tuple      =      f"""Unknown kernel: {kernel}"""
							raise ValueError(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.dot(lowerCAmelCase_			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				list[ndarray]			,			lowerCAmelCase_      :				ndarray		)		->    None:
				UpperCAmelCase_  :				int      =      observations
				UpperCAmelCase_  :				Optional[Any]      =      classes

				# using Wolfe's Dual to calculate w.
				# Primal problem: minimize 1/2*norm_squared(w)
				#   constraint: yn(w . xn + b) >= 1
				#
				# With l a vector
				# Dual problem: maximize sum_n(ln) -
				#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
				#   constraint: self.C >= ln >= 0
				#           and sum_n(ln*yn) = 0
				# Then we get w using w = sum_n(ln*yn*xn)
				# At the end we can get b ~= mean(yn - w . xn)
				#
				# Since we use kernels, we only need l_star to calculate b
				# and to classify observations

				((UpperCAmelCase_)  ,			)  :				Optional[int]      =      np.shape(lowerCAmelCase_		)

				def to_minimize(lowerCAmelCase_      :				ndarray		) -> float:
							UpperCAmelCase_  :				int      =      0
							((UpperCAmelCase_)  ,			)  :				str      =      np.shape(lowerCAmelCase_		)
							for i in range(lowerCAmelCase_		):
										for j in range(lowerCAmelCase_		):
													s += (
													    candidate[i]
													    * candidate[j]
													    * classes[i]
													    * classes[j]
													    * self.kernel(observations[i]			,			observations[j]		)
													)
							return 1 / 2 * s - sum(lowerCAmelCase_		)

				UpperCAmelCase_  :				int      =      LinearConstraint(lowerCAmelCase_			,			0			,			0		)
				UpperCAmelCase_  :				Dict      =      Bounds(0			,			self.regularization		)

				UpperCAmelCase_  :				Optional[Any]      =      minimize(
				    lowerCAmelCase_			,			np.ones(lowerCAmelCase_		)			,			bounds=lowerCAmelCase_			,			constraints=[ly_contraint]		).x
				UpperCAmelCase_  :				Optional[Any]      =      l_star

				# calculating mean offset of separation plane to points
				UpperCAmelCase_  :				Optional[int]      =      0
				for i in range(lowerCAmelCase_		):
							for j in range(lowerCAmelCase_		):
										s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
										    observations[i]			,			observations[j]		)
				UpperCAmelCase_  :				str      =      s / n





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				ndarray		)		->    int:
				UpperCAmelCase_  :				List[Any]      =      sum(
				    self.optimum[n]
				    * self.classes[n]
				    * self.kernel(self.observations[n]			,			lowerCAmelCase_		)
				    for n in range(len(self.classes		)		)		)
				return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



