





'''simple docstring'''

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





def 						UpperCamelCase    (					_lowerCamelCase : ndarray	):
    return np.dot(_a						,	_a	)






class        UpperCAmelCase     :
 def __init__(    self				:Optional[int]						,	*,
     lowercase_				:Tuple = np.inf						,	lowercase_				:str = "linear"						,	lowercase_				:List[str] = 0.0						,	)->							None:
     A__    =      regularization
     A__    =      gamma
     if kernel == "linear":
         A__    =      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"							)
         A__    =      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:
         A__    =      F"Unknown kernel: {kernel}"
         raise ValueError(_SCREAMING_SNAKE_CASE							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Union[str, Any]						,	lowercase_				:Any							)->							float:
     return np.dot(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Any						,	lowercase_				:Tuple							)->							float:
     return np.exp(-(self.gamma * norm_squared(vectora - vectora							))							)
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:str						,	lowercase_				:List[Any]							)->							None:
     A__    =      observations
     A__    =      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

     (A__    )    =      np.shape(_SCREAMING_SNAKE_CASE							)

     def to_minimize(lowercase_				:Union[str, Any]							) -> float:
         A__    =      0
         (A__    )    =      np.shape(_SCREAMING_SNAKE_CASE							)
         for i in range(_SCREAMING_SNAKE_CASE							):
             for j in range(_SCREAMING_SNAKE_CASE							):
                 s += (
                     candidate[i]
                     * candidate[j]
                     * classes[i]
                     * classes[j]
                     * self.kernel(observations[i]						,	observations[j]							)
                 )
         return 1 / 2 * s - sum(_SCREAMING_SNAKE_CASE							)

     A__    =      LinearConstraint(_SCREAMING_SNAKE_CASE						,	0						,	0							)
     A__    =      Bounds(0						,	self.regularization							)

     A__    =      minimize(
         _SCREAMING_SNAKE_CASE						,	np.ones(_SCREAMING_SNAKE_CASE							)						,	bounds=_SCREAMING_SNAKE_CASE						,	constraints=[ly_contraint]							).x
     A__    =      l_star

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



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


if __name__ == "__main__":
  import doctest

  doctest.testmod()










'''simple docstring'''

import argparse
import os
import re

import packaging.version


__lowerCAmelCase							:     List[Any]		     ="examples/"
__lowerCAmelCase							:     Dict		     ={
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__lowerCAmelCase							:     List[str]		     ={
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__lowerCAmelCase							:     str		     ="README.md"





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Optional[Any]	):
    with open(_lowerCamelCase						,	"r"						,	encoding="utf-8"						,	newline="\n"	) as f:
        A__    =      f.read()
    A__,     A__    =      REPLACE_PATTERNS[pattern]
    A__    =      replace.replace("VERSION"						,	_lowerCamelCase	)
    A__    =      re_pattern.sub(_lowerCamelCase						,	_lowerCamelCase	)
    with open(_lowerCamelCase						,	"w"						,	encoding="utf-8"						,	newline="\n"	) as f:
        f.write(_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    for folder, directories, fnames in os.walk(_lowerCamelCase	):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove("research_projects"	)
        if "legacy" in directories:
            directories.remove("legacy"	)
        for fname in fnames:
            if fname.endswith(".py"	):
                update_version_in_file(os.path.join(_lowerCamelCase						,	_lowerCamelCase	)						,	_lowerCamelCase						,	pattern="examples"	)





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Union[str, Any]=False	):
    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    if not patch:
        update_version_in_examples(_lowerCamelCase	)





def 						UpperCamelCase    (							):
    A__    =      "🤗 Transformers currently provides the following architectures"
    A__    =      "1. Want to contribute a new model?"
    with open(_lowerCamelCase						,	"r"						,	encoding="utf-8"						,	newline="\n"	) as f:
        A__    =      f.readlines()

    # Find the start of the list.
    A__    =      0
    while not lines[start_index].startswith(_start_prompt	):
        start_index += 1
    start_index += 1

    A__    =      start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt	):
        if lines[index].startswith("1."	):
            A__    =      lines[index].replace(
                "https://huggingface.co/docs/transformers/main/model_doc"						,	"https://huggingface.co/docs/transformers/model_doc"						,	)
        index += 1

    with open(_lowerCamelCase						,	"w"						,	encoding="utf-8"						,	newline="\n"	) as f:
        f.writelines(_lowerCamelCase	)





def 						UpperCamelCase    (							):
    with open(REPLACE_FILES["init"]						,	"r"	) as f:
        A__    =      f.read()
    A__    =      REPLACE_PATTERNS["init"][0].search(_lowerCamelCase	).groups()[0]
    return packaging.version.parse(_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : Dict=False	):
    A__    =      get_version()
    if patch and default_version.is_devrelease:
        raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"	)
    if default_version.is_devrelease:
        A__    =      default_version.base_version
    elif patch:
        A__    =      F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
    else:
        A__    =      F"{default_version.major}.{default_version.minor + 1}.0"

    # Now let's ask nicely if that's the right one.
    A__    =      input(F"Which version are you releasing? [{default_version}]"	)
    if len(_lowerCamelCase	) == 0:
        A__    =      default_version

    print(F"Updating version to {version}."	)
    global_version_update(_lowerCamelCase						,	patch=_lowerCamelCase	)
    if not patch:
        print("Cleaning main README, don't forget to run `make fix-copies`."	)
        clean_main_ref_in_model_list()





def 						UpperCamelCase    (							):
    A__    =      get_version()
    A__    =      F"{current_version.major}.{current_version.minor + 1}.0.dev0"
    A__    =      current_version.base_version

    # Check with the user we got that right.
    A__    =      input(F"Which version are we developing now? [{dev_version}]"	)
    if len(_lowerCamelCase	) == 0:
        A__    =      dev_version

    print(F"Updating version to {version}."	)
    global_version_update(_lowerCamelCase	)
    print("Cleaning main README, don't forget to run `make fix-copies`."	)
    clean_main_ref_in_model_list()


if __name__ == "__main__":
  __lowerCAmelCase							:     Union[str, Any]		     =argparse.ArgumentParser()
  parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
  parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
  __lowerCAmelCase							:     int		     =parser.parse_args()
  if not args.post_release:
    pre_release_work(patch=args.patch)
  elif args.patch:
    print("Nothing to do after a patch :-)")
  else:
    post_release_work()



