




import argparse
import os
import re

import packaging.version


SCREAMING_SNAKE_CASE:     Tuple									= '''examples/'''
SCREAMING_SNAKE_CASE:     Tuple									= {
    '''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'''),
}
SCREAMING_SNAKE_CASE:     Union[str, Any]									= {
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
SCREAMING_SNAKE_CASE:     List[str]									= '''README.md'''


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Optional[int]:
		with open(lowerCAmelCase    ,						'r'    ,						encoding='utf-8'    ,						newline='\n' ) as f:
				SCREAMING_SNAKE_CASE_					      =							f.read()
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							REPLACE_PATTERNS[pattern]
		SCREAMING_SNAKE_CASE_					      =							replace.replace('VERSION'    ,						lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							re_pattern.sub(lowerCAmelCase    ,						lowerCAmelCase )
		with open(lowerCAmelCase    ,						'w'    ,						encoding='utf-8'    ,						newline='\n' ) as f:
				f.write(lowerCAmelCase )


def 					_a							(					lowerCAmelCase )->	Tuple:
		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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=False )->	Dict:
		for pattern, fname in REPLACE_FILES.items():
				update_version_in_file(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
		if not patch:
				update_version_in_examples(lowerCAmelCase )


def 					_a							(					)->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							'🤗 Transformers currently provides the following architectures'
		SCREAMING_SNAKE_CASE_					      =							'1. Want to contribute a new model?'
		with open(lowerCAmelCase    ,						'r'    ,						encoding='utf-8'    ,						newline='\n' ) as f:
				SCREAMING_SNAKE_CASE_					      =							f.readlines()

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

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

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


def 					_a							(					)->	int:
		with open(REPLACE_FILES['init']    ,						'r' ) as f:
				SCREAMING_SNAKE_CASE_					      =							f.read()
		SCREAMING_SNAKE_CASE_					      =							REPLACE_PATTERNS['init'][0].search(lowerCAmelCase ).groups()[0]
		return packaging.version.parse(lowerCAmelCase )


def 					_a							(					lowerCAmelCase=False )->	str:
		SCREAMING_SNAKE_CASE_					      =							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:
				SCREAMING_SNAKE_CASE_					      =							default_version.base_version
		elif patch:
				SCREAMING_SNAKE_CASE_					      =							F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
		else:
				SCREAMING_SNAKE_CASE_					      =							F'''{default_version.major}.{default_version.minor + 1}.0'''

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

		print(F'''Updating version to {version}.''' )
		global_version_update(lowerCAmelCase    ,						patch=lowerCAmelCase )


def 					_a							(					)->	str:
		SCREAMING_SNAKE_CASE_					      =							get_version()
		SCREAMING_SNAKE_CASE_					      =							F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
		SCREAMING_SNAKE_CASE_					      =							current_version.base_version

		# Check with the user we got that right.
		SCREAMING_SNAKE_CASE_					      =							input(F'''Which version are we developing now? [{dev_version}]''' )
		if len(lowerCAmelCase ) == 0:
				SCREAMING_SNAKE_CASE_					      =							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__":
						SCREAMING_SNAKE_CASE:     List[str]									= 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.''')
						SCREAMING_SNAKE_CASE:     List[Any]									= 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()





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


def 					_a							(					lowerCAmelCase )->	float:
		return np.dot(lowerCAmelCase    ,						lowerCAmelCase )



class    lowercase_		:



						def __init__(			self	:   int					,							*,
						    snake_case__	:   float = np.inf					,							snake_case__	:   str = "linear"					,							snake_case__	:   float = 0.0					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							regularization
								SCREAMING_SNAKE_CASE_					      =							gamma
								if kernel == "linear":
										SCREAMING_SNAKE_CASE_					      =							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'  )
										SCREAMING_SNAKE_CASE_					      =							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:
										SCREAMING_SNAKE_CASE_					      =							f'''Unknown kernel: {kernel}'''
										raise ValueError(snake_case__  )



						def 	__a					(			self	:   Tuple					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								return np.dot(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   int					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								return np.exp(-(self.gamma * norm_squared(vectora - vectora  ))  )



						def 	__a					(			self	:   int					,							snake_case__	:   list[ndarray]					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							observations
								SCREAMING_SNAKE_CASE_					      =							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

								((SCREAMING_SNAKE_CASE_)      ,				)					      =							np.shape(snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							LinearConstraint(snake_case__					,							0					,							0  )
								SCREAMING_SNAKE_CASE_					      =							Bounds(0					,							self.regularization  )

								SCREAMING_SNAKE_CASE_					      =							minimize(
								    snake_case__					,							np.ones(snake_case__  )					,							bounds=snake_case__					,							constraints=[ly_contraint]  ).x
								SCREAMING_SNAKE_CASE_					      =							l_star

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






						def 	__a					(			self	:   Any					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							sum(
								    self.optimum[n]
								    * self.classes[n]
								    * self.kernel(self.observations[n]					,							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()