



import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 snake_case__				:							str      							= np.array([[1, item, train_mtch[i]] for i, item in enumerate(_lowerCamelCase)])
 snake_case__				:							Optional[Any]      							= np.array(_lowerCamelCase)
 snake_case__				:							List[str]      							= np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()						,   _lowerCamelCase))						,   x.transpose())						,   _lowerCamelCase)
 return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 snake_case__				:							Optional[int]      							= (1, 2, 1)
 snake_case__				:							Optional[int]      							= (1, 1, 0, 7)
 snake_case__				:							List[Any]      							= SARIMAX(
     _lowerCamelCase						,   exog=_lowerCamelCase						,   order=_lowerCamelCase						,   seasonal_order=_lowerCamelCase)
 snake_case__				:							List[Any]      							= model.fit(disp=_lowerCamelCase						,   maxiter=600						,   method="""nm""")
 snake_case__				:							List[Any]      							= model_fit.predict(1						,   len(_lowerCamelCase)						,   exog=[test_match])
 return result[0]
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 snake_case__				:							Optional[Any]      							= SVR(kernel="""rbf"""						,   C=1						,   gamma=0.1						,   epsilon=0.1)
 regressor.fit(_lowerCamelCase						,   _lowerCamelCase)
 snake_case__				:							Optional[Any]      							= regressor.predict(_lowerCamelCase)
 return y_pred[0]
def 	_lowercase	(     UpperCAmelCase_):




 """simple docstring"""




 train_user.sort()
 snake_case__				:							List[str]      							= np.percentile(_lowerCamelCase						,   25)
 snake_case__				:							Optional[int]      							= np.percentile(_lowerCamelCase						,   75)
 snake_case__				:							List[Any]      							= qa - qa
 snake_case__				:							List[str]      							= qa - (iqr * 0.1)
 return low_lim
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 snake_case__				:							Tuple      							= 0
 snake_case__				:							str      							= 0
 for i in list_vote:
  if i > actual_result:
   snake_case__				:							str      							= not_safe + 1
  else:
   if abs(abs(_lowerCamelCase) - abs(_lowerCamelCase)) <= 0.1:
    safe += 1
   else:
    not_safe += 1
 return safe > not_safe


if __name__ == "__main__":
 # data_input_df = pd.read_csv("ex_data.csv", header=None)
 lowercase_:  Tuple     						=					[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
 lowercase_:  Tuple     						=					pd.DataFrame(
     data_input, columns=['total_user', 'total_even', 'days']
 )
 lowercase_:  Optional[Any]     						=					Normalizer().fit_transform(data_input_df.values)
 # split data
 lowercase_:  str     						=					normalize_df[:, 2].tolist()
 lowercase_:  Tuple     						=					normalize_df[:, 0].tolist()
 lowercase_:  int     						=					normalize_df[:, 1].tolist()

 # for svr (input variable = total date and total match)
 lowercase_:  List[str]     						=					normalize_df[:, [1, 2]].tolist()
 lowercase_:  Any     						=					x[: len(x) - 1]
 lowercase_:  Optional[int]     						=					x[len(x) - 1 :]

 # for linear regression & sarimax
 lowercase_:  Dict     						=					total_date[: len(total_date) - 1]
 lowercase_:  List[str]     						=					total_user[: len(total_user) - 1]
 lowercase_:  int     						=					total_match[: len(total_match) - 1]

 lowercase_:  Union[str, Any]     						=					total_date[len(total_date) - 1 :]
 lowercase_:  Any     						=					total_user[len(total_user) - 1 :]
 lowercase_:  List[str]     						=					total_match[len(total_match) - 1 :]

 # voting system with forecasting
 lowercase_:  Union[str, Any]     						=					[
     linear_regression_prediction(
         trn_date, trn_user, trn_match, tst_date, tst_match
     ),
     sarimax_predictor(trn_user, trn_match, tst_match),
     support_vector_regressor(x_train, x_test, trn_user),
 ]

 # check the safety of today's data
 lowercase_:  int     						=					'' if data_safety_checker(res_vote, tst_user) else 'not '
 print('Today\'s data is {not_str}safe.')











'''simple docstring'''






import argparse
import os
import re

import packaging.version


__UpperCamelCase					    =	"examples/"
__UpperCamelCase					    =	{
    "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"),
}
__UpperCamelCase					    =	{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__UpperCamelCase					    =	"README.md"







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Tuple:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


			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 ) ->				str:



			"""simple docstring"""


			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
			if not patch:
						update_version_in_examples(_lowerCamelCase )







def 					_a						(       ) ->				Optional[int]:



			"""simple docstring"""


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

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

			__snake_case					:      int							      =     start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt ):
						if lines[index].startswith("""1.""" ):
									__snake_case					:      Optional[Any]							      =     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 					_a						(       ) ->				Union[str, Any]:



			"""simple docstring"""


			with open(REPLACE_FILES["""init"""]    ,	"""r""" ) as f:
						__snake_case					:      List[Any]							      =     f.read()
			__snake_case					:      str							      =     REPLACE_PATTERNS["""init"""][0].search(_lowerCamelCase ).groups()[0]
			return packaging.version.parse(_lowerCamelCase )







def 					_a						(       _lowerCamelCase=False ) ->				int:



			"""simple docstring"""


			__snake_case					:      List[Any]							      =     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:
						__snake_case					:      str							      =     default_version.base_version
			elif patch:
						__snake_case					:      Optional[int]							      =     F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
			else:
						__snake_case					:      Dict							      =     F'''{default_version.major}.{default_version.minor + 1}.0'''

			# Now let's ask nicely if that's the right one.
			__snake_case					:      Dict							      =     input(F'''Which version are you releasing? [{default_version}]''' )
			if len(_lowerCamelCase ) == 0:
						__snake_case					:      Any							      =     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 					_a						(       ) ->				Tuple:



			"""simple docstring"""


			__snake_case					:      Optional[Any]							      =     get_version()
			__snake_case					:      Tuple							      =     F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
			__snake_case					:      Union[str, Any]							      =     current_version.base_version

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


