



import argparse
import os
import re

import packaging.version


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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       int ,       UpperCamelCase_				:       Union[str, Any] ,       UpperCamelCase_				:       Any	)->  Any:
    with open(UpperCamelCase_ ,       '''r''' ,       encoding='''utf-8''' ,       newline='''\n'''	) as f:
        A__		 =					f.read()
    A__ ,	A__		 =					REPLACE_PATTERNS[pattern]
    A__		 =					replace.replace('''VERSION''' ,       UpperCamelCase_	)
    A__		 =					re_pattern.sub(UpperCamelCase_ ,       UpperCamelCase_	)
    with open(UpperCamelCase_ ,       '''w''' ,       encoding='''utf-8''' ,       newline='''\n'''	) as f:
        f.write(UpperCamelCase_	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any]	)->  Union[str, Any]:
    for folder, directories, fnames in os.walk(UpperCamelCase_	):
        # 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(UpperCamelCase_ ,       UpperCamelCase_	) ,       UpperCamelCase_ ,       pattern='''examples'''	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any] ,       UpperCamelCase_				:       Optional[Any]=False	)->  Union[str, Any]:
    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)
    if not patch:
        update_version_in_examples(UpperCamelCase_	)



def 		lowerCAmelCase__    (					)->  Any:
    A__		 =					'''🤗 Transformers currently provides the following architectures'''
    A__		 =					'''1. Want to contribute a new model?'''
    with open(UpperCamelCase_ ,       '''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/diffusers/main/model_doc''' ,       '''https://huggingface.co/docs/diffusers/model_doc''' ,       )
        index += 1

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



def 		lowerCAmelCase__    (					)->  str:
    with open(REPLACE_FILES['''init'''] ,       '''r'''	) as f:
        A__		 =					f.read()
    A__		 =					REPLACE_PATTERNS['''init'''][0].search(UpperCamelCase_	).groups()[0]
    return packaging.version.parse(UpperCamelCase_	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str=False	)->  Optional[int]:
    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(UpperCamelCase_	) == 0:
        A__		 =					default_version

    print(f"Updating version to {version}."	)
    global_version_update(UpperCamelCase_ ,       patch=UpperCamelCase_	)



def 		lowerCAmelCase__    (					)->  Any:
    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(UpperCamelCase_	) == 0:
        A__		 =					dev_version

    print(f"Updating version to {version}."	)
    global_version_update(UpperCamelCase_	)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
   _lowercase						  = 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.")
   _lowercase						  = 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 matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier



def 		lowerCAmelCase__    (					UpperCamelCase_				:       dict	)->  tuple:
    return (data["data"], data["target"])



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray ,       UpperCamelCase_				:       np.ndarray	)->  XGBClassifier:
    A__		 =					XGBClassifier()
    classifier.fit(UpperCamelCase_ ,       UpperCamelCase_	)
    return classifier



def 		lowerCAmelCase__    (					)->  None:
    A__		 =					load_iris()
    A__ ,	A__		 =					data_handling(UpperCamelCase_	)
    A__ ,	A__ ,	A__ ,	A__		 =					train_test_split(
        UpperCamelCase_ ,       UpperCamelCase_ ,       test_size=0.25	)

    A__		 =					iris['''target_names''']

    # Create an XGBoost Classifier from the training data
    A__		 =					xgboost(UpperCamelCase_ ,       UpperCamelCase_	)

    # Display the confusion matrix of the classifier with both training and test sets
    ConfusionMatrixDisplay.from_estimator(
        UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       display_labels=UpperCamelCase_ ,       cmap='''Blues''' ,       normalize='''true''' ,       )
    plt.title('''Normalized Confusion Matrix - IRIS Dataset'''	)
    plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()
