
'''simple docstring'''
import argparse
import os
import re

import packaging.version


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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    with open(__SCREAMING_SNAKE_CASE,		"r",		encoding="utf-8",		newline="\n"       ) as f:
        snake_case_ :    Dict     			=						f.read()
    snake_case_ :    int     			=						REPLACE_PATTERNS[pattern]
    snake_case_ :    int     			=						replace.replace("VERSION",		__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Any     			=						re_pattern.sub(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    with open(__SCREAMING_SNAKE_CASE,		"w",		encoding="utf-8",		newline="\n"       ) as f:
        f.write(__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for folder, directories, fnames in os.walk(__SCREAMING_SNAKE_CASE       ):
        # 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(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		__SCREAMING_SNAKE_CASE,		pattern="examples"       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	):
    """simple docstring"""






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

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

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

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

def 						UpperCamelCase_     (	):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE=False       ):
    """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_ :    Any     			=						default_version.base_version
    elif patch:
        snake_case_ :    List[Any]     			=						f'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
    else:
        snake_case_ :    str     			=						f'{default_version.major}.{default_version.minor + 1}.0'

    # Now let's ask nicely if that's the right one.
    snake_case_ :    Optional[Any]     			=						input(f'Which version are you releasing? [{default_version}]'       )
    if len(__SCREAMING_SNAKE_CASE       ) == 0:
        snake_case_ :    Any     			=						default_version

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

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    int     			=						get_version()
    snake_case_ :    Tuple     			=						f'{current_version.major}.{current_version.minor + 1}.0.dev0'
    snake_case_ :    Optional[Any]     			=						current_version.base_version

    # Check with the user we got that right.
    snake_case_ :    Tuple     			=						input(f'Which version are we developing now? [{dev_version}]'       )
    if len(__SCREAMING_SNAKE_CASE       ) == 0:
        snake_case_ :    Union[str, Any]     			=						dev_version

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


if __name__ == "__main__":
 a_											=						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.")
 a_											=						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()






'''simple docstring'''
from typing import Dict, Optional

import numpy as np

import datasets


a_											=						"\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

a_											=						"\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

a_											=						"\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		):
    """simple docstring"""






    if label_map is not None:
        for old_id, new_id in label_map.items():
            snake_case_ :    int     			=						new_id

    # turn into Numpy arrays
    snake_case_ :    int     			=						np.array(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Any     			=						np.array(__SCREAMING_SNAKE_CASE       )

    if reduce_labels:
        snake_case_ :    str     			=						2_5_5
        snake_case_ :    str     			=						label - 1
        snake_case_ :    List[Any]     			=						2_5_5

    snake_case_ :    Union[str, Any]     			=						label != ignore_index
    snake_case_ :    Dict     			=						np.not_equal(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Tuple     			=						pred_label[mask]
    snake_case_ :    Any     			=						np.array(__SCREAMING_SNAKE_CASE       )[mask]

    snake_case_ :    List[str]     			=						pred_label[pred_label == label]

    snake_case_ :    int     			=						np.histogram(__SCREAMING_SNAKE_CASE,		bins=__SCREAMING_SNAKE_CASE,		range=(0, num_labels - 1)       )[0]
    snake_case_ :    Tuple     			=						np.histogram(__SCREAMING_SNAKE_CASE,		bins=__SCREAMING_SNAKE_CASE,		range=(0, num_labels - 1)       )[0]
    snake_case_ :    List[Any]     			=						np.histogram(__SCREAMING_SNAKE_CASE,		bins=__SCREAMING_SNAKE_CASE,		range=(0, num_labels - 1)       )[0]

    snake_case_ :    List[Any]     			=						area_pred_label + area_label - area_intersect

    return area_intersect, area_union, area_pred_label, area_label

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    snake_case_ :    List[str]     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    snake_case_ :    int     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    snake_case_ :    int     			=						np.zeros((num_labels,),		dtype=np.floataa       )
    for result, gt_seg_map in zip(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
        snake_case_			, snake_case_			, snake_case_			, snake_case_ :    int     			=						intersect_and_union(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        total_area_intersect += area_intersect
        total_area_union += area_union
        total_area_pred_label += area_pred_label
        total_area_label += area_label
    return total_area_intersect, total_area_union, total_area_pred_label, total_area_label

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = None,		__SCREAMING_SNAKE_CASE = False,		):
    """simple docstring"""






    snake_case_			, snake_case_			, snake_case_			, snake_case_ :    Tuple     			=						total_intersect_and_union(
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    # compute metrics
    snake_case_ :    Union[str, Any]     			=						{}

    snake_case_ :    int     			=						total_area_intersect.sum() / total_area_label.sum()
    snake_case_ :    str     			=						total_area_intersect / total_area_union
    snake_case_ :    Union[str, Any]     			=						total_area_intersect / total_area_label

    snake_case_ :    List[str]     			=						np.nanmean(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						np.nanmean(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Any     			=						all_acc
    snake_case_ :    Union[str, Any]     			=						iou
    snake_case_ :    Tuple     			=						acc

    if nan_to_num is not None:
        snake_case_ :    str     			=						{metric: np.nan_to_num(__SCREAMING_SNAKE_CASE,		nan=__SCREAMING_SNAKE_CASE       ) for metric, metric_value in metrics.items()}

    return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION					)
class    UpperCAmelCase_				(	datasets.Metric					):





 def        snake_case__		(    self):
     return datasets.MetricInfo(
         description=_DESCRIPTION ,       citation=_CITATION ,       inputs_description=_KWARGS_DESCRIPTION ,       features=datasets.Features(
             # 1st Seq - height dim, 2nd - width dim
             {
                 "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"))),
                 "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"))),
             }) ,       reference_urls=[
             "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
         ] ,       )





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = False ,       ):
     snake_case_ :    Optional[Any]     			=						mean_iou(
         results=lowercase_ ,       gt_seg_maps=lowercase_ ,       num_labels=lowercase_ ,       ignore_index=lowercase_ ,       nan_to_num=lowercase_ ,       label_map=lowercase_ ,       reduce_labels=lowercase_ ,       )
     return iou_result




