
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_											=						"src/diffusers"
a_											=						"."


# This is to make sure the diffusers module imported is the one in the repo.
a_											=						importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
a_											=						spec.loader.load_module()

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






    return line.startswith(__SCREAMING_SNAKE_CASE       ) or len(__SCREAMING_SNAKE_CASE       ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$",		__SCREAMING_SNAKE_CASE       ) is not None

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






    snake_case_ :    Optional[int]     			=						object_name.split("."       )
    snake_case_ :    Dict     			=						0

    # First let's find the module where our object lives.
    snake_case_ :    int     			=						parts[i]
    while i < len(__SCREAMING_SNAKE_CASE       ) and not os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE,		f'{module}.py'       )       ):
        i += 1
        if i < len(__SCREAMING_SNAKE_CASE       ):
            snake_case_ :    List[Any]     			=						os.path.join(__SCREAMING_SNAKE_CASE,		parts[i]       )
    if i >= len(__SCREAMING_SNAKE_CASE       ):
        raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.'       )

    with open(os.path.join(__SCREAMING_SNAKE_CASE,		f'{module}.py'       ),		"r",		encoding="utf-8",		newline="\n"       ) as f:
        snake_case_ :    str     			=						f.readlines()

    # Now let's find the class / func in the code!
    snake_case_ :    Any     			=						""
    snake_case_ :    Optional[Any]     			=						0
    for name in parts[i + 1 :]:
        while (
            line_index < len(__SCREAMING_SNAKE_CASE       ) and re.search(rf'^{indent}(class|def)\s+{name}(\(|\:)',		lines[line_index]       ) is None
        ):
            line_index += 1
        indent += "    "
        line_index += 1

    if line_index >= len(__SCREAMING_SNAKE_CASE       ):
        raise ValueError(f' {object_name} does not match any function or class in {module}.'       )

    # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
    snake_case_ :    Union[str, Any]     			=						line_index
    while line_index < len(__SCREAMING_SNAKE_CASE       ) and _should_continue(lines[line_index],		__SCREAMING_SNAKE_CASE       ):
        line_index += 1
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]       ) <= 1:
        line_index -= 1

    snake_case_ :    Dict     			=						lines[start_index:line_index]
    return "".join(__SCREAMING_SNAKE_CASE       )


a_											=						re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
a_											=						re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
a_											=						re.compile(R"<FILL\s+[^>]*>")

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






    snake_case_ :    str     			=						code.split("\n"       )
    snake_case_ :    Union[str, Any]     			=						0
    while idx < len(__SCREAMING_SNAKE_CASE       ) and len(lines[idx]       ) == 0:
        idx += 1
    if idx < len(__SCREAMING_SNAKE_CASE       ):
        return re.search(r"^(\s*)\S",		lines[idx]       ).groups()[0]
    return ""

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






    snake_case_ :    Optional[Any]     			=						len(get_indent(__SCREAMING_SNAKE_CASE       )       ) > 0
    if has_indent:
        snake_case_ :    Optional[Any]     			=						f'class Bla:\n{code}'
    snake_case_ :    List[Any]     			=						black.Mode(target_versions={black.TargetVersion.PYaa},		line_length=1_1_9,		preview=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[str]     			=						black.format_str(__SCREAMING_SNAKE_CASE,		mode=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[Any]     			=						style_docstrings_in_code(__SCREAMING_SNAKE_CASE       )
    return result[len("class Bla:\n"       ) :] if has_indent else result

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






    with open(__SCREAMING_SNAKE_CASE,		"r",		encoding="utf-8",		newline="\n"       ) as f:
        snake_case_ :    List[Any]     			=						f.readlines()
    snake_case_ :    str     			=						[]
    snake_case_ :    Tuple     			=						0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Union[str, Any]     			=						_re_copy_warning.search(lines[line_index]       )
        if search is None:
            line_index += 1
            continue

        # There is some copied code here, let's retrieve the original.
        snake_case_ :    Union[str, Any]     			=						search.groups()
        snake_case_ :    int     			=						find_code_in_diffusers(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    List[Any]     			=						get_indent(__SCREAMING_SNAKE_CASE       )

        snake_case_ :    Dict     			=						line_index + 1 if indent == theoretical_indent else line_index + 2
        snake_case_ :    Dict     			=						theoretical_indent
        snake_case_ :    Tuple     			=						start_index

        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
        snake_case_ :    str     			=						True
        while line_index < len(__SCREAMING_SNAKE_CASE       ) and should_continue:
            line_index += 1
            if line_index >= len(__SCREAMING_SNAKE_CASE       ):
                break
            snake_case_ :    Dict     			=						lines[line_index]
            snake_case_ :    List[Any]     			=						_should_continue(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ) and re.search(f'^{indent}# End copy',		__SCREAMING_SNAKE_CASE       ) is None
        # Clean up empty lines at the end (if any).
        while len(lines[line_index - 1]       ) <= 1:
            line_index -= 1

        snake_case_ :    Optional[int]     			=						lines[start_index:line_index]
        snake_case_ :    Optional[Any]     			=						"".join(__SCREAMING_SNAKE_CASE       )

        # Remove any nested `Copied from` comments to avoid circular copies
        snake_case_ :    int     			=						[line for line in theoretical_code.split("\n"       ) if _re_copy_warning.search(__SCREAMING_SNAKE_CASE       ) is None]
        snake_case_ :    Dict     			=						"\n".join(__SCREAMING_SNAKE_CASE       )

        # Before comparing, use the `replace_pattern` on the original code.
        if len(__SCREAMING_SNAKE_CASE       ) > 0:
            snake_case_ :    Union[str, Any]     			=						replace_pattern.replace("with",		""       ).split(","       )
            snake_case_ :    Optional[Any]     			=						[_re_replace_pattern.search(__SCREAMING_SNAKE_CASE       ) for p in patterns]
            for pattern in patterns:
                if pattern is None:
                    continue
                snake_case_ :    Optional[Any]     			=						pattern.groups()
                snake_case_ :    Optional[int]     			=						re.sub(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
                if option.strip() == "all-casing":
                    snake_case_ :    List[Any]     			=						re.sub(obja.lower(),		obja.lower(),		__SCREAMING_SNAKE_CASE       )
                    snake_case_ :    Optional[int]     			=						re.sub(obja.upper(),		obja.upper(),		__SCREAMING_SNAKE_CASE       )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
            snake_case_ :    Optional[int]     			=						blackify(lines[start_index - 1] + theoretical_code       )
            snake_case_ :    Optional[Any]     			=						theoretical_code[len(lines[start_index - 1]       ) :]

        # Test for a diff and act accordingly.
        if observed_code != theoretical_code:
            diffs.append([object_name, start_index]       )
            if overwrite:
                snake_case_ :    List[str]     			=						lines[:start_index] + [theoretical_code] + lines[line_index:]
                snake_case_ :    List[str]     			=						start_index + 1

    if overwrite and len(__SCREAMING_SNAKE_CASE       ) > 0:
        # Warn the user a file has been modified.
        print(f'Detected changes, rewriting {filename}.'       )
        with open(__SCREAMING_SNAKE_CASE,		"w",		encoding="utf-8",		newline="\n"       ) as f:
            f.writelines(__SCREAMING_SNAKE_CASE       )
    return diffs

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






    snake_case_ :    List[str]     			=						glob.glob(os.path.join(__SCREAMING_SNAKE_CASE,		"**/*.py"       ),		recursive=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						[]
    for filename in all_files:
        snake_case_ :    Tuple     			=						is_copy_consistent(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
    if not overwrite and len(__SCREAMING_SNAKE_CASE       ) > 0:
        snake_case_ :    str     			=						"\n".join(__SCREAMING_SNAKE_CASE       )
        raise Exception(
            "Found the following copy inconsistencies:\n"
            + diff
            + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."       )


if __name__ == "__main__":
 a_											=						argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 a_											=						parser.parse_args()

 check_copies(args.fix_and_overwrite)






'''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




