



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
__SCREAMING_SNAKE_CASE           =  """src/diffusers"""
__SCREAMING_SNAKE_CASE           =  """."""


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




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




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Dict              =						object_name.split("."    )
			A							:					Tuple              =						0

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

			with open(os.path.join(_lowerCamelCase ,    f"""{module}.py"""    ) ,    "r" ,    encoding="utf-8" ,    newline="\n"    ) as f:
						A							:					Tuple              =						f.readlines()

			# Now let's find the class / func in the code!
			A							:					Any              =						""
			A							:					Union[str, Any]              =						0
			for name in parts[i + 1 :]:
						while (
						    line_index < len(_lowerCamelCase    ) 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(_lowerCamelCase    ):
						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).
			A							:					Optional[int]              =						line_index
			while line_index < len(_lowerCamelCase    ) and _should_continue(lines[line_index] ,    _lowerCamelCase    ):
						line_index += 1
			# Clean up empty lines at the end (if any).
			while len(lines[line_index - 1]    ) <= 1:
						line_index -= 1

			A							:					int              =						lines[start_index:line_index]
			return "".join(_lowerCamelCase    )


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




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					List[str]              =						code.split("\n"    )
			A							:					List[str]              =						0
			while idx < len(_lowerCamelCase    ) and len(lines[idx]    ) == 0:
						idx += 1
			if idx < len(_lowerCamelCase    ):
						return re.search(R"^(\s*)\S" ,    lines[idx]    ).groups()[0]
			return ""




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					List[Any]              =						len(get_indent(_lowerCamelCase    )    ) > 0
			if has_indent:
						A							:					Tuple              =						f"""class Bla:\n{code}"""
			A							:					int              =						black.Mode(target_versions={black.TargetVersion.PYaa} ,    line_length=119 ,    preview=_lowerCamelCase    )
			A							:					Any              =						black.format_str(_lowerCamelCase ,    mode=_lowerCamelCase    )
			A       , A							:					Dict              =						style_docstrings_in_code(_lowerCamelCase    )
			return result[len("class Bla:\n"    ) :] if has_indent else result




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase=False    ):
			with open(_lowerCamelCase ,    "r" ,    encoding="utf-8" ,    newline="\n"    ) as f:
						A							:					Dict              =						f.readlines()
			A							:					List[Any]              =						[]
			A							:					List[str]              =						0
			# Not a for loop cause `lines` is going to change (if `overwrite=True`).
			while line_index < len(_lowerCamelCase    ):
						A							:					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.
						A       , A       , A							:					Optional[int]              =						search.groups()
						A							:					str              =						find_code_in_diffusers(_lowerCamelCase    )
						A							:					Optional[int]              =						get_indent(_lowerCamelCase    )

						A							:					int              =						line_index + 1 if indent == theoretical_indent else line_index + 2
						A							:					Union[str, Any]              =						theoretical_indent
						A							:					Dict              =						start_index

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

						A							:					str              =						lines[start_index:line_index]
						A							:					str              =						"".join(_lowerCamelCase    )

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

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

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

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




def 	UpperCAmelCase   (						_lowerCamelCase = False    ):
			A							:					List[Any]              =						glob.glob(os.path.join(_lowerCamelCase ,    "**/*.py"    ) ,    recursive=_lowerCamelCase    )
			A							:					Any              =						[]
			for filename in all_files:
						A							:					Dict              =						is_copy_consistent(_lowerCamelCase ,    _lowerCamelCase    )
						diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
			if not overwrite and len(_lowerCamelCase    ) > 0:
						A							:					List[str]              =						"\n".join(_lowerCamelCase    )
						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__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()

							check_copies(args.fix_and_overwrite)




from sklearn.metrics import recall_score

import datasets


__SCREAMING_SNAKE_CASE           =  """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


__SCREAMING_SNAKE_CASE           =  """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


__SCREAMING_SNAKE_CASE           =  """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""

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






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				str:
									return datasets.MetricInfo(
									    description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(
									        {
									            "predictions": datasets.Sequence(datasets.Value("int32" ) ),
									            "references": datasets.Sequence(datasets.Value("int32" ) ),
									        }
									        if self.config_name == "multilabel"
									        else {
									            "predictions": datasets.Value("int32" ),
									            "references": datasets.Value("int32" ),
									        } )	,	reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"]	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			str=None	,	__lowerCamelCase      :			List[Any]=1	,	__lowerCamelCase      :			Tuple="binary"	,	__lowerCamelCase      :			Tuple=None	,	__lowerCamelCase      :			Tuple="warn"	,	)      ->				Optional[Any]:
									A							:					str              =						recall_score(
									    __lowerCamelCase	,	__lowerCamelCase	,	labels=__lowerCamelCase	,	pos_label=__lowerCamelCase	,	average=__lowerCamelCase	,	sample_weight=__lowerCamelCase	,	zero_division=__lowerCamelCase	,	)
									return {"recall": float(__lowerCamelCase ) if score.size == 1 else score}