
"""simple docstring"""


import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


A_						       =				"""1.5.0"""

A_						       =				["""vision-encoder-decoder"""]






def    lowercase	(  lowerCAmelCase__	):
			if is_optimum_available():
						from optimum.version import __version__ as optimum_version

						lowerCamelCase_ 		=     version.parse(lowerCAmelCase__	)
						if parsed_optimum_version < version.parse(lowerCAmelCase__	):
									raise RuntimeError(
									    f"transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You "
									    '''can upgrade optimum by running: pip install -U optimum[exporters]'''	)
			else:
						raise RuntimeError(
						    '''transformers.onnx requires optimum to run, you can install the library by running: pip install '''
						    '''optimum[exporters]'''	)
			lowerCamelCase_ 		=     [
			    sys.executable,
			    '''-m''',
			    '''optimum.exporters.onnx''',
			    f"--model {args.model}",
			    f"--task {args.feature}",
			    f"--framework {args.framework}" if args.framework is not None else '''''',
			    f"{args.output}",
			]
			lowerCamelCase_ 		=     subprocess.Popen(''' '''.join(lowerCAmelCase__	)  ,stdout=subprocess.PIPE  ,shell=lowerCAmelCase__	)
			proc.wait()

			logger.info(
			    '''The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as '''
			    '''transformers.onnx is deprecated, and will be removed in v5. You can find more information here: '''
			    '''https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'''	)






def    lowercase	(  lowerCAmelCase__	):
			lowerCamelCase_ 		=     args.output if args.output.is_file() else args.output.joinpath('''model.onnx'''	)
			if not args.output.parent.exists():
						args.output.parent.mkdir(parents=lowerCAmelCase__	)

			# Allocate the model
			lowerCamelCase_ 		=     FeaturesManager.get_model_from_feature(
			    args.feature  ,args.model  ,framework=args.framework  ,cache_dir=args.cache_dir	)

			lowerCamelCase_		,   lowerCamelCase_ 		=     FeaturesManager.check_supported_model_or_raise(lowerCAmelCase__  ,feature=args.feature	)
			lowerCamelCase_ 		=     model_onnx_config(model.config	)

			if model_kind in ENCODER_DECODER_MODELS:
						lowerCamelCase_ 		=     model.get_encoder()
						lowerCamelCase_ 		=     model.get_decoder()

						lowerCamelCase_ 		=     onnx_config.get_encoder_config(encoder_model.config	)
						lowerCamelCase_ 		=     onnx_config.get_decoder_config(
						    encoder_model.config  ,decoder_model.config  ,feature=args.feature	)

						if args.opset is None:
									lowerCamelCase_ 		=     max(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset	)

						if args.opset < min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset	):
									raise ValueError(
									    f"Opset {args.opset} is not sufficient to export {model_kind}. At least "
									    f" {min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset	)} is required."	)

						lowerCamelCase_ 		=     AutoFeatureExtractor.from_pretrained(args.model	)

						lowerCamelCase_		,   lowerCamelCase_ 		=     export(
						    lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,args.opset  ,args.output.parent.joinpath('''encoder_model.onnx'''	)  ,)

						validate_model_outputs(
						    lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,args.output.parent.joinpath('''encoder_model.onnx'''	)  ,lowerCAmelCase__  ,args.atol if args.atol else encoder_onnx_config.atol_for_validation  ,)

						lowerCamelCase_ 		=     AutoTokenizer.from_pretrained(args.model	)

						lowerCamelCase_		,   lowerCamelCase_ 		=     export(
						    lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,args.opset  ,args.output.parent.joinpath('''decoder_model.onnx'''	)  ,)

						validate_model_outputs(
						    lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,args.output.parent.joinpath('''decoder_model.onnx'''	)  ,lowerCAmelCase__  ,args.atol if args.atol else decoder_onnx_config.atol_for_validation  ,)
						logger.info(
						    f"All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'	).as_posix()},"
						    f" {args.output.parent.joinpath('decoder_model.onnx'	).as_posix()}"	)

			else:
						# Instantiate the appropriate preprocessor
						if args.preprocessor == "auto":
									lowerCamelCase_ 		=     get_preprocessor(args.model	)
						elif args.preprocessor == "tokenizer":
									lowerCamelCase_ 		=     AutoTokenizer.from_pretrained(args.model	)
						elif args.preprocessor == "image_processor":
									lowerCamelCase_ 		=     AutoImageProcessor.from_pretrained(args.model	)
						elif args.preprocessor == "feature_extractor":
									lowerCamelCase_ 		=     AutoFeatureExtractor.from_pretrained(args.model	)
						elif args.preprocessor == "processor":
									lowerCamelCase_ 		=     AutoProcessor.from_pretrained(args.model	)
						else:
									raise ValueError(f"Unknown preprocessor type '{args.preprocessor}'"	)

						# Ensure the requested opset is sufficient
						if args.opset is None:
									lowerCamelCase_ 		=     onnx_config.default_onnx_opset

						if args.opset < onnx_config.default_onnx_opset:
									raise ValueError(
									    f"Opset {args.opset} is not sufficient to export {model_kind}. "
									    f"At least  {onnx_config.default_onnx_opset} is required."	)

						lowerCamelCase_		,   lowerCamelCase_ 		=     export(
						    lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,args.opset  ,args.output  ,)

						if args.atol is None:
									lowerCamelCase_ 		=     onnx_config.atol_for_validation

						validate_model_outputs(lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,args.output  ,lowerCAmelCase__  ,args.atol	)
						logger.info(f"All good, model saved at: {args.output.as_posix()}"	)
						warnings.warn(
						    '''The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend'''
						    ''' using optimum.exporters.onnx in future. You can find more information here:'''
						    ''' https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'''  ,lowerCAmelCase__  ,)






def    lowercase	(  ):
			lowerCamelCase_ 		=     ArgumentParser('''Hugging Face Transformers ONNX exporter'''	)
			parser.add_argument(
			    '''-m'''  ,'''--model'''  ,type=lowerCAmelCase__  ,required=lowerCAmelCase__  ,help='''Model ID on huggingface.co or path on disk to load model from.'''	)
			parser.add_argument(
			    '''--feature'''  ,default='''default'''  ,help='''The type of features to export the model with.'''  ,)
			parser.add_argument('''--opset'''  ,type=lowerCAmelCase__  ,default=lowerCAmelCase__  ,help='''ONNX opset version to export the model with.'''	)
			parser.add_argument(
			    '''--atol'''  ,type=lowerCAmelCase__  ,default=lowerCAmelCase__  ,help='''Absolute difference tolerance when validating the model.'''	)
			parser.add_argument(
			    '''--framework'''  ,type=lowerCAmelCase__  ,choices=['''pt''', '''tf''']  ,default=lowerCAmelCase__  ,help=(
			        '''The framework to use for the ONNX export.'''
			        ''' If not provided, will attempt to use the local checkpoint\'s original framework'''
			        ''' or what is available in the environment.'''
			    )  ,)
			parser.add_argument('''output'''  ,type=lowerCAmelCase__  ,help='''Path indicating where to store generated ONNX model.'''	)
			parser.add_argument('''--cache_dir'''  ,type=lowerCAmelCase__  ,default=lowerCAmelCase__  ,help='''Path indicating where to store cache.'''	)
			parser.add_argument(
			    '''--preprocessor'''  ,type=lowerCAmelCase__  ,choices=['''auto''', '''tokenizer''', '''feature_extractor''', '''image_processor''', '''processor''']  ,default='''auto'''  ,help='''Which type of preprocessor to use. \'auto\' tries to automatically detect it.'''  ,)
			parser.add_argument(
			    '''--export_with_transformers'''  ,action='''store_true'''  ,help=(
			        '''Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be '''
			        '''useful when exporting a model supported in transformers but not in optimum, otherwise it is not '''
			        '''recommended.'''
			    )  ,)

			lowerCamelCase_ 		=     parser.parse_args()
			if args.export_with_transformers or not is_optimum_available():
						export_with_transformers(lowerCAmelCase__	)
			else:
						export_with_optimum(lowerCAmelCase__	)


if __name__ == "__main__":
						A_						       =				logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
						logger.setLevel(logging.INFO)
						main()





"""simple docstring"""


from sklearn.metrics import roc_auc_score

import datasets


A_						       =				"""
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
"""

A_						       =				"""
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `'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 (i.e. the number of true instances for each label).
    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.
    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results['roc_auc'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
        >>> print(round(results['roc_auc'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results['roc_auc']])
        [0.83, 0.38, 0.94]
"""

A_						       =				"""\
@article{doi:10.1177/0272989X8900900307,
author = {Donna Katzman McClish},
title ={Analyzing a Portion of the ROC Curve},
journal = {Medical Decision Making},
volume = {9},
number = {3},
pages = {190-195},
year = {1989},
doi = {10.1177/0272989X8900900307},
    note ={PMID: 2668680},
URL = {https://doi.org/10.1177/0272989X8900900307},
eprint = {https://doi.org/10.1177/0272989X8900900307}
}


@article{10.1023/A:1010920819831,
author = {Hand, David J. and Till, Robert J.},
title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
year = {2001},
issue_date = {November 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {45},
number = {2},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1010920819831},
doi = {10.1023/A:1010920819831},
journal = {Mach. Learn.},
month = {oct},
pages = {171–186},
numpages = {16},
keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
}


@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 ):




				def 					UpperCAmelCase__	(							self      ):
							return datasets.MetricInfo(
							    description=_DESCRIPTION       ,	citation=_CITATION       ,	inputs_description=_KWARGS_DESCRIPTION       ,	features=datasets.Features(
							        {
							            '''prediction_scores''': datasets.Sequence(datasets.Value('''float'''      )      ),
							            '''references''': datasets.Value('''int32'''      ),
							        }
							        if self.config_name == '''multiclass'''
							        else {
							            '''references''': datasets.Sequence(datasets.Value('''int32'''      )      ),
							            '''prediction_scores''': datasets.Sequence(datasets.Value('''float'''      )      ),
							        }
							        if self.config_name == '''multilabel'''
							        else {
							            '''references''': datasets.Value('''int32'''      ),
							            '''prediction_scores''': datasets.Value('''float'''      ),
							        }      )       ,	reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html''']       ,	)





				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase="macro"       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	UpperCAmelCase="raise"       ,	UpperCAmelCase=None       ,	):
							return {
							    "roc_auc": roc_auc_score(
							        UpperCAmelCase       ,	UpperCAmelCase       ,	average=UpperCAmelCase       ,	sample_weight=UpperCAmelCase       ,	max_fpr=UpperCAmelCase       ,	multi_class=UpperCAmelCase       ,	labels=UpperCAmelCase       ,	)
							}



