



"""simple docstring"""







import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


A    =	{}
for task, _ in pipeline_test_mapping.items():
			A    =	{'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
A    =	{
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       List[Any]   ):
						"""simple docstring"""






						if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
												return "pt"
						elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
												return "tf"
						elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
												return "flax"
						else:
												return None

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       List[str]    ,		lowerCamelCase_:       List[str]   ):
						"""simple docstring"""






						if PIPELINE_TEST_MAPPING[task].get(lowerCamelCase_    ,		lowerCamelCase_   ) is not None:
												return PIPELINE_TEST_MAPPING[task][framework]

						snake_case      : List[str]							=   pipeline_test_mapping[task]["test"]
						snake_case      : Optional[Any]							=   None

						if framework == "pt":
												snake_case      : int							=   getattr(lowerCamelCase_    ,		"model_mapping"    ,		lowerCamelCase_   )
						elif framework == "tf":
												snake_case      : Dict							=   getattr(lowerCamelCase_    ,		"tf_model_mapping"    ,		lowerCamelCase_   )

						if mapping is not None:
												snake_case      : Union[str, Any]							=   dict(mapping.items()   )

						# cache the results
						snake_case      : List[str]							=   mapping
						return mapping

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       str    ,		lowerCamelCase_:       List[str]   ):
						"""simple docstring"""






						snake_case      : int							=   get_framework(lowerCamelCase_   )
						if framework is None:
												return None
						snake_case      : Any							=   get_mapping_for_task(lowerCamelCase_    ,		lowerCamelCase_   )
						if mapping is None:
												return None

						snake_case      : str							=   list({model_class.config_class for model_class in test_class.all_model_classes}   )
						if len(lowerCamelCase_   ) != 1:
												raise ValueError("There should be exactly one configuration class from `test_class.all_model_classes`."   )

						# This could be a list/tuple of model classes, but it's rare.
						snake_case      : Tuple							=   mapping.get(config_classes[0]    ,		lowerCamelCase_   )
						if isinstance(lowerCamelCase_    ,		(tuple, list)   ):
												snake_case      : int							=   sorted(lowerCamelCase_    ,		key=lambda lowerCamelCase_   : x.__name__   )

						return model_class

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[Any]   ):
						"""simple docstring"""






						snake_case      : int							=   [(task, get_model_for_pipeline_test(lowerCamelCase_    ,		lowerCamelCase_   )) for task in pipeline_test_mapping]
						snake_case      : Any							=   sorted([(task, model) for task, model in mapping if model is not None]    ,		key=lambda lowerCamelCase_   : x[0]   )

						return dict(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Union[str, Any]   ):
						"""simple docstring"""






						snake_case      : str							=   get_framework(lowerCamelCase_   )
						if framework == "pt":
												snake_case      : Optional[int]							=   "torch"
						snake_case      : List[str]							=   "{}"

						snake_case      : str							=   get_pipeline_model_mapping(lowerCamelCase_   )
						if len(lowerCamelCase_   ) == 0:
												return ""

						snake_case      : Union[str, Any]							=   []
						for task, model_classes in mapping.items():
												if isinstance(lowerCamelCase_    ,		(tuple, list)   ):
																		# A list/tuple of model classes
																		snake_case      : Union[str, Any]							=   "(" + ", ".join([x.__name__ for x in model_classes]   ) + ")"
												else:
																		# A single model class
																		snake_case      : List[str]							=   model_classes.__name__
												texts.append(f'''"{task}": {value}'''   )
						snake_case      : Optional[Any]							=   "{" + ", ".join(lowerCamelCase_   ) + "}"
						snake_case      : Tuple							=   f'''pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'''

						return text

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       List[Any]   ):
						"""simple docstring"""






						snake_case      : List[str]							=   {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"}
						if not issubclass(lowerCamelCase_    ,		unittest.TestCase   ):
												return False
						return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]   )   ) > 0

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[Any]   ):
						"""simple docstring"""






						snake_case      : Optional[int]							=   [x for x in get_test_classes(lowerCamelCase_   ) if is_valid_test_class(lowerCamelCase_   )]

						snake_case      : List[str]							=   None
						for test_class in test_classes:
												# If a test class has defined `pipeline_model_mapping`, let's take it
												if getattr(lowerCamelCase_    ,		"pipeline_model_mapping"    ,		lowerCamelCase_   ) is not None:
																		snake_case      : Optional[int]							=   test_class
																		break
    # Take the test class with the shortest name (just a heuristic)
						if target_test_class is None and len(lowerCamelCase_   ) > 0:
												snake_case      : Tuple							=   sorted(lowerCamelCase_    ,		key=lambda lowerCamelCase_   : (len(x.__name__   ), x.__name__)   )[0]

						return target_test_class

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Tuple    ,		lowerCamelCase_:       Dict    ,		lowerCamelCase_:       Union[str, Any]   ):
						"""simple docstring"""






						snake_case      : str							=   start_idx
						for idx, line in enumerate(lines[start_idx:]   ):
												snake_case      : List[str]							=   len(lowerCamelCase_   ) - len(line.lstrip()   )
												if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
																		snake_case      : Dict							=   start_idx + idx
												elif idx > 0 and indent <= indent_level:
																		# Outside the definition block of `pipeline_model_mapping`
																		break

						return end_idx

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Dict    ,		lowerCamelCase_:       Any=False   ):
						"""simple docstring"""






						if getattr(lowerCamelCase_    ,		"pipeline_model_mapping"    ,		lowerCamelCase_   ) is not None:
												if not overwrite:
																		return "", -1

						snake_case      : Optional[Any]							=   get_pipeline_model_mapping_string(lowerCamelCase_   )
						if len(lowerCamelCase_   ) == 0:
												return "", -1
						snake_case      : Dict							=   line_to_add + "\n"

						# The code defined the class `test_class`
						snake_case						, snake_case      : int							=   inspect.getsourcelines(lowerCamelCase_   )
						# `inspect` gives the code for an object, including decorator(s) if any.
						# We (only) need the exact line of the class definition.
						for idx, line in enumerate(lowerCamelCase_   ):
												if line.lstrip().startswith("class "   ):
																		snake_case      : str							=   class_lines[idx:]
																		class_start_line_no += idx
																		break
						snake_case      : Union[str, Any]							=   class_start_line_no + len(lowerCamelCase_   ) - 1

						# The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
						# `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
						# block that appears in a `test_class`.
						snake_case      : List[Any]							=   None
						# The indent level of the line at `class_lines[start_idx]` (if defined)
						snake_case      : List[Any]							=   0
						# To record if `pipeline_model_mapping` is found in `test_class`.
						snake_case      : Optional[int]							=   None
						for idx, line in enumerate(lowerCamelCase_   ):
												if line.strip().startswith("all_model_classes = "   ):
																		snake_case      : List[Any]							=   len(lowerCamelCase_   ) - len(line.lstrip()   )
																		snake_case      : Union[str, Any]							=   idx
												elif line.strip().startswith("all_generative_model_classes = "   ):
																		snake_case      : List[str]							=   len(lowerCamelCase_   ) - len(line.lstrip()   )
																		snake_case      : List[Any]							=   idx
												elif line.strip().startswith("pipeline_model_mapping = "   ):
																		snake_case      : Dict							=   len(lowerCamelCase_   ) - len(line.lstrip()   )
																		snake_case      : Any							=   idx
																		snake_case      : List[str]							=   line
																		break

						if start_idx is None:
												return "", -1
						# Find the ending index (inclusive) of the above found block.
						snake_case      : Any							=   find_block_ending(lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_   )

						# Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
						# `is_timm_available()` instead of `is_torch_available()`.
						# Keep leading and trailing whitespaces
						snake_case      : str							=   re.compile(r"\s(is_\S+?_available\(\))\s"   )
						for line in class_lines[start_idx : end_idx + 1]:
												snake_case      : str							=   r.search(lowerCamelCase_   )
												if backend_condition is not None:
																		# replace the leading and trailing whitespaces to the space character " ".
																		snake_case      : List[str]							=   " " + backend_condition[0][1:-1] + " "
																		snake_case      : Optional[Any]							=   r.sub(lowerCamelCase_    ,		lowerCamelCase_   )
																		break

						if def_line is None:
												# `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
												# `all_model_classes` or `all_generative_model_classes`.
												snake_case      : List[Any]							=   end_idx
						else:
												# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
												snake_case      : int							=   start_idx - 1
												# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
												for idx in range(lowerCamelCase_    ,		end_idx + 1   ):
																		# These lines are going to be removed before writing to the test file.
																		snake_case      : List[Any]							=   None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
						snake_case      : Any							=   [x.__name__ for x in test_class.__bases__]
						if "PipelineTesterMixin" not in parent_classes:
												# Put `PipelineTesterMixin` just before `unittest.TestCase`
												snake_case      : Optional[int]							=   [x for x in parent_classes if x != "TestCase"] + ["PipelineTesterMixin"]
												if "TestCase" in parent_classes:
																		# Here we **assume** the original string is always with `unittest.TestCase`.
																		_parent_classes.append("unittest.TestCase"   )
												snake_case      : Any							=   ", ".join(_parent_classes   )
												for idx, line in enumerate(lowerCamelCase_   ):
																		# Find the ending of the declaration of `test_class`
																		if line.strip().endswith("):"   ):
																								# mark the lines of the declaration of `test_class` to be removed
																								for _idx in range(idx + 1   ):
																														snake_case      : Union[str, Any]							=   None  # noqa
																								break
        # Add the new, one-line, class declaration for `test_class`
												snake_case      : Optional[Any]							=   f'''class {test_class.__name__}({parent_classes}):\n'''

						# Add indentation
						snake_case      : str							=   " " * indent_level + line_to_add
						# Insert `pipeline_model_mapping` to `class_lines`.
						# (The line at `target_idx` should be kept by definition!)
						snake_case      : str							=   class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
						# Remove the lines that are marked to be removed
						snake_case      : List[str]							=   [x for x in class_lines if x is not None]

						# Move from test class to module (in order to write to the test file)
						snake_case      : List[Any]							=   inspect.getsourcelines(inspect.getmodule(lowerCamelCase_   )   )[0]
						# Be careful with the 1-off between line numbers and array indices
						snake_case      : Optional[Any]							=   module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
						snake_case      : Any							=   "".join(lowerCamelCase_   )

						snake_case      : List[str]							=   inspect.getsourcefile(lowerCamelCase_   )
						with open(lowerCamelCase_    ,		"w"    ,		encoding="UTF-8"    ,		newline="\n"   ) as fp:
												fp.write(lowerCamelCase_   )

						return line_to_add

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[Any]    ,		lowerCamelCase_:       List[str]=False   ):
						"""simple docstring"""






						snake_case      : Any							=   find_test_class(lowerCamelCase_   )
						if test_class:
												add_pipeline_model_mapping(lowerCamelCase_    ,		overwrite=lowerCamelCase_   )


if __name__ == "__main__":
			A    =	argparse.ArgumentParser()
			parser.add_argument(
			    '--test_file', type=str, help='A path to the test file, starting with the repository\'s `tests` directory.'
			)
			parser.add_argument(
			    '--all',
			    action='store_true',
			    help='If to check and modify all test files.',
			)
			parser.add_argument(
			    '--overwrite',
			    action='store_true',
			    help='If to overwrite a test class if it has already defined `pipeline_model_mapping`.',
			)
			A    =	parser.parse_args()

			if not args.all and not args.test_file:
						raise ValueError('Please specify either `test_file` or pass `--all` to check/modify all test files.')
			elif args.all and args.test_file:
						raise ValueError('Only one of `--test_file` and `--all` could be specified.')

			A    =	[]
			if args.test_file:
						A    =	[args.test_file]
			else:
						A    =	os.path.join('tests', 'models', '**', 'test_modeling_*.py')
						for test_file in glob.glob(pattern):
									# `Flax` is not concerned at this moment
									if not test_file.startswith('test_modeling_flax_'):
												test_files.append(test_file)

			for test_file in test_files:
						if test_file in TEST_FILE_TO_IGNORE:
									print(F'''[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.''')
									continue
						add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)








"""simple docstring"""







from sklearn.metrics import accuracy_score

import datasets


A    =	'\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


A    =	'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


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

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



			def 							__lowercase				( self       :							List[Any]		)       ->				List[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.accuracy_score.html"]      ,  )



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Optional[int]      ,  _lowercase       :							Tuple      ,  _lowercase       :							Union[str, Any]=True      ,  _lowercase       :							int=None		)       ->				Any:
									return {
									    "accuracy": float(
									        accuracy_score(_lowercase      ,  _lowercase      ,  normalize=_lowercase      ,  sample_weight=_lowercase		)		)
									}



