
"""simple docstring"""






import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase__ :       Any						     =			logging.get_logger(__name__)





def __A						(   a_ :							List[Any]     )->							Union[str, Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= r'''\w+[.]\d+'''
		SCREAMING_SNAKE_CASE       :     Any				= re.findall(a_		,		a_     )
		for pat in pats:
				SCREAMING_SNAKE_CASE       :     Any				= key.replace(a_		,		'''_'''.join(pat.split('''.'''     )     )     )
		return key





def __A						(   a_ :							Dict		,		a_ :							Optional[int]		,		a_ :							Dict     )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= pt_tuple_key[:-1] + ('''scale''',)
		if (
		    any('''norm''' in str_ for str_ in pt_tuple_key     )
		    and (pt_tuple_key[-1] == "bias")
		    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
		    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
		):
				SCREAMING_SNAKE_CASE       :     List[str]				= pt_tuple_key[:-1] + ('''scale''',)
				return renamed_pt_tuple_key, pt_tensor
		elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
				SCREAMING_SNAKE_CASE       :     str				= pt_tuple_key[:-1] + ('''scale''',)
				return renamed_pt_tuple_key, pt_tensor

		# embedding
		if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
				SCREAMING_SNAKE_CASE       :     Dict				= pt_tuple_key[:-1] + ('''embedding''',)
				return renamed_pt_tuple_key, pt_tensor

		# conv layer
		SCREAMING_SNAKE_CASE       :     int				= pt_tuple_key[:-1] + ('''kernel''',)
		if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
				SCREAMING_SNAKE_CASE       :     Dict				= pt_tensor.transpose(2		,		3		,		1		,		0     )
				return renamed_pt_tuple_key, pt_tensor

		# linear layer
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pt_tuple_key[:-1] + ('''kernel''',)
		if pt_tuple_key[-1] == "weight":
				SCREAMING_SNAKE_CASE       :     int				= pt_tensor.T
				return renamed_pt_tuple_key, pt_tensor

		# old PyTorch layer norm weight
		SCREAMING_SNAKE_CASE       :     Optional[int]				= pt_tuple_key[:-1] + ('''weight''',)
		if pt_tuple_key[-1] == "gamma":
				return renamed_pt_tuple_key, pt_tensor

		# old PyTorch layer norm bias
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pt_tuple_key[:-1] + ('''bias''',)
		if pt_tuple_key[-1] == "beta":
				return renamed_pt_tuple_key, pt_tensor

		return pt_tuple_key, pt_tensor





def __A						(   a_ :							Optional[Any]		,		a_ :							int		,		a_ :							List[Any]=42     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {k: v.numpy() for k, v in pt_state_dict.items()}

		# Step 2: Since the model is stateless, get random Flax params
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= flax_model.init_weights(PRNGKey(a_     )     )

		SCREAMING_SNAKE_CASE       :     int				= flatten_dict(a_     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= {}

		# Need to change some parameters name to match Flax names
		for pt_key, pt_tensor in pt_state_dict.items():
				SCREAMING_SNAKE_CASE       :     List[Any]				= rename_key(a_     )
				SCREAMING_SNAKE_CASE       :     List[str]				= tuple(renamed_pt_key.split('''.'''     )     )

				# Correctly rename weight parameters
				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= rename_key_and_reshape_tensor(a_		,		a_		,		a_     )

				if flax_key in random_flax_state_dict:
						if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
								raise ValueError(
								    F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
								    F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."     )

        # also add unexpected weight so that warning is thrown
				SCREAMING_SNAKE_CASE       :     Dict				= jnp.asarray(a_     )

		return unflatten_dict(a_     )







"""simple docstring"""






from sklearn.metrics import fa_score

import datasets


lowerCamelCase__ :       List[Any]						     =			"\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


lowerCamelCase__ :       str						     =			"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. 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 `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - '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. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results['f1'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


lowerCamelCase__ :       int						     =			"\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 				lowercase__(				datasets.Metric   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




			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.f1_score.html''']		, )







	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :int=None		, lowerCamelCase_    :str=1		, lowerCamelCase_    :Union[str, Any]="binary"		, lowerCamelCase_    :Dict=None					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= fa_score(
			    lowerCamelCase_		, lowerCamelCase_		, labels=lowerCamelCase_		, pos_label=lowerCamelCase_		, average=lowerCamelCase_		, sample_weight=lowerCamelCase_					)
			return {"f1": float(lowerCamelCase_					) if score.size == 1 else score}





