






'''simple docstring'''







from sklearn.metrics import fa_score

import datasets


_A			:    Union[str, Any]					  ='''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


_A			:    List[str]					  ='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    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.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    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\'`.

        - \'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.
        - \'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. This option 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.

Returns:
    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.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> 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])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


_A			:    List[Any]					  ='''
@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 _lowercase			(       datasets.Metric	):




							def 	lowerCamelCase_       (							self:       Dict  ):
														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    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       Dict=1    ,							UpperCamelCase__:       Tuple="binary"    ,							UpperCamelCase__:       Dict=None  ):
														lowerCamelCase__      :			str 						= fa_score(
														    UpperCamelCase__    ,							UpperCamelCase__    ,							labels=UpperCamelCase__    ,							pos_label=UpperCamelCase__    ,							average=UpperCamelCase__    ,							sample_weight=UpperCamelCase__  )
														return {"f1": float(UpperCamelCase__  ) if score.size == 1 else score}









'''simple docstring'''







import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Optional[int] 						= dataset
														lowerCamelCase__      :			Optional[int] 						= process
														lowerCamelCase__      :			List[str] 						= params




							def __len__(							self:       List[str]  ):
														return len(self.dataset  )




							def __getitem__(							self:       Any    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Dict 						= self.dataset[i]
														lowerCamelCase__      :			Union[str, Any] 						= self.process(UpperCamelCase__    ,							**self.params  )
														return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Any=None  ):
														lowerCamelCase__      :			int 						= loader
														lowerCamelCase__      :			str 						= infer
														lowerCamelCase__      :			Optional[int] 						= params
														if loader_batch_size == 1:
																					# Let's spare some time by deactivating altogether
																					lowerCamelCase__      :			Optional[int] 						= None
														lowerCamelCase__      :			int 						= loader_batch_size

														# Internal bookkeeping
														lowerCamelCase__      :			Optional[Any] 						= None
														lowerCamelCase__      :			Optional[Any] 						= None




							def __len__(							self:       Dict  ):
														return len(self.loader  )




							def __iter__(							self:       Optional[int]  ):
														lowerCamelCase__      :			List[Any] 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Any  ):
														if isinstance(self._loader_batch_data    ,							torch.Tensor  ):
																					# Batch data is simple tensor, just fetch the slice
																					lowerCamelCase__      :			str 						= self._loader_batch_data[self._loader_batch_index]
														else:
																					# Batch data is assumed to be BaseModelOutput (or dict)
																					lowerCamelCase__      :			int 						= {}
																					for k, element in self._loader_batch_data.items():
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Convert ModelOutput to tuple first
																																			lowerCamelCase__      :			str 						= element.to_tuple()
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			Tuple 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			str 						= tuple(np.expand_dims(el[self._loader_batch_index]    ,							0  ) for el in element  )
																																			continue
																												if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Those are stored as lists of tensors so need specific unbatching.
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			List[str] 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			int 						= tuple(np.expand_dims(el[self._loader_batch_index]    ,							0  ) for el in element  )
																																			continue
																												if element is None:
																																			# This can happen for optional data that get passed around
																																			lowerCamelCase__      :			List[str] 						= None
																												elif isinstance(element[self._loader_batch_index]    ,							torch.Tensor  ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers

																																			lowerCamelCase__      :			Optional[Any] 						= element[self._loader_batch_index].unsqueeze(0  )
																												elif isinstance(element[self._loader_batch_index]    ,							np.ndarray  ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers
																																			lowerCamelCase__      :			int 						= np.expand_dims(element[self._loader_batch_index]    ,							0  )
																												else:
																																			# This is typically a list, so no need to `unsqueeze`.
																																			lowerCamelCase__      :			str 						= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																					lowerCamelCase__      :			Optional[int] 						= self._loader_batch_data.__class__(UpperCamelCase__  )
														self._loader_batch_index += 1
														return result




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					# We are currently unrolling a batch so we just need to return
																					# the current item within a batch
																					return self.loader_batch_item()

														# We're out of items within a batch
														lowerCamelCase__      :			Optional[Any] 						= next(self.iterator  )
														lowerCamelCase__      :			List[str] 						= self.infer(UpperCamelCase__    ,							**self.params  )
														# We now have a batch of "inferred things".
														if self.loader_batch_size is not None:
																					# Try to infer the size of the batch
																					if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																												lowerCamelCase__      :			Optional[Any] 						= processed
																					else:
																												lowerCamelCase__      :			Union[str, Any] 						= list(processed.keys()  )[0]
																												lowerCamelCase__      :			Any 						= processed[key]
																					if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																												lowerCamelCase__      :			Any 						= len(UpperCamelCase__  )
																					else:
																												lowerCamelCase__      :			List[str] 						= first_tensor.shape[0]
																					if 0 < observed_batch_size < self.loader_batch_size:
																												# could be last batch so we can't unroll as many
																												# elements.
																												lowerCamelCase__      :			Union[str, Any] 						= observed_batch_size
																					# Setting internal index to unwrap the batch
																					lowerCamelCase__      :			List[Any] 						= processed
																					lowerCamelCase__      :			List[Any] 						= 0
																					return self.loader_batch_item()
														else:
																					# We're not unrolling batches
																					return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       List[Any]=None  ):
														super().__init__(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def __iter__(							self:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= iter(self.loader  )
														lowerCamelCase__      :			int 						= None
														return self




							def 	lowerCamelCase_       (							self:       str  ):
														if self.subiterator is None:
																					lowerCamelCase__      :			Optional[Any] 						= self.infer(next(self.iterator  )    ,							**self.params  )
														try:
																					# Try to return next item
																					lowerCamelCase__      :			Tuple 						= next(self.subiterator  )
														except StopIteration:
																					# When a preprocess iterator ends, we can start lookig at the next item
																					# ChunkIterator will keep feeding until ALL elements of iterator
																					# all have created their subiterator and have been iterating against.
																					#
																					# Another way to look at it, is we're basically flattening lists of lists
																					# into a single list, but with generators
																					lowerCamelCase__      :			Any 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					lowerCamelCase__      :			Union[str, Any] 						= next(self.subiterator  )
														return processed



class _lowercase			(       _lowercase	):




							def __iter__(							self:       List[Any]  ):
														lowerCamelCase__      :			int 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Tuple  ):
														# Extremely similar to PipelineIterator in its unpacking mechanism
														# BUT, we have an extra required item which is the presence of `is_last`
														# That is because everything is flattened by `PipelineChunkIterator` we
														# need to keep track of how to regroup here in the original `process`
														# boundaries so that `process` and `postprocess` see the same data.

														# This iterator accumulates items (possibly while unbatching) until it
														# its a `is_last` and then just passes it on to the caller.
														lowerCamelCase__      :			List[str] 						= False
														lowerCamelCase__      :			Union[str, Any] 						= []
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					while self._loader_batch_index < self.loader_batch_size:
																												lowerCamelCase__      :			Any 						= self.loader_batch_item()
																												lowerCamelCase__      :			Tuple 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
																												if is_last:
																																			return accumulator

														while not is_last:
																					lowerCamelCase__      :			str 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					if self.loader_batch_size is not None:
																												if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																																			lowerCamelCase__      :			Dict 						= processed
																												else:
																																			lowerCamelCase__      :			Dict 						= list(processed.keys()  )[0]
																																			lowerCamelCase__      :			Dict 						= processed[key]
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			lowerCamelCase__      :			List[Any] 						= len(UpperCamelCase__  )
																												else:
																																			lowerCamelCase__      :			Dict 						= first_tensor.shape[0]
																												if 0 < observed_batch_size < self.loader_batch_size:
																																			# could be last batch so we can't unroll as many
																																			# elements.
																																			lowerCamelCase__      :			str 						= observed_batch_size
																												lowerCamelCase__      :			str 						= processed
																												lowerCamelCase__      :			Optional[int] 						= 0
																												while self._loader_batch_index < self.loader_batch_size:
																																			lowerCamelCase__      :			List[Any] 						= self.loader_batch_item()
																																			lowerCamelCase__      :			str 						= item.pop("""is_last"""  )
																																			accumulator.append(UpperCamelCase__  )
																																			if is_last:
																																										return accumulator
																					else:
																												lowerCamelCase__      :			Optional[Any] 						= processed
																												lowerCamelCase__      :			Optional[int] 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
														return accumulator



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			Union[str, Any] 						= dataset
														lowerCamelCase__      :			str 						= key




							def __len__(							self:       Optional[Any]  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Any  ):
														return self.dataset[i][self.key]



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			str 						= dataset
														lowerCamelCase__      :			Dict 						= keya
														lowerCamelCase__      :			List[str] 						= keya




							def __len__(							self:       str  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Union[str, Any]  ):
														return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

