import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map




def       __lowerCAmelCase      (						A_   : Union[dict, list, tuple, torch.Tensor]					)    ->  List[Tuple[int, ...]]:
  __UpperCAmelCase						    =       []
  if isinstance(A_						,	A_					):
    for v in tree.values():
      shapes.extend(_fetch_dims(A_					)					)
  elif isinstance(A_						,	(list, tuple)					):
    for t in tree:
      shapes.extend(_fetch_dims(A_					)					)
  elif isinstance(A_						,	torch.Tensor					):
    shapes.append(tree.shape					)
  else:
    raise ValueError("Not supported"					)

  return shapes




@torch.jit.ignore
def       __lowerCAmelCase      (						A_   : int						,	A_   : Tuple[int, ...]					)    ->  Tuple[int, ...]:
  __UpperCAmelCase						    =       []
  for d in reversed(A_					):
    idx.append(flat_idx % d					)
    __UpperCAmelCase						    =       flat_idx // d

  return tuple(reversed(A_					)					)




@torch.jit.ignore
def       __lowerCAmelCase      (						A_   : Sequence[int]						,	A_   : Sequence[int]						,	A_   : Sequence[int]						,	A_   : Optional[Sequence[bool]] = None						,	A_   : Optional[Sequence[bool]] = None						,	)    ->  List[Tuple[slice, ...]]:

  # start_edges and end_edges both indicate whether, starting from any given
  # dimension, the start/end index is at the top/bottom edge of the
  # corresponding tensor, modeled as a tree
  def reduce_edge_list(A_   : List[bool]					) -> None:
    __UpperCAmelCase						    =       True
    for i in range(len(A_					)					):
      __UpperCAmelCase						    =       -1 * (i + 1)
      l[reversed_idx] &= tally
      __UpperCAmelCase						    =       l[reversed_idx]

  if start_edges is None:
    __UpperCAmelCase						    =       [s == 0 for s in start]
    reduce_edge_list(A_					)
  if end_edges is None:
    __UpperCAmelCase						    =       [e == (d - 1) for e, d in zip(A_						,	A_					)]
    reduce_edge_list(A_					)

  # Base cases. Either start/end are empty and we're done, or the final,
  # one-dimensional tensor can be simply sliced
  if len(A_					) == 0:
    return [()]
  elif len(A_					) == 1:
    return [(slice(start[0]						,	end[0] + 1					),)]

  __UpperCAmelCase						    =       []
  __UpperCAmelCase						    =       []

  # Dimensions common to start and end can be selected directly
  for s, e in zip(A_						,	A_					):
    if s == e:
      path_list.append(slice(A_						,	s + 1					)					)
    else:
      break

  __UpperCAmelCase						    =       tuple(A_					)
  __UpperCAmelCase						    =       len(A_					)

  # start == end, and we're done
  if divergence_idx == len(A_					):
    return [path]

  def upper() -> Tuple[Tuple[slice, ...], ...]:
    assert start_edges is not None
    assert end_edges is not None

    __UpperCAmelCase						    =       start[divergence_idx]
    return tuple(
        path + (slice(A_						,	sdi + 1					),) + s
        for s in _get_minimal_slice_set(
            start[divergence_idx + 1 :]						,	[d - 1 for d in dims[divergence_idx + 1 :]]						,	dims[divergence_idx + 1 :]						,	start_edges=start_edges[divergence_idx + 1 :]						,	end_edges=[True for _ in end_edges[divergence_idx + 1 :]]						,	)					)

  def lower() -> Tuple[Tuple[slice, ...], ...]:
    assert start_edges is not None
    assert end_edges is not None

    __UpperCAmelCase						    =       end[divergence_idx]
    return tuple(
        path + (slice(A_						,	edi + 1					),) + s
        for s in _get_minimal_slice_set(
            [0 for _ in start[divergence_idx + 1 :]]						,	end[divergence_idx + 1 :]						,	dims[divergence_idx + 1 :]						,	start_edges=[True for _ in start_edges[divergence_idx + 1 :]]						,	end_edges=end_edges[divergence_idx + 1 :]						,	)					)

  # If both start and end are at the edges of the subtree rooted at
  # divergence_idx, we can just select the whole subtree at once
  if start_edges[divergence_idx] and end_edges[divergence_idx]:
    slices.append(path + (slice(start[divergence_idx]						,	end[divergence_idx] + 1					),)					)
  # If just start is at the edge, we can grab almost all of the subtree,
  # treating only the ragged bottom edge as an edge case
  elif start_edges[divergence_idx]:
    slices.append(path + (slice(start[divergence_idx]						,	end[divergence_idx]					),)					)
    slices.extend(lower()					)
  # Analogous to the previous case, but the top is ragged this time
  elif end_edges[divergence_idx]:
    slices.extend(upper()					)
    slices.append(path + (slice(start[divergence_idx] + 1						,	end[divergence_idx] + 1					),)					)
  # If both sides of the range are ragged, we need to handle both sides
  # separately. If there's contiguous meat in between them, we can index it
  # in one big chunk
  else:
    slices.extend(upper()					)
    __UpperCAmelCase						    =       end[divergence_idx] - start[divergence_idx]
    if middle_ground > 1:
      slices.append(path + (slice(start[divergence_idx] + 1						,	end[divergence_idx]					),)					)
    slices.extend(lower()					)

  return slices




@torch.jit.ignore
def       __lowerCAmelCase      (						A_   : torch.Tensor						,	A_   : int						,	A_   : int						,	A_   : int					)    ->  torch.Tensor:
  __UpperCAmelCase						    =       t.shape[:no_batch_dims]
  __UpperCAmelCase						    =       list(_flat_idx_to_idx(A_						,	A_					)					)
  # _get_minimal_slice_set is inclusive
  __UpperCAmelCase						    =       list(_flat_idx_to_idx(flat_end - 1						,	A_					)					)

  # Get an ordered list of slices to perform
  __UpperCAmelCase						    =       _get_minimal_slice_set(
      A_						,	A_						,	A_						,	)

  __UpperCAmelCase						    =       [t[s] for s in slices]

  return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]					) for s in sliced_tensors]					)




def       __lowerCAmelCase      (						A_   : Callable						,	A_   : Dict[str, Any]						,	A_   : int						,	A_   : int						,	A_   : bool = False						,	A_   : Any = None						,	A_   : bool = False						,	)    ->  Any:
  if not (len(A_					) > 0):
    raise ValueError("Must provide at least one input"					)

  __UpperCAmelCase						    =       [shape[:no_batch_dims] for shape in _fetch_dims(A_					)]
  __UpperCAmelCase						    =       tuple([max(A_					) for s in zip(*A_					)]					)

  def _prep_inputs(A_   : torch.Tensor					) -> torch.Tensor:
    if not low_mem:
      if not sum(t.shape[:no_batch_dims]					) == no_batch_dims:
        __UpperCAmelCase						    =       t.expand(orig_batch_dims + t.shape[no_batch_dims:]					)
      __UpperCAmelCase						    =       t.reshape(-1						,	*t.shape[no_batch_dims:]					)
    else:
      __UpperCAmelCase						    =       t.expand(orig_batch_dims + t.shape[no_batch_dims:]					)
    return t

  __UpperCAmelCase						    =       tensor_tree_map(_prep_inputs						,	A_					)
  __UpperCAmelCase						    =       None
  if _out is not None:
    __UpperCAmelCase						    =       tensor_tree_map(lambda A_					: t.view([-1] + list(t.shape[no_batch_dims:]					)					)						,	_out					)

  __UpperCAmelCase						    =       1
  for d in orig_batch_dims:
    flat_batch_dim *= d

  __UpperCAmelCase						    =       flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

  def _select_chunk(A_   : torch.Tensor					) -> torch.Tensor:
    return t[i : i + chunk_size] if t.shape[0] != 1 else t

  __UpperCAmelCase						    =       0
  __UpperCAmelCase						    =       prepped_outputs
  for _ in range(A_					):
    # Chunk the input
    if not low_mem:
      __UpperCAmelCase						    =       _select_chunk
    else:
      __UpperCAmelCase						    =       partial(
          _chunk_slice						,	flat_start=A_						,	flat_end=min(A_						,	i + chunk_size					)						,	no_batch_dims=len(A_					)						,	)

    __UpperCAmelCase						    =       tensor_tree_map(A_						,	A_					)

    # Run the layer on the chunk
    __UpperCAmelCase						    =       layer(**A_					)

    # Allocate space for the output
    if out is None:
      __UpperCAmelCase						    =       tensor_tree_map(lambda A_					: t.new_zeros((flat_batch_dim,) + t.shape[1:]					)						,	A_					)

    # Put the chunk in its pre-allocated space
    if isinstance(A_						,	A_					):

      def assign(A_   : dict						,	A_   : dict					) -> None:
        for k, v in da.items():
          if isinstance(A_						,	A_					):
            assign(A_						,	da[k]					)
          else:
            if _add_into_out:
              v[i : i + chunk_size] += da[k]
            else:
              __UpperCAmelCase						    =       da[k]

      assign(A_						,	A_					)
    elif isinstance(A_						,	A_					):
      for xa, xa in zip(A_						,	A_					):
        if _add_into_out:
          xa[i : i + chunk_size] += xa
        else:
          __UpperCAmelCase						    =       xa
    elif isinstance(A_						,	torch.Tensor					):
      if _add_into_out:
        out[i : i + chunk_size] += output_chunk
      else:
        __UpperCAmelCase						    =       output_chunk
    else:
      raise ValueError("Not supported"					)

    i += chunk_size

  __UpperCAmelCase						    =       tensor_tree_map(lambda A_					: t.view(orig_batch_dims + t.shape[1:]					)						,	A_					)

  return out





class 			UpperCAmelCase__    :



  """simple docstring"""






  def __init__(				self:			Tuple				,				__lowerCAmelCase:			int = 512				,				) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       max_chunk_size
    __UpperCAmelCase						    =       None
    __UpperCAmelCase						    =       None






  def 	_UpperCAmelCase				(				self:			Optional[int]				,				__lowerCAmelCase:			Callable				,				__lowerCAmelCase:			tuple				,				__lowerCAmelCase:			int   ) -> int:




    '''simple docstring'''




    logging.info("Tuning chunk size..."   )

    if min_chunk_size >= self.max_chunk_size:
      return min_chunk_size

    __UpperCAmelCase						    =       [2**l for l in range(int(math.log(self.max_chunk_size				,				2   )   ) + 1   )]
    __UpperCAmelCase						    =       [c for c in candidates if c > min_chunk_size]
    __UpperCAmelCase						    =       [min_chunk_size] + candidates
    candidates[-1] += 4

    def test_chunk_size(__lowerCAmelCase:			int   ) -> bool:
      try:
        with torch.no_grad():
          fn(*__lowerCAmelCase				,				chunk_size=__lowerCAmelCase   )
        return True
      except RuntimeError:
        return False

    __UpperCAmelCase						    =       0
    __UpperCAmelCase						    =       len(__lowerCAmelCase   ) - 1
    while i > min_viable_chunk_size_index:
      __UpperCAmelCase						    =       test_chunk_size(candidates[i]   )
      if not viable:
        __UpperCAmelCase						    =       (min_viable_chunk_size_index + i) // 2
      else:
        __UpperCAmelCase						    =       i
        __UpperCAmelCase						    =       (i + len(__lowerCAmelCase   ) - 1) // 2

    return candidates[min_viable_chunk_size_index]






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			Iterable				,				__lowerCAmelCase:			Iterable   ) -> bool:




    '''simple docstring'''




    __UpperCAmelCase						    =       True
    for aa, aa in zip(__lowerCAmelCase				,				__lowerCAmelCase   ):
      assert type(__lowerCAmelCase   ) == type(__lowerCAmelCase   )
      if isinstance(__lowerCAmelCase				,				(list, tuple)   ):
        consistent &= self._compare_arg_caches(__lowerCAmelCase				,				__lowerCAmelCase   )
      elif isinstance(__lowerCAmelCase				,				__lowerCAmelCase   ):
        __UpperCAmelCase						    =       [v for _, v in sorted(aa.items()				,				key=lambda __lowerCAmelCase   : x[0]   )]
        __UpperCAmelCase						    =       [v for _, v in sorted(aa.items()				,				key=lambda __lowerCAmelCase   : x[0]   )]
        consistent &= self._compare_arg_caches(__lowerCAmelCase				,				__lowerCAmelCase   )
      else:
        consistent &= aa == aa

    return consistent






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			Callable				,				__lowerCAmelCase:			tuple				,				__lowerCAmelCase:			int				,				) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       True
    __UpperCAmelCase						    =       tree_map(lambda __lowerCAmelCase   : a.shape if isinstance(__lowerCAmelCase				,				torch.Tensor   ) else a				,				__lowerCAmelCase				,				__lowerCAmelCase   )
    if self.cached_arg_data is not None:
      # If args have changed shape/value, we need to re-tune
      assert len(self.cached_arg_data   ) == len(__lowerCAmelCase   )
      __UpperCAmelCase						    =       self._compare_arg_caches(self.cached_arg_data				,				__lowerCAmelCase   )
    else:
      # Otherwise, we can reuse the precomputed value
      __UpperCAmelCase						    =       False

    if not consistent:
      __UpperCAmelCase						    =       self._determine_favorable_chunk_size(
          __lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase				,				)
      __UpperCAmelCase						    =       arg_data

    assert self.cached_chunk_size is not None

    return self.cached_chunk_size




from sklearn.metrics import fa_score

import datasets


a_							=      """
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_							=      """
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_							=      """
@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 			UpperCAmelCase__    (		datasets.Metric					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''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 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Optional[Any]=1				,				__lowerCAmelCase:			List[Any]="binary"				,				__lowerCAmelCase:			List[Any]=None   ) -> Optional[int]:




    '''simple docstring'''




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



