




"""simple docstring"""


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
_A													=			logging.get_logger()


@dataclass
class _lowerCamelCase				:
   _lowerCamelCase				:int	      =	42
   _lowerCamelCase				:List[Any]	      =	field(default_factory=__snake_case						)
   _lowerCamelCase				:List[str]	      =	field(default_factory=__snake_case						)






   def        _lowerCAmelCase    (		self   :		Dict    ,	UpperCamelCase   :		Optional[Any]    ,	UpperCamelCase   :		Any    ,	UpperCamelCase   :		Tuple    ) ->						Tuple:

         """simple docstring"""
         lowerCAmelCase__			: str  					=       len(list(m.modules()    )    ) == 1 or isinstance(a_    ,	nn.Convad    ) or isinstance(a_    ,	nn.BatchNormad    )
         if has_not_submodules:
               self.traced.append(a_    )






   def __call__(		self   :		Union[str, Any]    ,	UpperCamelCase   :		Union[str, Any]    ) ->						List[Any]:

         """simple docstring"""

         for m in self.module.modules():
               self.handles.append(m.register_forward_hook(self._forward_hook    )    )
         self.module(a_    )
         [x.remove() for x in self.handles]
         return self






   @property
   def        _lowerCAmelCase    (		self   :		int    ) ->						Dict:

         """simple docstring"""
         return list(filter(lambda UpperCamelCase    : len(list(x.state_dict().keys()    )    ) > 0    ,	self.traced    )    )






@dataclass
class _lowerCamelCase				:
   _lowerCamelCase				:List[Any]	      =	42
   _lowerCamelCase				:Optional[Any]	      =	42
   _lowerCamelCase				:List[Any]	      =	1
   _lowerCamelCase				:int	      =	field(default_factory=__snake_case						)
   _lowerCamelCase				:str	      =	field(default_factory=__snake_case						)
   _lowerCamelCase				:List[Any]	      =	True






   def __call__(		self   :		Dict    ,	UpperCamelCase   :		Union[str, Any]    ) ->						List[str]:

         """simple docstring"""
         lowerCAmelCase__			: int  					=       Tracker(self.dest    )(a_    ).parametrized
         lowerCAmelCase__			: Optional[int]  					=       Tracker(self.src    )(a_    ).parametrized

         lowerCAmelCase__			: Union[str, Any]  					=       list(filter(lambda UpperCamelCase    : type(a_    ) not in self.src_skip    ,	a_    )    )
         lowerCAmelCase__			: Dict  					=       list(filter(lambda UpperCamelCase    : type(a_    ) not in self.dest_skip    ,	a_    )    )

         if len(a_    ) != len(a_    ) and self.raise_if_mismatch:
               raise Exception(
                   f"""Numbers of operations are different. Source module has {len(a_    )} operations while"""
                   f""" destination module has {len(a_    )}."""    )

         for dest_m, src_m in zip(a_    ,	a_    ):
               dest_m.load_state_dict(src_m.state_dict()    )
               if self.verbose == 1:
                     print(f"""Transfered from={src_m} to={dest_m}"""    )






class _lowerCamelCase				(     nn.Module						):






   def __init__(		self   :		Any    ,	UpperCamelCase   :		List[Any]    ) ->						Union[str, Any]:

         """simple docstring"""
         super().__init__()

         lowerCAmelCase__			: List[Tuple[str, nn.Module]]  					=       []
         # - get the stem
         feature_blocks.append(("""conv1""", model.stem)    )
         # - get all the feature blocks
         for k, v in model.trunk_output.named_children():
               assert k.startswith("""block"""    ), f"""Unexpected layer name {k}"""
               lowerCAmelCase__			: Optional[int]  					=       len(a_    ) + 1
               feature_blocks.append((f"""res{block_index}""", v)    )

         lowerCAmelCase__			: List[Any]  					=       nn.ModuleDict(a_    )






   def        _lowerCAmelCase    (		self   :		str    ,	UpperCamelCase   :		Dict    ) ->						List[Any]:

         """simple docstring"""
         return get_trunk_forward_outputs(
             a_    ,	out_feat_keys=a_    ,	feature_blocks=self._feature_blocks    ,	)






class _lowerCamelCase				(     __snake_case						):






   def        _lowerCAmelCase    (		self   :		Optional[Any]    ,	UpperCamelCase   :		int    ) ->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__			: Union[str, Any]  					=       x.split("""-"""    )
         return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]    )






   def __getitem__(		self   :		List[str]    ,	UpperCamelCase   :		Tuple    ) ->						Union[str, Any]:

         """simple docstring"""

         if x not in self:
               lowerCAmelCase__			: Tuple  					=       self.convert_name_to_timm(a_    )
               lowerCAmelCase__			: str  					=       partial(lambda: (timm.create_model(a_    ,	pretrained=a_    ).eval(), None)    )

         else:
               lowerCAmelCase__			: Optional[int]  					=       super().__getitem__(a_    )

         return val






class _lowerCamelCase				(     __snake_case						):






   def __getitem__(		self   :		str    ,	UpperCamelCase   :		Union[str, Any]    ) ->						Union[str, Any]:

         """simple docstring"""

         if "seer" in x and "in1k" not in x:
               lowerCAmelCase__			: List[Any]  					=       RegNetModel
         else:
               lowerCAmelCase__			: List[str]  					=       RegNetForImageClassification
         return val






def        lowercase_      ( __UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase      )						->				Optional[Any]:





      for from_key, to_key in keys:
            lowerCAmelCase__			: Union[str, Any]  					=       from_state_dict[from_key].clone()
            print(f"""Copied key={from_key} to={to_key}"""      )
      return to_state_dict



def        lowercase_      ( __UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase = True				,		)						->				Dict:
      print(f"""Converting {name}..."""      )
      with torch.no_grad():
            lowerCAmelCase__			: Optional[int]  					=       from_model_func()
            lowerCAmelCase__			: Tuple  					=       our_model_func(_snake_case      ).eval()
            lowerCAmelCase__			: List[Any]  					=       ModuleTransfer(src=_snake_case				,		dest=_snake_case				,		raise_if_mismatch=_snake_case      )
            lowerCAmelCase__			: Union[str, Any]  					=       torch.randn((1, 3, 224, 224)      )
            module_transfer(_snake_case      )

      if from_state_dict is not None:
            lowerCAmelCase__			: int  					=       []
            # for seer - in1k finetuned we have to manually copy the head
            if "seer" in name and "in1k" in name:
                  lowerCAmelCase__			: Tuple  					=       [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
            lowerCAmelCase__			: Union[str, Any]  					=       manually_copy_vissl_head(_snake_case				,		our_model.state_dict()				,		_snake_case      )
            our_model.load_state_dict(_snake_case      )

      lowerCAmelCase__			: List[Any]  					=       our_model(_snake_case				,		output_hidden_states=_snake_case      )
      lowerCAmelCase__			: Union[str, Any]  					=       (
          our_outputs.logits if isinstance(_snake_case				,		_snake_case      ) else our_outputs.last_hidden_state
      )

      lowerCAmelCase__			: Optional[Any]  					=       from_model(_snake_case      )
      lowerCAmelCase__			: Optional[Any]  					=       from_output[-1] if type(_snake_case      ) is list else from_output

      # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
      if "seer" in name and "in1k" in name:
            lowerCAmelCase__			: str  					=       our_outputs.hidden_states[-1]

      assert torch.allclose(_snake_case				,		_snake_case      ), "The model logits don't match the original one."

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / name				,		commit_message="""Add model"""				,		use_temp_dir=_snake_case				,		)

            lowerCAmelCase__			: Any  					=       224 if '''seer''' not in name else 384
            # we can use the convnext one
            lowerCAmelCase__			: Dict  					=       AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				,		size=_snake_case      )
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / name				,		commit_message="""Add image processor"""				,		use_temp_dir=_snake_case				,		)

            print(f"""Pushed {name}"""      )



def        lowercase_      ( __UpperCAmelCase				,		__UpperCAmelCase = None				,		__UpperCAmelCase = True      )						->				Any:
      lowerCAmelCase__			: Union[str, Any]  					=       '''imagenet-1k-id2label.json'''
      lowerCAmelCase__			: Optional[Any]  					=       1000
      lowerCAmelCase__			: int  					=       (1, num_labels)

      lowerCAmelCase__			: Optional[Any]  					=       '''huggingface/label-files'''
      lowerCAmelCase__			: Optional[int]  					=       num_labels
      lowerCAmelCase__			: str  					=       json.load(open(cached_download(hf_hub_url(_snake_case				,		_snake_case				,		repo_type="""dataset"""      )      )				,		"""r"""      )      )
      lowerCAmelCase__			: str  					=       {int(_snake_case      ): v for k, v in idalabel.items()}

      lowerCAmelCase__			: Union[str, Any]  					=       idalabel
      lowerCAmelCase__			: int  					=       {v: k for k, v in idalabel.items()}

      lowerCAmelCase__			: int  					=       partial(_snake_case				,		num_labels=_snake_case				,		idalabel=_snake_case				,		labelaid=_snake_case      )

      lowerCAmelCase__			: int  					=       {
          '''regnet-x-002''': ImageNetPreTrainedConfig(
              depths=[1, 1, 4, 7]				,		hidden_sizes=[24, 56, 152, 368]				,		groups_width=8				,		layer_type="""x"""      ),
          '''regnet-x-004''': ImageNetPreTrainedConfig(
              depths=[1, 2, 7, 12]				,		hidden_sizes=[32, 64, 160, 384]				,		groups_width=16				,		layer_type="""x"""      ),
          '''regnet-x-006''': ImageNetPreTrainedConfig(
              depths=[1, 3, 5, 7]				,		hidden_sizes=[48, 96, 240, 528]				,		groups_width=24				,		layer_type="""x"""      ),
          '''regnet-x-008''': ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 5]				,		hidden_sizes=[64, 128, 288, 672]				,		groups_width=16				,		layer_type="""x"""      ),
          '''regnet-x-016''': ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 2]				,		hidden_sizes=[72, 168, 408, 912]				,		groups_width=24				,		layer_type="""x"""      ),
          '''regnet-x-032''': ImageNetPreTrainedConfig(
              depths=[2, 6, 15, 2]				,		hidden_sizes=[96, 192, 432, 1008]				,		groups_width=48				,		layer_type="""x"""      ),
          '''regnet-x-040''': ImageNetPreTrainedConfig(
              depths=[2, 5, 14, 2]				,		hidden_sizes=[80, 240, 560, 1360]				,		groups_width=40				,		layer_type="""x"""      ),
          '''regnet-x-064''': ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]				,		hidden_sizes=[168, 392, 784, 1624]				,		groups_width=56				,		layer_type="""x"""      ),
          '''regnet-x-080''': ImageNetPreTrainedConfig(
              depths=[2, 5, 15, 1]				,		hidden_sizes=[80, 240, 720, 1920]				,		groups_width=120				,		layer_type="""x"""      ),
          '''regnet-x-120''': ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]				,		hidden_sizes=[224, 448, 896, 2240]				,		groups_width=112				,		layer_type="""x"""      ),
          '''regnet-x-160''': ImageNetPreTrainedConfig(
              depths=[2, 6, 13, 1]				,		hidden_sizes=[256, 512, 896, 2048]				,		groups_width=128				,		layer_type="""x"""      ),
          '''regnet-x-320''': ImageNetPreTrainedConfig(
              depths=[2, 7, 13, 1]				,		hidden_sizes=[336, 672, 1344, 2520]				,		groups_width=168				,		layer_type="""x"""      ),
          # y variant
          '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,		hidden_sizes=[24, 56, 152, 368]				,		groups_width=8      ),
          '''regnet-y-004''': ImageNetPreTrainedConfig(
              depths=[1, 3, 6, 6]				,		hidden_sizes=[48, 104, 208, 440]				,		groups_width=8      ),
          '''regnet-y-006''': ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 4]				,		hidden_sizes=[48, 112, 256, 608]				,		groups_width=16      ),
          '''regnet-y-008''': ImageNetPreTrainedConfig(
              depths=[1, 3, 8, 2]				,		hidden_sizes=[64, 128, 320, 768]				,		groups_width=16      ),
          '''regnet-y-016''': ImageNetPreTrainedConfig(
              depths=[2, 6, 17, 2]				,		hidden_sizes=[48, 120, 336, 888]				,		groups_width=24      ),
          '''regnet-y-032''': ImageNetPreTrainedConfig(
              depths=[2, 5, 13, 1]				,		hidden_sizes=[72, 216, 576, 1512]				,		groups_width=24      ),
          '''regnet-y-040''': ImageNetPreTrainedConfig(
              depths=[2, 6, 12, 2]				,		hidden_sizes=[128, 192, 512, 1088]				,		groups_width=64      ),
          '''regnet-y-064''': ImageNetPreTrainedConfig(
              depths=[2, 7, 14, 2]				,		hidden_sizes=[144, 288, 576, 1296]				,		groups_width=72      ),
          '''regnet-y-080''': ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]				,		hidden_sizes=[168, 448, 896, 2016]				,		groups_width=56      ),
          '''regnet-y-120''': ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]				,		hidden_sizes=[224, 448, 896, 2240]				,		groups_width=112      ),
          '''regnet-y-160''': ImageNetPreTrainedConfig(
              depths=[2, 4, 11, 1]				,		hidden_sizes=[224, 448, 1232, 3024]				,		groups_width=112      ),
          '''regnet-y-320''': ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]				,		hidden_sizes=[232, 696, 1392, 3712]				,		groups_width=232      ),
          # models created by SEER -> https://arxiv.org/abs/2202.08360
          '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]				,		hidden_sizes=[232, 696, 1392, 3712]				,		groups_width=232      ),
          '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]				,		hidden_sizes=[328, 984, 1968, 4920]				,		groups_width=328      ),
          '''regnet-y-1280-seer''': RegNetConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[528, 1056, 2904, 7392]				,		groups_width=264      ),
          '''regnet-y-2560-seer''': RegNetConfig(
              depths=[3, 7, 16, 1]				,		hidden_sizes=[640, 1696, 2544, 5088]				,		groups_width=640      ),
          '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[2020, 4040, 1_1110, 2_8280]				,		groups_width=1010      ),
          # finetuned on imagenet
          '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]				,		hidden_sizes=[232, 696, 1392, 3712]				,		groups_width=232      ),
          '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]				,		hidden_sizes=[328, 984, 1968, 4920]				,		groups_width=328      ),
          '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[528, 1056, 2904, 7392]				,		groups_width=264      ),
          '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
              depths=[3, 7, 16, 1]				,		hidden_sizes=[640, 1696, 2544, 5088]				,		groups_width=640      ),
          '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[2020, 4040, 1_1110, 2_8280]				,		groups_width=1010      ),
      }

      lowerCAmelCase__			: int  					=       NameToOurModelFuncMap()
      lowerCAmelCase__			: List[str]  					=       NameToFromModelFuncMap()
      # add seer weights logic

      def load_using_classy_vision(__UpperCAmelCase				,		__UpperCAmelCase      ) -> Tuple[nn.Module, Dict]:
            lowerCAmelCase__			: Tuple  					=       torch.hub.load_state_dict_from_url(_snake_case				,		model_dir=str(_snake_case      )				,		map_location="""cpu"""      )
            lowerCAmelCase__			: Dict  					=       model_func()
            # check if we have a head, if yes add it
            lowerCAmelCase__			: Any  					=       files['''classy_state_dict''']['''base_model''']['''model''']
            lowerCAmelCase__			: Tuple  					=       model_state_dict['''trunk''']
            model.load_state_dict(_snake_case      )
            return model.eval(), model_state_dict["heads"]

      # pretrained
      lowerCAmelCase__			: List[Any]  					=       partial(
          _snake_case				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )				,		)

      lowerCAmelCase__			: str  					=       partial(
          _snake_case				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )				,		)

      lowerCAmelCase__			: int  					=       partial(
          _snake_case				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )				,		)

      lowerCAmelCase__			: Optional[Any]  					=       partial(
          _snake_case				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""				,		lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27				,		group_width=1010				,		w_a=1744				,		w_a=620.83				,		w_m=2.52      )      )      )				,		)

      # IN1K finetuned
      lowerCAmelCase__			: Union[str, Any]  					=       partial(
          _snake_case				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )				,		)

      lowerCAmelCase__			: List[str]  					=       partial(
          _snake_case				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )				,		)

      lowerCAmelCase__			: Optional[int]  					=       partial(
          _snake_case				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )				,		)

      lowerCAmelCase__			: Optional[int]  					=       partial(
          _snake_case				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""				,		lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27				,		group_width=1010				,		w_a=1744				,		w_a=620.83				,		w_m=2.52      )      )      )				,		)

      if model_name:
            convert_weight_and_push(
                _snake_case				,		names_to_from_model_map[model_name]				,		names_to_ours_model_map[model_name]				,		names_to_config[model_name]				,		_snake_case				,		_snake_case				,		)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(
                      _snake_case				,		names_to_from_model_map[model_name]				,		names_to_ours_model_map[model_name]				,		_snake_case				,		_snake_case				,		_snake_case				,		)
      return config, expected_shape


if __name__ == "__main__":
  _A													=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default=None,
      type=str,
      help=(
          """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
          """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=Path,
      required=True,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      default=True,
      type=bool,
      required=False,
      help="""If True, push model and image processor to the hub.""",
  )

  _A													=			parser.parse_args()

  _A													=			args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









"""simple docstring"""
from sklearn.metrics import recall_score

import datasets


SCREAMING_SNAKE_CASE :    Dict									=				"""
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


SCREAMING_SNAKE_CASE :    Any									=				"""
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while 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 y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If 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 target labels and predictions 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. Note that it 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`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


SCREAMING_SNAKE_CASE :    Tuple									=				"""
@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 				SCREAMING_SNAKE_CASE  (self					):





			'''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.recall_score.html''']				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_=None				,       a_=1				,       a_="binary"				,       a_=None				,       a_="warn"				,       ):





			'''simple docstring'''

			__snake_case	:						Any						=				recall_score(
			    a_				,       a_				,       labels=a_				,       pos_label=a_				,       average=a_				,       sample_weight=a_				,       zero_division=a_				,       )
			return {"recall": float(a_					) if score.size == 1 else score}





