





'''simple docstring'''






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

	if is_vision_available():
		from transformers import MaskFormerImageProcessor

if is_vision_available():
	from PIL import Image




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   Dict  ,      __magic_name__					:   Optional[Any]=2  ,      __magic_name__					:   List[Any]=True  ,      __magic_name__					:   Optional[Any]=False  ,      __magic_name__					:   Optional[int]=10  ,      __magic_name__					:   Optional[int]=3  ,      __magic_name__					:   Optional[int]=32 * 4  ,      __magic_name__					:   int=32 * 6  ,      __magic_name__					:   List[Any]=4  ,      __magic_name__					:   List[Any]=32  ,      )					->    Dict:


					"""simple docstring"""

					__snake_case					:      str							      =     parent
					__snake_case					:      int							      =     batch_size
					__snake_case					:      int							      =     is_training
					__snake_case					:      Tuple							      =     use_auxiliary_loss
					__snake_case					:      List[str]							      =     num_queries
					__snake_case					:      str							      =     num_channels
					__snake_case					:      Dict							      =     min_size
					__snake_case					:      int							      =     max_size
					__snake_case					:      List[Any]							      =     num_labels
					__snake_case					:      Union[str, Any]							      =     mask_feature_size

		def 	lowercase__     (   self					:   str			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      str							      =     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
					    __magic_name__			)

					__snake_case					:      Dict							      =     torch.ones([self.batch_size, self.min_size, self.max_size]  ,      device=__magic_name__			)

					__snake_case					:      Optional[Any]							      =     (
					    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,      device=__magic_name__			) > 0.5
					).float()
					__snake_case					:      Union[str, Any]							      =     (torch.rand((self.batch_size, self.num_labels)  ,      device=__magic_name__			) > 0.5).long()

					__snake_case					:      Tuple							      =     self.get_config()
					return config, pixel_values, pixel_mask, mask_labels, class_labels

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Any:


					"""simple docstring"""

					return MaskFormerConfig.from_backbone_and_decoder_configs(
					    backbone_config=SwinConfig(
					        depths=[1, 1, 1, 1]  ,      )  ,      decoder_config=DetrConfig(
					        decoder_ffn_dim=1_28  ,      num_queries=self.num_queries  ,      decoder_attention_heads=2  ,      d_model=self.mask_feature_size  ,      )  ,      mask_feature_size=self.mask_feature_size  ,      fpn_feature_size=self.mask_feature_size  ,      num_channels=self.num_channels  ,      num_labels=self.num_labels  ,      )

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      Any							      =     self.prepare_config_and_inputs()
					__snake_case					:      str							      =     {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
					return config, inputs_dict

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     output.encoder_hidden_states
					__snake_case					:      List[str]							      =     output.pixel_decoder_hidden_states
					__snake_case					:      str							      =     output.transformer_decoder_hidden_states

					self.parent.assertTrue(len(__magic_name__			)  ,      len(config.backbone_config.depths			)			)
					self.parent.assertTrue(len(__magic_name__			)  ,      len(config.backbone_config.depths			)			)
					self.parent.assertTrue(len(__magic_name__			)  ,      config.decoder_config.decoder_layers			)

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   Tuple=False			)					->    int:


					"""simple docstring"""

					with torch.no_grad():
								__snake_case					:      str							      =     MaskFormerModel(config=__magic_name__			)
								model.to(__magic_name__			)
								model.eval()

								__snake_case					:      List[Any]							      =     model(pixel_values=__magic_name__  ,      pixel_mask=__magic_name__			)
								__snake_case					:      int							      =     model(__magic_name__  ,      output_hidden_states=__magic_name__			)
					# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
					# encoder and pixel decoder
					self.parent.assertEqual(
					    output.transformer_decoder_last_hidden_state.shape  ,      (self.batch_size, self.num_queries, self.mask_feature_size)  ,      )
					# let's ensure the other two hidden state exists
					self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None			)
					self.parent.assertTrue(output.encoder_last_hidden_state is not None			)

					if output_hidden_states:
								self.check_output_hidden_state(__magic_name__  ,      __magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Dict  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Union[str, Any]			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     MaskFormerForInstanceSegmentation(config=__magic_name__			)
					model.to(__magic_name__			)
					model.eval()

					def comm_check_on_output(__magic_name__					:   Dict			):
								# let's still check that all the required stuff is there
								self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None			)
								self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None			)
								self.parent.assertTrue(result.encoder_last_hidden_state is not None			)
								# okay, now we need to check the logits shape
								# due to the encoder compression, masks have a //4 spatial size
								self.parent.assertEqual(
								    result.masks_queries_logits.shape  ,      (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)  ,      )
								# + 1 for null class
								self.parent.assertEqual(
								    result.class_queries_logits.shape  ,      (self.batch_size, self.num_queries, self.num_labels + 1)			)

					with torch.no_grad():
								__snake_case					:      Tuple							      =     model(pixel_values=__magic_name__  ,      pixel_mask=__magic_name__			)
								__snake_case					:      List[Any]							      =     model(__magic_name__			)

								comm_check_on_output(__magic_name__			)

								__snake_case					:      List[Any]							      =     model(
								    pixel_values=__magic_name__  ,      pixel_mask=__magic_name__  ,      mask_labels=__magic_name__  ,      class_labels=__magic_name__			)

					comm_check_on_output(__magic_name__			)

					self.parent.assertTrue(result.loss is not None			)
					self.parent.assertEqual(result.loss.shape  ,      torch.Size([1]			)			)





@require_torch
class  _A (					__lowercase						,					__lowercase						,					unittest.TestCase		):
		lowercase__:   str				     =   (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
		lowercase__:   Tuple				     =   (
		    {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
		    if is_torch_available()
		    else {}
		)

		lowercase__:   Optional[Any]				     =   False
		lowercase__:   Union[str, Any]				     =   False
		lowercase__:   Optional[int]				     =   False
		lowercase__:   Dict				     =   False

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     MaskFormerModelTester(self			)
					__snake_case					:      Optional[int]							      =     ConfigTester(self  ,      config_class=__magic_name__  ,      has_text_modality=__magic_name__			)

		def 	lowercase__     (   self					:   List[Any]			)					->    Dict:


					"""simple docstring"""

					self.config_tester.run_common_tests()

		def 	lowercase__     (   self					:   Tuple			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      List[str]							      =     self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskformer_model(__magic_name__  ,      **__magic_name__  ,      output_hidden_states=__magic_name__			)

		def 	lowercase__     (   self					:   Any			)					->    str:


					"""simple docstring"""

					__snake_case					:      int							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__magic_name__			)

		@unittest.skip(reason="""MaskFormer does not use inputs_embeds"""			)
		def 	lowercase__     (   self					:   Optional[int]			)					->    int:


					"""simple docstring"""

					pass

		@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""			)
		def 	lowercase__     (   self					:   Any			)					->    Tuple:


					"""simple docstring"""

					pass

		@unittest.skip(reason="""MaskFormer is not a generative model"""			)
		def 	lowercase__     (   self					:   Optional[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					pass

		@unittest.skip(reason="""MaskFormer does not use token embeddings"""			)
		def 	lowercase__     (   self					:   List[str]			)					->    List[str]:


					"""simple docstring"""

					pass

		@require_torch_multi_gpu
		@unittest.skip(
		    reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""			)
		def 	lowercase__     (   self					:   Dict			)					->    Tuple:


					"""simple docstring"""

					pass

		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
		def 	lowercase__     (   self					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   List[str]			)					->    List[str]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      str							      =     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__snake_case					:      List[str]							      =     model_class(__magic_name__			)
								__snake_case					:      Dict							      =     inspect.signature(model.forward			)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								__snake_case					:      Union[str, Any]							      =     [*signature.parameters.keys()]

								__snake_case					:      str							      =     ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]  ,      __magic_name__			)

		@slow
		def 	lowercase__     (   self					:   List[Any]			)					->    Tuple:


					"""simple docstring"""

					for model_name in ["facebook/maskformer-swin-small-coco"]:
								__snake_case					:      str							      =     MaskFormerModel.from_pretrained(__magic_name__			)
								self.assertIsNotNone(__magic_name__			)

		def 	lowercase__     (   self					:   int			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Any							      =     (self.model_tester.min_size,) * 2
					__snake_case					:      List[Any]							      =     {
					    """pixel_values""": torch.randn((2, 3, *size)  ,      device=__magic_name__			),
					    """mask_labels""": torch.randn((2, 10, *size)  ,      device=__magic_name__			),
					    """class_labels""": torch.zeros(2  ,      10  ,      device=__magic_name__			).long(),
					}

					__snake_case					:      str							      =     MaskFormerForInstanceSegmentation(MaskFormerConfig()			).to(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     model(**__magic_name__			)
					self.assertTrue(outputs.loss is not None			)

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Any							      =     self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskformer_model(__magic_name__  ,      **__magic_name__  ,      output_hidden_states=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    Dict:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Tuple							      =     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__snake_case					:      List[Any]							      =     model_class(__magic_name__			).to(__magic_name__			)
								__snake_case					:      Any							      =     model(**__magic_name__  ,      output_attentions=__magic_name__			)
								self.assertTrue(outputs.attentions is not None			)

		def 	lowercase__     (   self					:   List[Any]			)					->    List[str]:


					"""simple docstring"""

					if not self.model_tester.is_training:
								return
					# only MaskFormerForInstanceSegmentation has the loss
					__snake_case					:      Dict							      =     self.all_model_classes[1]
					__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      Any							      =     self.model_tester.prepare_config_and_inputs()

					__snake_case					:      Union[str, Any]							      =     model_class(__magic_name__			)
					model.to(__magic_name__			)
					model.train()

					__snake_case					:      Dict							      =     model(__magic_name__  ,      mask_labels=__magic_name__  ,      class_labels=__magic_name__			).loss
					loss.backward()

		def 	lowercase__     (   self					:   Tuple			)					->    str:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     self.all_model_classes[1]
					__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      List[str]							      =     self.model_tester.prepare_config_and_inputs()
					__snake_case					:      Any							      =     True
					__snake_case					:      str							      =     True

					__snake_case					:      List[Any]							      =     model_class(__magic_name__			)
					model.to(__magic_name__			)
					model.train()

					__snake_case					:      str							      =     model(__magic_name__  ,      mask_labels=__magic_name__  ,      class_labels=__magic_name__			)

					__snake_case					:      Dict							      =     outputs.encoder_hidden_states[0]
					encoder_hidden_states.retain_grad()

					__snake_case					:      Union[str, Any]							      =     outputs.pixel_decoder_hidden_states[0]
					pixel_decoder_hidden_states.retain_grad()
					# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
					__snake_case					:      Dict							      =     outputs.transformer_decoder_hidden_states[0]
					transformer_decoder_hidden_states.retain_grad()

					__snake_case					:      Optional[Any]							      =     outputs.attentions[0]
					attentions.retain_grad()

					outputs.loss.backward(retain_graph=__magic_name__			)

					self.assertIsNotNone(encoder_hidden_states.grad			)
					self.assertIsNotNone(pixel_decoder_hidden_states.grad			)
					self.assertIsNotNone(transformer_decoder_hidden_states.grad			)
					self.assertIsNotNone(attentions.grad			)





__UpperCamelCase					    =	1E-4







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      List[Any]							      =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
			return image




@require_vision
@slow
class  _A (					unittest.TestCase		):

		@cached_property
		def 	lowercase__     (   self					:   int			)					->    List[Any]:


					"""simple docstring"""

					return (
					    MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""			)
					    if is_vision_available()
					    else None
					)

		def 	lowercase__     (   self					:   Tuple			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""			).to(__magic_name__			)
					__snake_case					:      List[str]							      =     self.default_image_processor
					__snake_case					:      Optional[int]							      =     prepare_img()
					__snake_case					:      Optional[int]							      =     image_processor(__magic_name__  ,      return_tensors="""pt"""			).to(__magic_name__			)
					__snake_case					:      str							      =     inputs["""pixel_values"""].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
					# check size
					self.assertEqual(__magic_name__  ,      (1, 3, 8_00, 10_88)			)

					with torch.no_grad():
								__snake_case					:      Dict							      =     model(**__magic_name__			)

					__snake_case					:      Tuple							      =     torch.tensor(
					    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]			).to(__magic_name__			)
					self.assertTrue(
					    torch.allclose(
					        outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,      __magic_name__  ,      atol=__magic_name__			)			)

					__snake_case					:      Optional[Any]							      =     torch.tensor(
					    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]			).to(__magic_name__			)
					self.assertTrue(
					    torch.allclose(
					        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,      __magic_name__  ,      atol=__magic_name__			)			)

					__snake_case					:      Dict							      =     torch.tensor(
					    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]			).to(__magic_name__			)
					self.assertTrue(
					    torch.allclose(
					        outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,      __magic_name__  ,      atol=__magic_name__			)			)

		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     (
					    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""			)
					    .to(__magic_name__			)
					    .eval()
					)
					__snake_case					:      List[Any]							      =     self.default_image_processor
					__snake_case					:      List[str]							      =     prepare_img()
					__snake_case					:      Tuple							      =     image_processor(__magic_name__  ,      return_tensors="""pt"""			).to(__magic_name__			)
					__snake_case					:      Optional[int]							      =     inputs["""pixel_values"""].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
					# check size
					self.assertEqual(__magic_name__  ,      (1, 3, 8_00, 10_88)			)

					with torch.no_grad():
								__snake_case					:      int							      =     model(**__magic_name__			)
					# masks_queries_logits
					__snake_case					:      str							      =     outputs.masks_queries_logits
					self.assertEqual(
					    masks_queries_logits.shape  ,      (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,      )
					__snake_case					:      Optional[int]							      =     [
					    [-1.3737124, -1.7724937, -1.9364233],
					    [-1.5977281, -1.9867939, -2.1523695],
					    [-1.5795398, -1.9269832, -2.093942],
					]
					__snake_case					:      Optional[Any]							      =     torch.tensor(__magic_name__			).to(__magic_name__			)
					self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,      __magic_name__  ,      atol=__magic_name__			)			)
					# class_queries_logits
					__snake_case					:      Optional[Any]							      =     outputs.class_queries_logits
					self.assertEqual(
					    class_queries_logits.shape  ,      (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)			)
					__snake_case					:      Optional[int]							      =     torch.tensor(
					    [
					        [1.6_512E00, -5.2_572E00, -3.3_519E00],
					        [3.6_169E-02, -5.9_025E00, -2.9_313E00],
					        [1.0_766E-04, -7.7_630E00, -5.1_263E00],
					    ]			).to(__magic_name__			)
					self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,      __magic_name__  ,      atol=__magic_name__			)			)

		def 	lowercase__     (   self					:   Any			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     (
					    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""			)
					    .to(__magic_name__			)
					    .eval()
					)
					__snake_case					:      str							      =     self.default_image_processor
					__snake_case					:      Union[str, Any]							      =     prepare_img()
					__snake_case					:      List[str]							      =     image_processor(__magic_name__  ,      return_tensors="""pt"""			).to(__magic_name__			)
					__snake_case					:      Dict							      =     inputs["""pixel_values"""].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
					# check size
					self.assertEqual(__magic_name__  ,      (1, 3, 8_00, 10_88)			)

					with torch.no_grad():
								__snake_case					:      str							      =     model(**__magic_name__			)
					# masks_queries_logits
					__snake_case					:      List[Any]							      =     outputs.masks_queries_logits
					self.assertEqual(
					    masks_queries_logits.shape  ,      (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,      )
					__snake_case					:      List[Any]							      =     [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
					__snake_case					:      Union[str, Any]							      =     torch.tensor(__magic_name__			).to(__magic_name__			)
					self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,      __magic_name__  ,      atol=__magic_name__			)			)
					# class_queries_logits
					__snake_case					:      Union[str, Any]							      =     outputs.class_queries_logits
					self.assertEqual(
					    class_queries_logits.shape  ,      (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)			)
					__snake_case					:      Dict							      =     torch.tensor(
					    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]			).to(__magic_name__			)
					self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,      __magic_name__  ,      atol=__magic_name__			)			)

		def 	lowercase__     (   self					:   Optional[Any]			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     (
					    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""			)
					    .to(__magic_name__			)
					    .eval()
					)
					__snake_case					:      List[str]							      =     self.default_image_processor

					__snake_case					:      str							      =     image_processor(
					    [np.zeros((3, 8_00, 13_33)			), np.zeros((3, 8_00, 13_33)			)]  ,      segmentation_maps=[np.zeros((3_84, 3_84)			).astype(np.floataa			), np.zeros((3_84, 3_84)			).astype(np.floataa			)]  ,      return_tensors="""pt"""  ,      )

					__snake_case					:      List[str]							      =     inputs["""pixel_values"""].to(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     [el.to(__magic_name__			) for el in inputs["""mask_labels"""]]
					__snake_case					:      Dict							      =     [el.to(__magic_name__			) for el in inputs["""class_labels"""]]

					with torch.no_grad():
								__snake_case					:      Any							      =     model(**__magic_name__			)

					self.assertTrue(outputs.loss is not None			)









'''simple docstring'''






from sklearn.metrics import recall_score

import datasets


__UpperCamelCase					    =	"\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"


__UpperCamelCase					    =	"\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **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.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **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'`.\n    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions 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. Note that it 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- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **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.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {'recall': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {'recall': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {'recall': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'recall': array([1., 0., 0.])}\n"


__UpperCamelCase					    =	"\n@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}\n"




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

		def 	lowercase__     (   self					:   Optional[int]			)					->    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.recall_score.html"""]  ,      )

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   int  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Any=None  ,      __magic_name__					:   Optional[Any]=1  ,      __magic_name__					:   List[str]="binary"  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Dict="warn"  ,      )					->    Any:


					"""simple docstring"""

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


