code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import inspect
  import unittest
  import warnings
  from typing import List
  
  from transformers import EfficientFormerConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  	import torch
  
  	from transformers import (
  	    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
  	    MODEL_MAPPING,
  	    EfficientFormerForImageClassification,
  	    EfficientFormerForImageClassificationWithTeacher,
  	    EfficientFormerModel,
  	)
  	from transformers.models.efficientformer.modeling_efficientformer import (
  	    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  	)
  
  
  if is_vision_available():
  	from PIL import Image
  
  	from transformers import EfficientFormerImageProcessor
  
  
  
  
  
  class 					_UpperCAmelCase							:
  		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 13,__SCREAMING_SNAKE_CASE = 64,__SCREAMING_SNAKE_CASE = 2,__SCREAMING_SNAKE_CASE = 3,__SCREAMING_SNAKE_CASE = 3,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE = 1_28,__SCREAMING_SNAKE_CASE=[16, 32, 64, 1_28],__SCREAMING_SNAKE_CASE = 7,__SCREAMING_SNAKE_CASE = 4,__SCREAMING_SNAKE_CASE = 37,__SCREAMING_SNAKE_CASE = "gelu",__SCREAMING_SNAKE_CASE = 0.1,__SCREAMING_SNAKE_CASE = 0.1,__SCREAMING_SNAKE_CASE = 10,__SCREAMING_SNAKE_CASE = 0.02,__SCREAMING_SNAKE_CASE = 2,__SCREAMING_SNAKE_CASE = 1,__SCREAMING_SNAKE_CASE = 1_28,__SCREAMING_SNAKE_CASE = [2, 2, 2, 2],__SCREAMING_SNAKE_CASE = 2,__SCREAMING_SNAKE_CASE = 2,):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       parent
  					__lowerCAmelCase      		=       batch_size
  					__lowerCAmelCase      		=       image_size
  					__lowerCAmelCase      		=       patch_size
  					__lowerCAmelCase      		=       num_channels
  					__lowerCAmelCase      		=       is_training
  					__lowerCAmelCase      		=       use_labels
  					__lowerCAmelCase      		=       hidden_size
  					__lowerCAmelCase      		=       num_hidden_layers
  					__lowerCAmelCase      		=       num_attention_heads
  					__lowerCAmelCase      		=       intermediate_size
  					__lowerCAmelCase      		=       hidden_act
  					__lowerCAmelCase      		=       hidden_dropout_prob
  					__lowerCAmelCase      		=       attention_probs_dropout_prob
  					__lowerCAmelCase      		=       type_sequence_label_size
  					__lowerCAmelCase      		=       initializer_range
  					__lowerCAmelCase      		=       encoder_stride
  					__lowerCAmelCase      		=       num_attention_outputs
  					__lowerCAmelCase      		=       embed_dim
  					__lowerCAmelCase      		=       embed_dim + 1
  					__lowerCAmelCase      		=       resolution
  					__lowerCAmelCase      		=       depths
  					__lowerCAmelCase      		=       hidden_sizes
  					__lowerCAmelCase      		=       dim
  					__lowerCAmelCase      		=       mlp_expansion_ratio
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
  
  					__lowerCAmelCase      		=       None
  					if self.use_labels:
  								__lowerCAmelCase      		=       ids_tensor([self.batch_size],self.type_sequence_label_size    )
  
  					__lowerCAmelCase      		=       self.get_config()
  					return config, pixel_values, labels
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return EfficientFormerConfig(
  					    image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,is_decoder=__SCREAMING_SNAKE_CASE,initializer_range=self.initializer_range,encoder_stride=self.encoder_stride,resolution=self.resolution,depths=self.depths,hidden_sizes=self.hidden_sizes,dim=self.dim,mlp_expansion_ratio=self.mlp_expansion_ratio,)
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       EfficientFormerModel(config=__SCREAMING_SNAKE_CASE    )
  					model.to(__SCREAMING_SNAKE_CASE    )
  					model.eval()
  					__lowerCAmelCase      		=       model(__SCREAMING_SNAKE_CASE    )
  					self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.type_sequence_label_size
  					__lowerCAmelCase      		=       EfficientFormerForImageClassification(__SCREAMING_SNAKE_CASE    )
  					model.to(__SCREAMING_SNAKE_CASE    )
  					model.eval()
  					__lowerCAmelCase      		=       model(__SCREAMING_SNAKE_CASE,labels=__SCREAMING_SNAKE_CASE    )
  					self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)    )
  
  					# test greyscale images
  					__lowerCAmelCase      		=       1
  					__lowerCAmelCase      		=       EfficientFormerForImageClassification(__SCREAMING_SNAKE_CASE    )
  					model.to(__SCREAMING_SNAKE_CASE    )
  					model.eval()
  
  					__lowerCAmelCase      		=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  					__lowerCAmelCase      		=       model(__SCREAMING_SNAKE_CASE    )
  					self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.prepare_config_and_inputs()
  					(
  					    (
  					    __lowerCAmelCase
  					)		, (
  					    __lowerCAmelCase
  					)		, (
  					    __lowerCAmelCase
  					)		, 
  					)      		=       config_and_inputs
  					__lowerCAmelCase      		=       {"""pixel_values""": pixel_values}
  					return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 					_UpperCAmelCase							(		lowerCAmelCase_			,      lowerCAmelCase_			,      unittest.TestCase     ):
  		a     :     List[str]     						=(
  		    (
  		        EfficientFormerModel,
  		        EfficientFormerForImageClassificationWithTeacher,
  		        EfficientFormerForImageClassification,
  		    )
  		    if is_torch_available()
  		    else ()
  		)
  		a     :     List[Any]     						=(
  		    {
  		        """feature-extraction""": EfficientFormerModel,
  		        """image-classification""": (
  		            EfficientFormerForImageClassification,
  		            EfficientFormerForImageClassificationWithTeacher,
  		        ),
  		    }
  		    if is_torch_available()
  		    else {}
  		)
  		a     :     Union[str, Any]     						=False
  
  		a     :     str     						=False
  		a     :     List[str]     						=False
  		a     :     List[Any]     						=False
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       EfficientFormerModelTester(self    )
  					__lowerCAmelCase      		=       ConfigTester(
  					    self,config_class=__SCREAMING_SNAKE_CASE,has_text_modality=__SCREAMING_SNAKE_CASE,hidden_size=37    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					self.config_tester.run_common_tests()
  		@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					pass
  		@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					pass
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  
  					for model_class in self.all_model_classes:
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       inspect.signature(model.forward    )
  								# signature.parameters is an OrderedDict => so arg_names order is deterministic
  								__lowerCAmelCase      		=       [*signature.parameters.keys()]
  
  								__lowerCAmelCase      		=       ["""pixel_values"""]
  								self.assertListEqual(arg_names[:1],__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					def check_hidden_states_output(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								model.to(__SCREAMING_SNAKE_CASE    )
  								model.eval()
  
  								with torch.no_grad():
  											__lowerCAmelCase      		=       model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )    )
  
  								__lowerCAmelCase      		=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  								__lowerCAmelCase      		=       getattr(
  								    self.model_tester,"""expected_num_hidden_layers""",self.model_tester.num_hidden_layers + 1    )
  								self.assertEqual(len(__SCREAMING_SNAKE_CASE    ),__SCREAMING_SNAKE_CASE    )
  
  								if hasattr(self.model_tester,"""encoder_seq_length"""    ):
  											__lowerCAmelCase      		=       self.model_tester.encoder_seq_length
  											if hasattr(self.model_tester,"""chunk_length"""    ) and self.model_tester.chunk_length > 1:
  														__lowerCAmelCase      		=       seq_length * self.model_tester.chunk_length
  								else:
  											__lowerCAmelCase      		=       self.model_tester.seq_length
  
  								self.assertListEqual(
  								    list(hidden_states[-1].shape[-2:]    ),[seq_length, self.model_tester.hidden_size],)
  
  								if config.is_encoder_decoder:
  											__lowerCAmelCase      		=       outputs.decoder_hidden_states
  
  											self.assertIsInstance(__SCREAMING_SNAKE_CASE,(list, tuple)    )
  											self.assertEqual(len(__SCREAMING_SNAKE_CASE    ),__SCREAMING_SNAKE_CASE    )
  											__lowerCAmelCase      		=       getattr(self.model_tester,"""seq_length""",__SCREAMING_SNAKE_CASE    )
  											__lowerCAmelCase      		=       getattr(self.model_tester,"""decoder_seq_length""",__SCREAMING_SNAKE_CASE    )
  
  											self.assertListEqual(
  											    list(hidden_states[-1].shape[-2:]    ),[decoder_seq_length, self.model_tester.hidden_size],)
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  
  					for model_class in self.all_model_classes:
  								__lowerCAmelCase      		=       True
  								check_hidden_states_output(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  
  								# check that output_hidden_states also work using config
  								del inputs_dict["output_hidden_states"]
  								__lowerCAmelCase      		=       True
  
  								check_hidden_states_output(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       super()._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,return_labels=__SCREAMING_SNAKE_CASE    )
  
  					if return_labels:
  								if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
  											del inputs_dict["labels"]
  
  					return inputs_dict
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE    )
  		@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					if not self.model_tester.is_training:
  								return
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  					__lowerCAmelCase      		=       True
  
  					for model_class in self.all_model_classes:
  								# EfficientFormerForImageClassificationWithTeacher supports inference-only
  								if (
  								    model_class in get_values(__SCREAMING_SNAKE_CASE    )
  								    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
  								):
  											continue
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								model.to(__SCREAMING_SNAKE_CASE    )
  								model.train()
  								__lowerCAmelCase      		=       self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,return_labels=__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       model(**__SCREAMING_SNAKE_CASE    ).loss
  								loss.backward()
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  
  					__lowerCAmelCase      		=       [
  					    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
  					    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
  					    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
  					]
  
  					for model_class in self.all_model_classes:
  								if (
  								    model_class
  								    not in [
  								        *get_values(__SCREAMING_SNAKE_CASE    ),
  								    ]
  								    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
  								):
  											continue
  
  								for problem_type in problem_types:
  											with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'    ):
  														__lowerCAmelCase      		=       problem_type["""title"""]
  														__lowerCAmelCase      		=       problem_type["""num_labels"""]
  
  														__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  														model.to(__SCREAMING_SNAKE_CASE    )
  														model.train()
  
  														__lowerCAmelCase      		=       self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,return_labels=__SCREAMING_SNAKE_CASE    )
  
  														if problem_type["num_labels"] > 1:
  																	__lowerCAmelCase      		=       inputs["""labels"""].unsqueeze(1    ).repeat(1,problem_type["""num_labels"""]    )
  
  														__lowerCAmelCase      		=       inputs["""labels"""].to(problem_type["""dtype"""]    )
  
  														# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
  														# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
  														# they have the same size." which is a symptom something in wrong for the regression problem.
  														# See https://github.com/huggingface/transformers/issues/11780
  														with warnings.catch_warnings(record=__SCREAMING_SNAKE_CASE    ) as warning_list:
  																	__lowerCAmelCase      		=       model(**__SCREAMING_SNAKE_CASE    ).loss
  														for w in warning_list:
  																	if "Using a target size that is different to the input size" in str(w.message    ):
  																				raise ValueError(
  																				    f'Something is going wrong in the regression problem: intercepted {w.message}'    )
  
  														loss.backward()
  		@slow
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  								__lowerCAmelCase      		=       EfficientFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE    )
  								self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  					__lowerCAmelCase      		=       True
  
  					__lowerCAmelCase      		=       getattr(self.model_tester,"""seq_length""",__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       getattr(self.model_tester,"""encoder_seq_length""",__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       getattr(self.model_tester,"""key_length""",__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       getattr(self.model_tester,"""chunk_length""",__SCREAMING_SNAKE_CASE    )
  
  					if chunk_length is not None and hasattr(self.model_tester,"""num_hashes"""    ):
  								__lowerCAmelCase      		=       encoder_seq_length * self.model_tester.num_hashes
  
  					for model_class in self.all_model_classes:
  								__lowerCAmelCase      		=       True
  								__lowerCAmelCase      		=       False
  								__lowerCAmelCase      		=       True
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								model.to(__SCREAMING_SNAKE_CASE    )
  								model.eval()
  								with torch.no_grad():
  											__lowerCAmelCase      		=       model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )    )
  								__lowerCAmelCase      		=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
  								self.assertEqual(len(__SCREAMING_SNAKE_CASE    ),self.model_tester.num_attention_outputs    )
  
  								# check that output_attentions also work using config
  								del inputs_dict["output_attentions"]
  								__lowerCAmelCase      		=       True
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								model.to(__SCREAMING_SNAKE_CASE    )
  								model.eval()
  								with torch.no_grad():
  											__lowerCAmelCase      		=       model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )    )
  								__lowerCAmelCase      		=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
  								self.assertEqual(len(__SCREAMING_SNAKE_CASE    ),self.model_tester.num_attention_outputs    )
  
  								if chunk_length is not None:
  											self.assertListEqual(
  											    list(attentions[0].shape[-4:]    ),[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],)
  								else:
  											self.assertListEqual(
  											    list(attentions[0].shape[-3:]    ),[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      List[str]:
  			__lowerCAmelCase      		=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
  			return image
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					_UpperCAmelCase							(		unittest.TestCase     ):
  		@cached_property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return (
  					    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""    )
  					    if is_vision_available()
  					    else None
  					)
  		@slow
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       EfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""    ).to(
  					    __SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       self.default_image_processor
  					__lowerCAmelCase      		=       prepare_img()
  					__lowerCAmelCase      		=       image_processor(images=__SCREAMING_SNAKE_CASE,return_tensors="""pt"""    ).to(__SCREAMING_SNAKE_CASE    )
  
  					# forward pass
  					with torch.no_grad():
  								__lowerCAmelCase      		=       model(**__SCREAMING_SNAKE_CASE    )
  
  					# verify the logits
  					__lowerCAmelCase      		=       (1, 10_00)
  					self.assertEqual(outputs.logits.shape,__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       torch.tensor([-0.0555, 0.4825, -0.0852]    ).to(__SCREAMING_SNAKE_CASE    )
  					self.assertTrue(torch.allclose(outputs.logits[0][:3],__SCREAMING_SNAKE_CASE,atol=1e-4    )    )
  
  
  
  
  
  
  
  		@slow
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       EfficientFormerForImageClassificationWithTeacher.from_pretrained(
  					    """snap-research/efficientformer-l1-300"""    ).to(__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       self.default_image_processor
  					__lowerCAmelCase      		=       prepare_img()
  					__lowerCAmelCase      		=       image_processor(images=__SCREAMING_SNAKE_CASE,return_tensors="""pt"""    ).to(__SCREAMING_SNAKE_CASE    )
  
  					# forward pass
  					with torch.no_grad():
  								__lowerCAmelCase      		=       model(**__SCREAMING_SNAKE_CASE    )
  
  					# verify the logits
  					__lowerCAmelCase      		=       (1, 10_00)
  					self.assertEqual(outputs.logits.shape,__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       torch.tensor([-0.1312, 0.4353, -1.0499]    ).to(__SCREAMING_SNAKE_CASE    )
  					self.assertTrue(torch.allclose(outputs.logits[0][:3],__SCREAMING_SNAKE_CASE,atol=1e-4    )    )
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  _a      :				str											=					"""
  This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.
  
  This metric has three separate use cases:
      - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
      - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
      - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
  """
  
  _a      :				Optional[int]											=					"""
  Args:
  - references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
      - binary: expects an array-like of shape (n_samples,)
      - multiclass: expects an array-like of shape (n_samples,)
      - multilabel: expects an array-like of shape (n_samples, n_classes)
  - prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
      - binary: expects an array-like of shape (n_samples,)
      - multiclass: expects an array-like of shape (n_samples, n_classes)
      - multilabel: expects an array-like of shape (n_samples, n_classes)
  - average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
      - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
      - `'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 (i.e. the number of true instances for each label).
      - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
      - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
  - sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
  - max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
  - multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
      - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.
      - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
  - labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
      `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
      `prediction_scores` is used. Defaults to `None`.
  Returns:
      roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
  Examples:
      Example 1:
          >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
          >>> refs = [1, 0, 1, 1, 0, 0]
          >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
          >>> print(round(results['roc_auc'], 2))
          0.78
  
      Example 2:
          >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
          >>> refs = [1, 0, 1, 2, 2, 0]
          >>> pred_scores = [[0.3, 0.5, 0.2],
          ...                 [0.7, 0.2, 0.1],
          ...                 [0.005, 0.99, 0.005],
          ...                 [0.2, 0.3, 0.5],
          ...                 [0.1, 0.1, 0.8],
          ...                 [0.1, 0.7, 0.2]]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
          >>> print(round(results['roc_auc'], 2))
          0.85
  
      Example 3:
          >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
          >>> refs = [[1, 1, 0],
          ...         [1, 1, 0],
          ...         [0, 1, 0],
          ...         [0, 0, 1],
          ...         [0, 1, 1],
          ...         [1, 0, 1]]
          >>> pred_scores = [[0.3, 0.5, 0.2],
          ...                 [0.7, 0.2, 0.1],
          ...                 [0.005, 0.99, 0.005],
          ...                 [0.2, 0.3, 0.5],
          ...                 [0.1, 0.1, 0.8],
          ...                 [0.1, 0.7, 0.2]]
          >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
          >>> print([round(res, 2) for res in results['roc_auc']])
          [0.83, 0.38, 0.94]
  """
  
  _a      :				str											=					"""\
  @article{doi:10.1177/0272989X8900900307,
  author = {Donna Katzman McClish},
  title ={Analyzing a Portion of the ROC Curve},
  journal = {Medical Decision Making},
  volume = {9},
  number = {3},
  pages = {190-195},
  year = {1989},
  doi = {10.1177/0272989X8900900307},
      note ={PMID: 2668680},
  URL = {https://doi.org/10.1177/0272989X8900900307},
  eprint = {https://doi.org/10.1177/0272989X8900900307}
  }
  
  
  @article{10.1023/A:1010920819831,
  author = {Hand, David J. and Till, Robert J.},
  title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
  year = {2001},
  issue_date = {November 2001},
  publisher = {Kluwer Academic Publishers},
  address = {USA},
  volume = {45},
  number = {2},
  issn = {0885-6125},
  url = {https://doi.org/10.1023/A:1010920819831},
  doi = {10.1023/A:1010920819831},
  journal = {Mach. Learn.},
  month = {oct},
  pages = {171–186},
  numpages = {16},
  keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
  }
  
  
  @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     ):
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return datasets.MetricInfo(
  					    description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
  					        {
  					            """prediction_scores""": datasets.Sequence(datasets.Value("""float"""    )    ),
  					            """references""": datasets.Value("""int32"""    ),
  					        }
  					        if self.config_name == """multiclass"""
  					        else {
  					            """references""": datasets.Sequence(datasets.Value("""int32"""    )    ),
  					            """prediction_scores""": datasets.Sequence(datasets.Value("""float"""    )    ),
  					        }
  					        if self.config_name == """multilabel"""
  					        else {
  					            """references""": datasets.Value("""int32"""    ),
  					            """prediction_scores""": datasets.Value("""float"""    ),
  					        }    ),reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"""],)
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE="macro",__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="raise",__SCREAMING_SNAKE_CASE=None,):
  
  
  
  					'''simple docstring'''
  
  					return {
  					    "roc_auc": roc_auc_score(
  					        __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,average=__SCREAMING_SNAKE_CASE,sample_weight=__SCREAMING_SNAKE_CASE,max_fpr=__SCREAMING_SNAKE_CASE,multi_class=__SCREAMING_SNAKE_CASE,labels=__SCREAMING_SNAKE_CASE,)
  					}
  
  