






"""simple docstring"""






from sklearn.metrics import fa_score

import datasets


UpperCAmelCase_   : Tuple   			= """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


UpperCAmelCase_   : Dict   			= """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

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

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

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

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


UpperCAmelCase_   : Any   			= """
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
"""




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






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          return datasets.MetricInfo(
              description=_DESCRIPTION						,	citation=_CITATION						,	inputs_description=_KWARGS_DESCRIPTION						,	features=datasets.Features(
                  {
                      '''predictions''': datasets.Sequence(datasets.Value('''int32''')),
                      '''references''': datasets.Sequence(datasets.Value('''int32''')),
                  }
                  if self.config_name == '''multilabel'''
                  else {
                      '''predictions''': datasets.Value('''int32'''),
                      '''references''': datasets.Value('''int32'''),
                  })						,	reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html''']						,	)



      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  str						,	lowercase_     :  List[Any]						,	lowercase_     :  int=None						,	lowercase_     :  int=1						,	lowercase_     :  Any="binary"						,	lowercase_     :  Dict=None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		fa_score(
              lowercase_						,	lowercase_						,	labels=lowercase_						,	pos_label=lowercase_						,	average=lowercase_						,	sample_weight=lowercase_)
          return {"f1": float(lowercase_) if score.size == 1 else score}










"""simple docstring"""






import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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,
      LevitForImageClassification,
      LevitForImageClassificationWithTeacher,
      LevitModel,
  )
  from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(lowercase_						,	'''hidden_sizes'''))
          self.parent.assertTrue(hasattr(lowercase_						,	'''num_attention_heads'''))




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  str						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  List[Any]=13						,	lowercase_     :  Dict=64						,	lowercase_     :  Dict=3						,	lowercase_     :  Optional[Any]=3						,	lowercase_     :  List[Any]=2						,	lowercase_     :  Any=1						,	lowercase_     :  List[Any]=16						,	lowercase_     :  int=[128, 256, 384]						,	lowercase_     :  str=[4, 6, 8]						,	lowercase_     :  Optional[Any]=[2, 3, 4]						,	lowercase_     :  Union[str, Any]=[16, 16, 16]						,	lowercase_     :  Optional[Any]=0						,	lowercase_     :  Optional[int]=[2, 2, 2]						,	lowercase_     :  Any=[2, 2, 2]						,	lowercase_     :  List[str]=0.02						,	lowercase_     :  Any=True						,	lowercase_     :  Union[str, Any]=True						,	lowercase_     :  Optional[int]=2						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		parent
          SCREAMING_SNAKE_CASE_      :  Any    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		image_size
          SCREAMING_SNAKE_CASE_      :  int    						=		num_channels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kernel_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		stride
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		padding
          SCREAMING_SNAKE_CASE_      :  int    						=		hidden_sizes
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  int    						=		depths
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		key_dim
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		drop_path_rate
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		patch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		attention_ratio
          SCREAMING_SNAKE_CASE_      :  str    						=		mlp_ratio
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]
          SCREAMING_SNAKE_CASE_      :  Any    						=		is_training
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		num_labels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		initializer_range
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          SCREAMING_SNAKE_CASE_      :  Dict    						=		None
          if self.use_labels:
              SCREAMING_SNAKE_CASE_      :  str    						=		ids_tensor([self.batch_size]						,	self.num_labels)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_config()
          return config, pixel_values, labels
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          return LevitConfig(
              image_size=self.image_size						,	num_channels=self.num_channels						,	kernel_size=self.kernel_size						,	stride=self.stride						,	padding=self.padding						,	patch_size=self.patch_size						,	hidden_sizes=self.hidden_sizes						,	num_attention_heads=self.num_attention_heads						,	depths=self.depths						,	key_dim=self.key_dim						,	drop_path_rate=self.drop_path_rate						,	mlp_ratio=self.mlp_ratio						,	attention_ratio=self.attention_ratio						,	initializer_range=self.initializer_range						,	down_ops=self.down_ops						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  Any						,	lowercase_     :  int						,	lowercase_     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		LevitModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		(self.image_size, self.image_size)
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		image_size[0], image_size[1]
          for _ in range(4):
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
          self.parent.assertEqual(
              result.last_hidden_state.shape						,	(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1])						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  int						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		self.num_labels
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LevitForImageClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	labels=lowercase_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels))



      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		config_and_inputs
          SCREAMING_SNAKE_CASE_      :  int    						=		{'''pixel_values''': pixel_values}
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      __UpperCamelCase	 =  (
          {
              "feature-extraction": LevitModel,
              "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LevitModelTester(self)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		ConfigTester(self						,	config_class=lowercase_						,	has_text_modality=lowercase_						,	hidden_size=37)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          return
      @unittest.skip(reason='''Levit does not use inputs_embeds''')
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Levit does not support input and output embeddings''')
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Levit does not output attentions''')
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		['''pixel_values''']
              self.assertListEqual(arg_names[:1]						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          def check_hidden_states_output(lowercase_     :  List[str]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  str):
              SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.eval()

              with torch.no_grad():
                  SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(**self._prepare_for_class(lowercase_						,	lowercase_))

              SCREAMING_SNAKE_CASE_      :  str    						=		outputs.hidden_states

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		len(self.model_tester.depths) + 1
              self.assertEqual(len(lowercase_)						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		(self.model_tester.image_size, self.model_tester.image_size)
              SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		image_size[0], image_size[1]
              for _ in range(4):
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		floor(
                      (
                          (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1)
                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		floor(
                      (
                          (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1)
              # verify the first hidden states (first block)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:])						,	[
                      height * width,
                      self.model_tester.hidden_sizes[0],
                  ]						,	)

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		True
              check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		True

              check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)
      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Optional[int]						,	lowercase_     :  Tuple=False):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		super()._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)

          if return_labels:
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  del inputs_dict["labels"]

          return inputs_dict
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          if not self.model_tester.is_training:
              return

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		True

          for model_class in self.all_model_classes:
              # LevitForImageClassificationWithTeacher supports inference-only
              if (
                  model_class in get_values(lowercase_)
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                  continue
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.train()
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		model(**lowercase_).loss
              loss.backward()
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
              return

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		False
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		True

          for model_class in self.all_model_classes:
              if model_class in get_values(lowercase_) or not model_class.supports_gradient_checkpointing:
                  continue
              # LevitForImageClassificationWithTeacher supports inference-only
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  continue
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		model_class(lowercase_)
              model.gradient_checkpointing_enable()
              model.to(lowercase_)
              model.train()
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(**lowercase_).loss
              loss.backward()
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              {'''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(lowercase_),
                  ]
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                  continue

              for problem_type in problem_types:
                  with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'):
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		problem_type['''title''']
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		problem_type['''num_labels''']

                      SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)
                      model.to(lowercase_)
                      model.train()

                      SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)

                      if problem_type["num_labels"] > 1:
                          SCREAMING_SNAKE_CASE_      :  str    						=		inputs['''labels'''].unsqueeze(1).repeat(1						,	problem_type['''num_labels'''])

                      SCREAMING_SNAKE_CASE_      :  Any    						=		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=lowercase_) as warning_list:
                          SCREAMING_SNAKE_CASE_      :  int    						=		model(**lowercase_).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 _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		LevitModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)



def 					_A			() ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
    return image





@require_torch
@require_vision
class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      @cached_property
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
              lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.default_image_processor
          SCREAMING_SNAKE_CASE_      :  str    						=		prepare_img()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_processor(images=lowercase_						,	return_tensors='''pt''').to(lowercase_)

          # forward pass
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_      :  Any    						=		model(**lowercase_)

          # verify the logits
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.tensor([1.04_48, -0.37_45, -1.83_17]).to(lowercase_)

          self.assertTrue(torch.allclose(outputs.logits[0, :3]						,	lowercase_						,	atol=1e-4))


