
"""simple docstring"""

import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


UpperCamelCase     :    str       =			"\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

UpperCamelCase     :    Union[str, Any]       =			"\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

UpperCamelCase     :    Any       =			"\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"

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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if version.parse(scb.__version__				) < version.parse('1.4.12'				):
       raise ImportWarning(
           'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
           'You can install it with `pip install "sacrebleu>=1.4.12"`.'				)
      return datasets.MetricInfo(
          description=_DESCRIPTION     ,					citation=_CITATION     ,					homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf'     ,					inputs_description=_KWARGS_DESCRIPTION     ,					features=datasets.Features(
              {
                  'predictions': datasets.Value('string'     ,					id='sequence'				),
                  'references': datasets.Sequence(datasets.Value('string'     ,					id='sequence'				)     ,					id='references'				),
              }				)     ,					codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf']     ,					reference_urls=[
              'https://github.com/m-popovic/chrF',
          ]     ,					)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = CHRF.CHAR_ORDER     ,					__UpperCAmelCase = CHRF.WORD_ORDER     ,					__UpperCAmelCase = CHRF.BETA     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	len(references[0]				)
      if any(len(__UpperCAmelCase				) != references_per_prediction for refs in references				):
       raise ValueError('Sacrebleu requires the same number of references for each prediction'				)
      __UpperCamelCase      =	[[refs[i] for refs in references] for i in range(__UpperCAmelCase				)]

      __UpperCamelCase      =	CHRF(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	sb_chrf.corpus_score(__UpperCAmelCase     ,					__UpperCAmelCase				)

      return {
          "score": output.score,
          "char_order": output.char_order,
          "word_order": output.word_order,
          "beta": output.beta,
      }





"""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		(				__SCREAMING_SNAKE_CASE      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.config_class(**self.inputs_dict				)
      self.parent.assertTrue(hasattr(__UpperCAmelCase     ,					'hidden_sizes'				)				)
      self.parent.assertTrue(hasattr(__UpperCAmelCase     ,					'num_attention_heads'				)				)


class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=13     ,					__UpperCAmelCase=64     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=[128, 256, 384]     ,					__UpperCAmelCase=[4, 6, 8]     ,					__UpperCAmelCase=[2, 3, 4]     ,					__UpperCAmelCase=[16, 16, 16]     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=[2, 2, 2]     ,					__UpperCAmelCase=[2, 2, 2]     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=2     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	parent
      __UpperCamelCase      =	batch_size
      __UpperCamelCase      =	image_size
      __UpperCamelCase      =	num_channels
      __UpperCamelCase      =	kernel_size
      __UpperCamelCase      =	stride
      __UpperCamelCase      =	padding
      __UpperCamelCase      =	hidden_sizes
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	depths
      __UpperCamelCase      =	key_dim
      __UpperCamelCase      =	drop_path_rate
      __UpperCamelCase      =	patch_size
      __UpperCamelCase      =	attention_ratio
      __UpperCamelCase      =	mlp_ratio
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	[
          ['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],
      ]
      __UpperCamelCase      =	is_training
      __UpperCamelCase      =	use_labels
      __UpperCamelCase      =	num_labels
      __UpperCamelCase      =	initializer_range





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

      __UpperCamelCase      =	None
      if self.use_labels:
       __UpperCamelCase      =	ids_tensor([self.batch_size]     ,					self.num_labels				)

      __UpperCamelCase      =	self.get_config()
      return config, pixel_values, labels





     def 						UpperCAmelCase (     self				):

      '''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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	LevitModel(config=__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      __UpperCamelCase      =	model(__UpperCAmelCase				)
      __UpperCamelCase      =	(self.image_size, self.image_size)
      __UpperCamelCase  ,     __UpperCamelCase      =	image_size[0], image_size[1]
      for _ in range(4				):
       __UpperCamelCase      =	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
       __UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.num_labels
      __UpperCamelCase      =	LevitForImageClassification(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      __UpperCamelCase      =	model(__UpperCAmelCase     ,					labels=__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.prepare_config_and_inputs()
      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	config_and_inputs
      __UpperCamelCase      =	{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						(
         (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
         if is_torch_available()
         else ()
     )
     lowercase						       =						(
         {
             "feature-extraction": LevitModel,
             "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
         }
         if is_torch_available()
         else {}
     )

     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	LevitModelTester(self				)
      __UpperCamelCase      =	ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase     ,					hidden_size=37				)





     def 						UpperCAmelCase (     self				):

      '''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 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return





     @unittest.skip(reason='Levit does not use inputs_embeds'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip(reason='Levit does not support input and output embeddings'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





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

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       __UpperCamelCase      =	inspect.signature(model.forward				)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __UpperCamelCase      =	[*signature.parameters.keys()]

       __UpperCamelCase      =	['pixel_values']
       self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      def check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()

       with torch.no_grad():
        __UpperCamelCase      =	model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)				)

       __UpperCamelCase      =	outputs.hidden_states

       __UpperCamelCase      =	len(self.model_tester.depths				) + 1
       self.assertEqual(len(__UpperCAmelCase				)     ,					__UpperCAmelCase				)

       __UpperCamelCase      =	(self.model_tester.image_size, self.model_tester.image_size)
       __UpperCamelCase  ,     __UpperCamelCase      =	image_size[0], image_size[1]
       for _ in range(4				):
        __UpperCamelCase      =	floor(
            (
                (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                / self.model_tester.stride
            )
            + 1				)
        __UpperCamelCase      =	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],
           ]     ,					)

      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	True
       check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __UpperCamelCase      =	True

       check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)





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

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=False				):

      '''simple docstring'''




      __UpperCamelCase      =	super()._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)

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

      return inputs_dict





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if not self.model_tester.is_training:
       return

      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase      =	True

      for model_class in self.all_model_classes:
       # LevitForImageClassificationWithTeacher supports inference-only
       if (
           model_class in get_values(__UpperCAmelCase				)
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
        continue
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.train()
       __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)
       __UpperCamelCase      =	model(**__UpperCAmelCase				).loss
       loss.backward()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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

      __UpperCamelCase      =	False
      __UpperCamelCase      =	True

      for model_class in self.all_model_classes:
       if model_class in get_values(__UpperCAmelCase				) or not model_class.supports_gradient_checkpointing:
        continue
       # LevitForImageClassificationWithTeacher supports inference-only
       if model_class.__name__ == "LevitForImageClassificationWithTeacher":
        continue
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.gradient_checkpointing_enable()
       model.to(__UpperCAmelCase				)
       model.train()
       __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)
       __UpperCamelCase      =	model(**__UpperCAmelCase				).loss
       loss.backward()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      __UpperCamelCase      =	[
          {'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(__UpperCAmelCase				),
           ]
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
        continue

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

         __UpperCamelCase      =	model_class(__UpperCAmelCase				)
         model.to(__UpperCAmelCase				)
         model.train()

         __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)

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

         __UpperCamelCase      =	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=__UpperCAmelCase				) as warning_list:
          __UpperCamelCase      =	model(**__UpperCAmelCase				).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 						UpperCAmelCase (     self				):

      '''simple docstring'''




      for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase      =	LevitModel.from_pretrained(__UpperCAmelCase				)
       self.assertIsNotNone(__UpperCAmelCase				)






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
 return image





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





     @cached_property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
          __UpperCAmelCase				)

      __UpperCamelCase      =	self.default_image_processor
      __UpperCamelCase      =	prepare_img()
      __UpperCamelCase      =	image_processor(images=__UpperCAmelCase     ,					return_tensors='pt'				).to(__UpperCAmelCase				)

      # forward pass
      with torch.no_grad():
       __UpperCamelCase      =	model(**__UpperCAmelCase				)

      # verify the logits
      __UpperCamelCase      =	torch.Size((1, 1000)				)
      self.assertEqual(outputs.logits.shape     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]				).to(__UpperCAmelCase				)

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



