





'''simple docstring'''





import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_lowerCamelCase			       =      '\\n\n'

_lowerCamelCase			       =      '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

_lowerCamelCase			       =      '\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'







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




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             return datasets.MetricInfo(
                 description=_DESCRIPTION      ,							citation=_CITATION      ,							inputs_description=_KWARGS_DESCRIPTION      ,							features=datasets.Features(
                     {
                         '''input_texts''': datasets.Value('''string'''),
                     })      ,							reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']      ,							)



      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__ = 16      ,							a__ = True      ,							a__=None):



             """simple docstring"""





             if device is not None:
                    assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
                    if device == "gpu":
                           _lowerCamelCase							:     List[str]	=		'''cuda'''
             else:
                    _lowerCamelCase							:     Optional[int]	=		'''cuda''' if torch.cuda.is_available() else '''cpu'''

             _lowerCamelCase							:     Any	=		AutoModelForCausalLM.from_pretrained(A__)
             _lowerCamelCase							:     List[str]	=		model.to(A__)

             _lowerCamelCase							:     str	=		AutoTokenizer.from_pretrained(A__)

             # if batch_size > 1 (which generally leads to padding being required), and
             # if there is not an already assigned pad_token, assign an existing
             # special token to also be the padding token
             if tokenizer.pad_token is None and batch_size > 1:
                    _lowerCamelCase							:     int	=		list(tokenizer.special_tokens_map_extended.values())
                    # check that the model already has at least one special token defined
                    assert (
                        len(A__) > 0
                    ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
                    # assign one of the special tokens to also be the pad token
                    tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]})

             if add_start_token:
                    # leave room for <BOS> token to be added:
                    assert (
                        tokenizer.bos_token is not None
                    ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
                    _lowerCamelCase							:     List[str]	=		model.config.max_length - 1
             else:
                    _lowerCamelCase							:     Tuple	=		model.config.max_length

             _lowerCamelCase							:     Any	=		tokenizer(
                 A__      ,							add_special_tokens=A__      ,							padding=A__      ,							truncation=A__      ,							max_length=A__      ,							return_tensors='''pt'''      ,							return_attention_mask=A__      ,							).to(A__)

             _lowerCamelCase							:     str	=		encodings['''input_ids''']
             _lowerCamelCase							:     Dict	=		encodings['''attention_mask''']

             # check that each input is long enough:
             if add_start_token:
                    assert torch.all(torch.ge(attn_masks.sum(1)      ,							1)), "Each input text must be at least one token long."
             else:
                    assert torch.all(
                        torch.ge(attn_masks.sum(1)      ,							2)), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

             _lowerCamelCase							:     List[Any]	=		[]
             _lowerCamelCase							:     List[Any]	=		CrossEntropyLoss(reduction='''none''')

             for start_index in logging.tqdm(range(0      ,							len(A__)      ,							A__)):
                    _lowerCamelCase							:     Optional[int]	=		min(start_index + batch_size      ,							len(A__))
                    _lowerCamelCase							:     Optional[Any]	=		encoded_texts[start_index:end_index]
                    _lowerCamelCase							:     Any	=		attn_masks[start_index:end_index]

                    if add_start_token:
                           _lowerCamelCase							:     Tuple	=		torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0)).to(A__)
                           _lowerCamelCase							:     Optional[Any]	=		torch.cat([bos_tokens_tensor, encoded_batch]      ,							dim=1)
                           _lowerCamelCase							:     List[str]	=		torch.cat(
                               [torch.ones(bos_tokens_tensor.size()      ,							dtype=torch.intaa).to(A__), attn_mask]      ,							dim=1)

                    _lowerCamelCase							:     Optional[int]	=		encoded_batch

                    with torch.no_grad():
                           _lowerCamelCase							:     int	=		model(A__      ,							attention_mask=A__).logits

                    _lowerCamelCase							:     Union[str, Any]	=		out_logits[..., :-1, :].contiguous()
                    _lowerCamelCase							:     str	=		labels[..., 1:].contiguous()
                    _lowerCamelCase							:     Optional[Any]	=		attn_mask[..., 1:].contiguous()

                    _lowerCamelCase							:     Tuple	=		torch.expa(
                        (loss_fct(shift_logits.transpose(1      ,							2)      ,							A__) * shift_attention_mask_batch).sum(1)
                        / shift_attention_mask_batch.sum(1))

                    ppls += perplexity_batch.tolist()

             return {"perplexities": ppls, "mean_perplexity": np.mean(A__)}








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 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.config_class(**self.inputs_dict)
             self.parent.assertTrue(hasattr(a__      ,							'''hidden_sizes'''))
             self.parent.assertTrue(hasattr(a__      ,							'''num_attention_heads'''))







class 			__A       :




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__=13      ,							a__=64      ,							a__=3      ,							a__=3      ,							a__=2      ,							a__=1      ,							a__=16      ,							a__=[128, 256, 384]      ,							a__=[4, 6, 8]      ,							a__=[2, 3, 4]      ,							a__=[16, 16, 16]      ,							a__=0      ,							a__=[2, 2, 2]      ,							a__=[2, 2, 2]      ,							a__=0.02      ,							a__=True      ,							a__=True      ,							a__=2      ,							):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		parent
             _lowerCamelCase							:     Union[str, Any]	=		batch_size
             _lowerCamelCase							:     Dict	=		image_size
             _lowerCamelCase							:     Dict	=		num_channels
             _lowerCamelCase							:     Any	=		kernel_size
             _lowerCamelCase							:     int	=		stride
             _lowerCamelCase							:     Dict	=		padding
             _lowerCamelCase							:     Union[str, Any]	=		hidden_sizes
             _lowerCamelCase							:     Optional[Any]	=		num_attention_heads
             _lowerCamelCase							:     Any	=		depths
             _lowerCamelCase							:     List[Any]	=		key_dim
             _lowerCamelCase							:     Optional[Any]	=		drop_path_rate
             _lowerCamelCase							:     Union[str, Any]	=		patch_size
             _lowerCamelCase							:     int	=		attention_ratio
             _lowerCamelCase							:     Any	=		mlp_ratio
             _lowerCamelCase							:     List[str]	=		initializer_range
             _lowerCamelCase							:     Dict	=		[
                 ['''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],
             ]
             _lowerCamelCase							:     str	=		is_training
             _lowerCamelCase							:     Tuple	=		use_labels
             _lowerCamelCase							:     List[Any]	=		num_labels
             _lowerCamelCase							:     Union[str, Any]	=		initializer_range


      def 				__snake_case			(		self):



             """simple docstring"""





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

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

             _lowerCamelCase							:     List[Any]	=		self.get_config()
             return config, pixel_values, labels


      def 				__snake_case			(		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 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		LevitModel(config=a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     List[str]	=		model(a__)
             _lowerCamelCase							:     List[str]	=		(self.image_size, self.image_size)
             _lowerCamelCase,  _lowerCamelCase							:     Optional[int]	=		image_size[0], image_size[1]
             for _ in range(4):
                    _lowerCamelCase							:     int	=		floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
                    _lowerCamelCase							:     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 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		self.num_labels
             _lowerCamelCase							:     Dict	=		LevitForImageClassification(a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     List[Any]	=		model(a__      ,							labels=a__)
             self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels))



      def 				__snake_case			(		self):



             """simple docstring"""





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







@require_torch
class 			__A       (      lowerCamelCase__ ,lowerCamelCase__ ,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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		LevitModelTester(self)
             _lowerCamelCase							:     Tuple	=		ConfigTester(self      ,							config_class=a__      ,							has_text_modality=a__      ,							hidden_size=37)


      def 				__snake_case			(		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 				__snake_case			(		self):



             """simple docstring"""





             return


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



             """simple docstring"""





             pass


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



             """simple docstring"""





             pass


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



             """simple docstring"""





             pass


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Any	=		self.model_tester.prepare_config_and_inputs_for_common()

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

                    _lowerCamelCase							:     List[Any]	=		['''pixel_values''']
                    self.assertListEqual(arg_names[:1]      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             def check_hidden_states_output(a__      ,							a__      ,							a__):
                    _lowerCamelCase							:     List[Any]	=		model_class(a__)
                    model.to(a__)
                    model.eval()

                    with torch.no_grad():
                           _lowerCamelCase							:     Any	=		model(**self._prepare_for_class(a__      ,							a__))

                    _lowerCamelCase							:     int	=		outputs.hidden_states

                    _lowerCamelCase							:     List[str]	=		len(self.model_tester.depths) + 1
                    self.assertEqual(len(a__)      ,							a__)

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

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

             for model_class in self.all_model_classes:
                    _lowerCamelCase							:     List[Any]	=		True
                    check_hidden_states_output(a__      ,							a__      ,							a__)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    _lowerCamelCase							:     List[str]	=		True

                    check_hidden_states_output(a__      ,							a__      ,							a__)


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



             """simple docstring"""





             pass


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__=False):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		super()._prepare_for_class(a__      ,							a__      ,							return_labels=a__)

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

             return inputs_dict


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             if not self.model_tester.is_training:
                    return

             _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase							:     Optional[Any]	=		True

             for model_class in self.all_model_classes:
                    # LevitForImageClassificationWithTeacher supports inference-only
                    if (
                        model_class in get_values(a__)
                        or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                    ):
                           continue
                    _lowerCamelCase							:     Optional[Any]	=		model_class(a__)
                    model.to(a__)
                    model.train()
                    _lowerCamelCase							:     Any	=		self._prepare_for_class(a__      ,							a__      ,							return_labels=a__)
                    _lowerCamelCase							:     Tuple	=		model(**a__).loss
                    loss.backward()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                    return

             _lowerCamelCase							:     Union[str, Any]	=		False
             _lowerCamelCase							:     List[str]	=		True

             for model_class in self.all_model_classes:
                    if model_class in get_values(a__) or not model_class.supports_gradient_checkpointing:
                           continue
                    # LevitForImageClassificationWithTeacher supports inference-only
                    if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                           continue
                    _lowerCamelCase							:     List[Any]	=		model_class(a__)
                    model.gradient_checkpointing_enable()
                    model.to(a__)
                    model.train()
                    _lowerCamelCase							:     int	=		self._prepare_for_class(a__      ,							a__      ,							return_labels=a__)
                    _lowerCamelCase							:     Any	=		model(**a__).loss
                    loss.backward()


      def 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Dict	=		[
                 {'''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(a__),
                        ]
                        or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                    ):
                           continue

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

                                  _lowerCamelCase							:     int	=		model_class(a__)
                                  model.to(a__)
                                  model.train()

                                  _lowerCamelCase							:     int	=		self._prepare_for_class(a__      ,							a__      ,							return_labels=a__)

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

                                  _lowerCamelCase							:     Tuple	=		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=a__) as warning_list:
                                         _lowerCamelCase							:     Any	=		model(**a__).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 				__snake_case			(		self):



             """simple docstring"""





             for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase							:     int	=		LevitModel.from_pretrained(a__)
                    self.assertIsNotNone(a__)




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     int	=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       return image







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




      """simple docstring"""







      @cached_property
      def 				__snake_case			(		self):



             """simple docstring"""





             return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
                 a__)

             _lowerCamelCase							:     int	=		self.default_image_processor
             _lowerCamelCase							:     int	=		prepare_img()
             _lowerCamelCase							:     str	=		image_processor(images=a__      ,							return_tensors='''pt''').to(a__)

             # forward pass
             with torch.no_grad():
                    _lowerCamelCase							:     int	=		model(**a__)

             # verify the logits
             _lowerCamelCase							:     List[str]	=		torch.Size((1, 1000))
             self.assertEqual(outputs.logits.shape      ,							a__)

             _lowerCamelCase							:     Optional[Any]	=		torch.tensor([1.0448, -0.3745, -1.8317]).to(a__)

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

