'''simple docstring'''






import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch




class  a			(							unittest.TestCase					):



  """simple docstring"""
  __lowerCAmelCase					   =       JukeboxTokenizer
  __lowerCAmelCase					   =       {
      'artist': 'Zac Brown Band',
      'genres': 'Country',
      'lyrics': 'I met a traveller from an antique land,\n        Who said "Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ',
  }







  @require_torch
  def  lowercase_	(				self       ):

    '''simple docstring'''







    import torch

    __UpperCAmelCase:       List[Any]		=			JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""       )
    __UpperCAmelCase:       Tuple		=			tokenizer(**self.metas       )["input_ids"]
    # fmt: off
    __UpperCAmelCase:       List[Any]		=			[
        torch.tensor([[
            0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
            76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
            44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
            47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
            76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
            30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
            27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
            45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
            41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
            76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
            76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
            64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
            30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
            27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
            34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
            27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
            41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
            44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
            76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
            32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
            40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
            45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
            31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
            45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
            34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
            31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
            40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
            38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
            76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
            41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
            27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
            46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
            41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
            46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
            41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
            40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
            27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
            76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
            41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
            76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
            27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
            34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
            44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
            40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
            46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
            38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
            40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
            27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
            76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
            76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
            76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
            76, 76]]       ),
        torch.tensor([[0, 0, 0, 1069, 11]]       ),
        torch.tensor([[0, 0, 0, 1069, 11]]       ),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0] ,     EXPECTED_OUTPUT[0]       )       )
    self.assertTrue(torch.allclose(tokens[1] ,     EXPECTED_OUTPUT[1]       )       )
    self.assertTrue(torch.allclose(tokens[2] ,     EXPECTED_OUTPUT[2]       )       )







  @require_torch
  def  lowercase_	(				self       ):

    '''simple docstring'''







    import torch

    __UpperCAmelCase:       int		=			JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""       )
    __UpperCAmelCase:       int		=			tokenizer(**self.metas       )["input_ids"]
    # fmt: off
    __UpperCAmelCase:       Union[str, Any]		=			[
        torch.tensor([[
            0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
            31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
            31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
            40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
            79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
            77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
            27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
            37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
            32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
            77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
            77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
            46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
            77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
            77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
            77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
            77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
            64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
            40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
            40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
            38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
            31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
            41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
            46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
            41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
            31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
            31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
            44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
            31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
            38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
            40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
            27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
            31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
            34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
            31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
            31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
            45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
            31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
            15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
            11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
            45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
            41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
            44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
            46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
            27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
            35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
            77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
            31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
            41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
            77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
            40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
            77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
            27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
            77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
            77, 77, 77, 77, 77, 77]]       ),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]       ),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]       ),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0] ,     EXPECTED_OUTPUT[0]       )       )
    self.assertTrue(torch.allclose(tokens[1] ,     EXPECTED_OUTPUT[1]       )       )
    self.assertTrue(torch.allclose(tokens[2] ,     EXPECTED_OUTPUT[2]       )       )
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 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"hidden_sizes" ) )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"num_attention_heads" ) )





class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=64       ,						lowerCamelCase__=3       ,						lowerCamelCase__=3       ,						lowerCamelCase__=2       ,						lowerCamelCase__=1       ,						lowerCamelCase__=16       ,						lowerCamelCase__=[128, 256, 384]       ,						lowerCamelCase__=[4, 6, 8]       ,						lowerCamelCase__=[2, 3, 4]       ,						lowerCamelCase__=[16, 16, 16]       ,						lowerCamelCase__=0       ,						lowerCamelCase__=[2, 2, 2]       ,						lowerCamelCase__=[2, 2, 2]       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=2       ,						):
       lowerCAmelCase_:							int       =	parent
       lowerCAmelCase_:							Tuple       =	batch_size
       lowerCAmelCase_:							List[str]       =	image_size
       lowerCAmelCase_:							Tuple       =	num_channels
       lowerCAmelCase_:							Optional[int]       =	kernel_size
       lowerCAmelCase_:							int       =	stride
       lowerCAmelCase_:							Optional[int]       =	padding
       lowerCAmelCase_:							Tuple       =	hidden_sizes
       lowerCAmelCase_:							Union[str, Any]       =	num_attention_heads
       lowerCAmelCase_:							Tuple       =	depths
       lowerCAmelCase_:							Optional[int]       =	key_dim
       lowerCAmelCase_:							Optional[Any]       =	drop_path_rate
       lowerCAmelCase_:							List[str]       =	patch_size
       lowerCAmelCase_:							Any       =	attention_ratio
       lowerCAmelCase_:							Tuple       =	mlp_ratio
       lowerCAmelCase_:							Any       =	initializer_range
       lowerCAmelCase_:							List[str]       =	[
           ["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_:							Any       =	is_training
       lowerCAmelCase_:							Optional[int]       =	use_labels
       lowerCAmelCase_:							Dict       =	num_labels
       lowerCAmelCase_:							Any       =	initializer_range




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       lowerCAmelCase_:							Tuple       =	None
       if self.use_labels:
          lowerCAmelCase_:							int       =	ids_tensor([self.batch_size]       ,						self.num_labels )

       lowerCAmelCase_:							Tuple       =	self.get_config()
       return config, pixel_values, labels




    def      _a    (    self ):
       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      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	LevitModel(config=lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__ )
       lowerCAmelCase_:							Tuple       =	(self.image_size, self.image_size)
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	image_size[0], image_size[1]
       for _ in range(4 ):
          lowerCAmelCase_:							Union[str, Any]       =	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
          lowerCAmelCase_:							str       =	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      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	self.num_labels
       lowerCAmelCase_:							str       =	LevitForImageClassification(lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__       ,						labels=lowerCamelCase__ )
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_labels) )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.prepare_config_and_inputs()
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	config_and_inputs
       lowerCAmelCase_:							Tuple       =	{"pixel_values": pixel_values}
       return config, inputs_dict





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






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Any          =    (
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE:				int          =    (
        {
            'feature-extraction': LevitModel,
            'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE:				Optional[int]          =    False
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    False
    SCREAMING_SNAKE_CASE:				str          =    False
    SCREAMING_SNAKE_CASE:				str          =    False
    SCREAMING_SNAKE_CASE:				List[Any]          =    False




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	LevitModelTester(self )
       lowerCAmelCase_:							Union[str, Any]       =	ConfigTester(self       ,						config_class=lowerCamelCase__       ,						has_text_modality=lowerCamelCase__       ,						hidden_size=37 )




    def      _a    (    self ):
       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      _a    (    self ):
       return




    @unittest.skip(reason="Levit does not use inputs_embeds" )
    def      _a    (    self ):
       pass




    @unittest.skip(reason="Levit does not support input and output embeddings" )
    def      _a    (    self ):
       pass




    @unittest.skip(reason="Levit does not output attentions" )
    def      _a    (    self ):
       pass




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase_:							str       =	["pixel_values"]
          self.assertListEqual(arg_names[:1]       ,						lowerCamelCase__ )




    def      _a    (    self ):
       def check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							int       =	model_class(lowerCamelCase__ )
          model.to(lowerCamelCase__ )
          model.eval()

          with torch.no_grad():
             lowerCAmelCase_:							Dict       =	model(**self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__ ) )

          lowerCAmelCase_:							str       =	outputs.hidden_states

          lowerCAmelCase_:							List[Any]       =	len(self.model_tester.depths ) + 1
          self.assertEqual(len(lowerCamelCase__ )       ,						lowerCamelCase__ )

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

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

       for model_class in self.all_model_classes:
          lowerCAmelCase_:							int       =	True
          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowerCAmelCase_:							Union[str, Any]       =	True

          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




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




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=False ):
       lowerCAmelCase_:							List[str]       =	super()._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )

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

       return inputs_dict




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )




    def      _a    (    self ):
       if not self.model_tester.is_training:
          return

       lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase_:							int       =	True

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




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
          return

       lowerCAmelCase_:							Any       =	False
       lowerCAmelCase_:							Tuple       =	True

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




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase_:							Optional[int]       =	[
           {"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(lowerCamelCase__ ),
              ]
              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_:							Optional[Any]       =	problem_type["num_labels"]

                lowerCAmelCase_:							List[str]       =	model_class(lowerCamelCase__ )
                model.to(lowerCamelCase__ )
                model.train()

                lowerCAmelCase_:							List[Any]       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )

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

                lowerCAmelCase_:							List[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=lowerCamelCase__ ) as warning_list:
                   lowerCAmelCase_:							Dict       =	model(**lowerCamelCase__ ).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      _a    (    self ):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_:							Optional[int]       =	LevitModel.from_pretrained(lowerCamelCase__ )
          self.assertIsNotNone(lowerCamelCase__ )





def 		snake_case__	(		):
   lowerCAmelCase_:							Optional[int]       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
   return image







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






    '''simple docstring'''




    @cached_property
    def      _a    (    self ):
       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )




    @slow
    def      _a    (    self ):
       lowerCAmelCase_:							Any       =	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
           lowerCamelCase__ )

       lowerCAmelCase_:							str       =	self.default_image_processor
       lowerCAmelCase_:							int       =	prepare_img()
       lowerCAmelCase_:							Optional[int]       =	image_processor(images=lowerCamelCase__       ,						return_tensors="pt" ).to(lowerCamelCase__ )

       # forward pass
       with torch.no_grad():
          lowerCAmelCase_:							Tuple       =	model(**lowerCamelCase__ )

       # verify the logits
       lowerCAmelCase_:							str       =	torch.Size((1, 1_000) )
       self.assertEqual(outputs.logits.shape       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Union[str, Any]       =	torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(lowerCamelCase__ )

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