
import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class    _snake_case (						__a	,    unittest.TestCase				):
 __lowerCAmelCase	:  Union[str, Any]	   =      BarthezTokenizer
 __lowerCAmelCase	:  str	   =      BarthezTokenizerFast
 __lowerCAmelCase	:  List[Any]	   =      True
 __lowerCAmelCase	:  Union[str, Any]	   =      True


 def   lowercase__  (       self):


   '''simple docstring'''




   super().setUp()

   lowercase__	: Optional[int]     		=   BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""")
   tokenizer.save_pretrained(self.tmpdirname)
   tokenizer.save_pretrained(self.tmpdirname       ,	legacy_format=__UpperCamelCase)
   lowercase__	: str     		=   tokenizer


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   """<pad>"""
   lowercase__	: Any     		=   1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase)       ,	__UpperCamelCase)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase)       ,	__UpperCamelCase)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   list(self.get_tokenizer().get_vocab().keys())

   self.assertEqual(vocab_keys[0]       ,	"""<s>""")
   self.assertEqual(vocab_keys[1]       ,	"""<pad>""")
   self.assertEqual(vocab_keys[-1]       ,	"""<mask>""")
   self.assertEqual(len(__UpperCamelCase)       ,	10_11_22)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.assertEqual(self.get_tokenizer().vocab_size       ,	10_11_22)


 @require_torch
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   lowercase__	: List[str]     		=   [0, 57, 30_18, 7_03_07, 91, 2]

   lowercase__	: Union[str, Any]     		=   self.tokenizer(
       __UpperCamelCase       ,	max_length=len(__UpperCamelCase)       ,	padding=__UpperCamelCase       ,	truncation=__UpperCamelCase       ,	return_tensors="""pt""")
   self.assertIsInstance(__UpperCamelCase       ,	__UpperCamelCase)

   self.assertEqual((2, 6)       ,	batch.input_ids.shape)
   self.assertEqual((2, 6)       ,	batch.attention_mask.shape)
   lowercase__	: Tuple     		=   batch.input_ids.tolist()[0]
   self.assertListEqual(__UpperCamelCase       ,	__UpperCamelCase)


 def   lowercase__  (       self):


   '''simple docstring'''




   if not self.test_rust_tokenizer:
     return

   lowercase__	: Any     		=   self.get_tokenizer()
   lowercase__	: Optional[Any]     		=   self.get_rust_tokenizer()

   lowercase__	: Tuple     		=   """I was born in 92000, and this is falsé."""

   lowercase__	: Union[str, Any]     		=   tokenizer.tokenize(__UpperCamelCase)
   lowercase__	: Union[str, Any]     		=   rust_tokenizer.tokenize(__UpperCamelCase)
   self.assertListEqual(__UpperCamelCase       ,	__UpperCamelCase)

   lowercase__	: List[Any]     		=   tokenizer.encode(__UpperCamelCase       ,	add_special_tokens=__UpperCamelCase)
   lowercase__	: Tuple     		=   rust_tokenizer.encode(__UpperCamelCase       ,	add_special_tokens=__UpperCamelCase)
   self.assertListEqual(__UpperCamelCase       ,	__UpperCamelCase)

   lowercase__	: Dict     		=   self.get_rust_tokenizer()
   lowercase__	: List[str]     		=   tokenizer.encode(__UpperCamelCase)
   lowercase__	: Tuple     		=   rust_tokenizer.encode(__UpperCamelCase)
   self.assertListEqual(__UpperCamelCase       ,	__UpperCamelCase)



 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   {"""input_ids""": [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
   # fmt: on

   # moussaKam/mbarthez is a french model. So we also use french texts.
   lowercase__	: Any     		=   [
       """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
       """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
       """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
       """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
       """telles que la traduction et la synthèse de texte.""",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=__UpperCamelCase       ,	model_name="""moussaKam/mbarthez"""       ,	revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""       ,	sequences=__UpperCamelCase       ,	)







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        UpperCamelCase						(				__a				):





						def        A_							(self						)				->				Any:
												UpperCamelCase_		:							Dict  =		self.config_class(**self.inputs_dict						)
												self.parent.assertTrue(hasattr(__UpperCamelCase			,     """hidden_sizes"""						)						)
												self.parent.assertTrue(hasattr(__UpperCamelCase			,     """num_attention_heads"""						)						)


class        UpperCamelCase						:





						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=0.02			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=2			,     )				->				Optional[int]:
												UpperCamelCase_		:							Tuple  =		parent
												UpperCamelCase_		:							Optional[Any]  =		batch_size
												UpperCamelCase_		:							Dict  =		image_size
												UpperCamelCase_		:							Dict  =		num_channels
												UpperCamelCase_		:							Optional[Any]  =		kernel_size
												UpperCamelCase_		:							int  =		stride
												UpperCamelCase_		:							str  =		padding
												UpperCamelCase_		:							Tuple  =		hidden_sizes
												UpperCamelCase_		:							int  =		num_attention_heads
												UpperCamelCase_		:							List[str]  =		depths
												UpperCamelCase_		:							Dict  =		key_dim
												UpperCamelCase_		:							Any  =		drop_path_rate
												UpperCamelCase_		:							List[Any]  =		patch_size
												UpperCamelCase_		:							Any  =		attention_ratio
												UpperCamelCase_		:							Optional[Any]  =		mlp_ratio
												UpperCamelCase_		:							Optional[int]  =		initializer_range
												UpperCamelCase_		:							Optional[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],
												]
												UpperCamelCase_		:							Tuple  =		is_training
												UpperCamelCase_		:							Any  =		use_labels
												UpperCamelCase_		:							Dict  =		num_labels
												UpperCamelCase_		:							List[str]  =		initializer_range





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							Optional[Any]  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

												UpperCamelCase_		:							Any  =		self.get_config()
												return config, pixel_values, labels





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





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							Tuple  =		self.prepare_config_and_inputs()
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Any  =		config_and_inputs
												UpperCamelCase_		:							Tuple  =		{"""pixel_values""": pixel_values}
												return config, inputs_dict


@require_torch
class        UpperCamelCase						(				__a	,	__a	,	unittest.TestCase				):
						a__		:Any      =			(
						    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
						    if is_torch_available()
						    else ()
						)
						a__		:str      =			(
						    {
						        '''feature-extraction''': LevitModel,
						        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

						a__		:Optional[Any]      =			False
						a__		:Optional[int]      =			False
						a__		:Tuple      =			False
						a__		:List[str]      =			False
						a__		:Dict      =			False





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							int  =		LevitModelTester(self						)
												UpperCamelCase_		:							Union[str, Any]  =		ConfigTester(self			,     config_class=__UpperCamelCase			,     has_text_modality=__UpperCamelCase			,     hidden_size=37						)





						def        A_							(self						)				->				Dict:
												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						)				->				Optional[Any]:
												return





						@unittest.skip(reason="""Levit does not use inputs_embeds"""						)
						def        A_							(self						)				->				int:
												pass





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





						@unittest.skip(reason="""Levit does not output attentions"""						)
						def        A_							(self						)				->				List[str]:
												pass





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_,UpperCamelCase_		:							Any  =		self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCamelCase_		:							Tuple  =		["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]			,     __UpperCamelCase						)





						def        A_							(self						)				->				Optional[Any]:
												def check_hidden_states_output(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						):
																		UpperCamelCase_		:							List[str]  =		model_class(__UpperCamelCase						)
																		model.to(__UpperCamelCase						)
																		model.eval()

																		with torch.no_grad():
																								UpperCamelCase_		:							Union[str, Any]  =		model(**self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase						)						)

																		UpperCamelCase_		:							Optional[Any]  =		outputs.hidden_states

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

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

												for model_class in self.all_model_classes:
																		UpperCamelCase_		:							Union[str, Any]  =		True
																		check_hidden_states_output(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		UpperCamelCase_		:							List[str]  =		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        A_							(self						)				->				Dict:
												pass





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=False						)				->				Tuple:
												UpperCamelCase_		:							List[str]  =		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        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Tuple  =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase						)





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							int  =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)





						def        A_							(self						)				->				Optional[Any]:
												if not self.model_tester.is_training:
																		return

												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		self.model_tester.prepare_config_and_inputs_for_common()
												UpperCamelCase_		:							List[Any]  =		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_		:							int  =		model_class(__UpperCamelCase						)
																		model.to(__UpperCamelCase						)
																		model.train()
																		UpperCamelCase_		:							List[str]  =		self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)
																		UpperCamelCase_		:							int  =		model(**__UpperCamelCase						).loss
																		loss.backward()





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.model_tester.prepare_config_and_inputs_for_common()
												if not self.model_tester.is_training:
																		return

												UpperCamelCase_		:							Tuple  =		False
												UpperCamelCase_		:							str  =		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_		:							str  =		model_class(__UpperCamelCase						)
																		model.gradient_checkpointing_enable()
																		model.to(__UpperCamelCase						)
																		model.train()
																		UpperCamelCase_		:							Optional[Any]  =		self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)
																		UpperCamelCase_		:							Optional[Any]  =		model(**__UpperCamelCase						).loss
																		loss.backward()





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		self.model_tester.prepare_config_and_inputs_for_common()

												UpperCamelCase_		:							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(__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_		:							Any  =		problem_type["""title"""]
																														UpperCamelCase_		:							Dict  =		problem_type["""num_labels"""]

																														UpperCamelCase_		:							Dict  =		model_class(__UpperCamelCase						)
																														model.to(__UpperCamelCase						)
																														model.train()

																														UpperCamelCase_		:							Optional[int]  =		self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)

																														if problem_type["num_labels"] > 1:
																																				UpperCamelCase_		:							Union[str, Any]  =		inputs["""labels"""].unsqueeze(1						).repeat(1			,     problem_type["""num_labels"""]						)

																														UpperCamelCase_		:							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=__UpperCamelCase						) as warning_list:
																																				UpperCamelCase_		:							str  =		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        A_							(self						)				->				Dict:
												for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCamelCase_		:							Any  =		LevitModel.from_pretrained(__UpperCamelCase						)
																		self.assertIsNotNone(__UpperCamelCase						)






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							str  =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						return image







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





						@cached_property
						def        A_							(self						)				->				Any:
												return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							Tuple  =		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
												    __UpperCamelCase						)

												UpperCamelCase_		:							Optional[Any]  =		self.default_image_processor
												UpperCamelCase_		:							List[str]  =		prepare_img()
												UpperCamelCase_		:							Optional[int]  =		image_processor(images=__UpperCamelCase			,     return_tensors="""pt"""						).to(__UpperCamelCase						)

												# forward pass
												with torch.no_grad():
																		UpperCamelCase_		:							Union[str, Any]  =		model(**__UpperCamelCase						)

												# verify the logits
												UpperCamelCase_		:							List[str]  =		torch.Size((1, 1_000)						)
												self.assertEqual(outputs.logits.shape			,     __UpperCamelCase						)

												UpperCamelCase_		:							Any  =		torch.tensor([1.0_448, -0.3_745, -1.8_317]						).to(__UpperCamelCase						)

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






