



'''simple docstring'''





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class 				_lowerCAmelCase     (   _UpperCAmelCase,				unittest.TestCase				):


 """simple docstring"""

 lowerCamelCase		       =			DebertaTokenizer
 lowerCamelCase		       =			True
 lowerCamelCase		       =			DebertaTokenizerFast







 def 	UpperCAmelCase_    (	self   )  ->  Any:
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    A_   :						List[str]		= [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """[UNK]""",
    ]
    A_   :						str		= dict(zip(lowerCAmelCase__ ,  range(len(lowerCAmelCase__   )   )   )   )
    A_   :						Tuple		= ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    A_   :						str		= {"""unk_token""": """[UNK]"""}

    A_   :						List[str]		= os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES["""vocab_file"""]   )
    A_   :						int		= os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES["""merges_file"""]   )
    with open(self.vocab_file ,  """w""" ,  encoding="""utf-8"""   ) as fp:
       fp.write(json.dumps(lowerCAmelCase__   ) + """\n"""   )
    with open(self.merges_file ,  """w""" ,  encoding="""utf-8"""   ) as fp:
       fp.write("""\n""".join(lowerCAmelCase__   )   )







 def 	UpperCAmelCase_    (	self ,  **_lowerCamelCase   )  ->  Optional[int]:
    kwargs.update(self.special_tokens_map   )
    return self.tokenizer_class.from_pretrained(self.tmpdirname ,  **lowerCAmelCase__   )







 def 	UpperCAmelCase_    (	self ,  _lowerCamelCase   )  ->  Union[str, Any]:
    A_   :						int		= """lower newer"""
    A_   :						int		= """lower newer"""
    return input_text, output_text







 def 	UpperCAmelCase_    (	self   )  ->  Any:
    A_   :						Optional[Any]		= self.get_tokenizer()
    A_   :						str		= """lower newer"""
    A_   :						Any		= ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
    A_   :						str		= tokenizer.tokenize(lowerCAmelCase__   )
    self.assertListEqual(lowerCAmelCase__ ,  lowerCAmelCase__   )

    A_   :						Tuple		= tokens + [tokenizer.unk_token]
    A_   :						Union[str, Any]		= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__   ) ,  lowerCAmelCase__   )







 def 	UpperCAmelCase_    (	self   )  ->  List[str]:
    A_   :						Union[str, Any]		= self.get_tokenizer()
    A_   :						Optional[int]		= tokenizer("""Hello""" ,  """World"""   )
    A_   :						Optional[int]		= [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
    self.assertListEqual(tokd["""token_type_ids"""] ,  lowerCAmelCase__   )







 @slow
 def 	UpperCAmelCase_    (	self   )  ->  Any:
    A_   :						List[Any]		= self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

    A_   :						int		= tokenizer.encode("""sequence builders""" ,  add_special_tokens=lowerCAmelCase__   )
    A_   :						List[str]		= tokenizer.encode("""multi-sequence build""" ,  add_special_tokens=lowerCAmelCase__   )

    A_   :						Tuple		= tokenizer.encode(
        """sequence builders""" ,  add_special_tokens=lowerCAmelCase__ ,  add_prefix_space=lowerCAmelCase__   )
    A_   :						Optional[int]		= tokenizer.encode(
        """sequence builders""" ,  """multi-sequence build""" ,  add_special_tokens=lowerCAmelCase__ ,  add_prefix_space=lowerCAmelCase__   )

    A_   :						str		= tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__   )
    A_   :						str		= tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ ,  lowerCAmelCase__   )

    assert encoded_sentence == encoded_text_from_decode
    assert encoded_pair == encoded_pair_from_decode







 @slow
 def 	UpperCAmelCase_    (	self   )  ->  Optional[Any]:
    A_   :						Dict		= [self.tokenizer_class]
    if self.test_rust_tokenizer:
       tokenizer_classes.append(self.rust_tokenizer_class   )

    for tokenizer_class in tokenizer_classes:
       A_   :						Tuple		= tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

       A_   :						Tuple		= [
           """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
           """ALBERT incorporates two parameter reduction techniques""",
           """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
           """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
           """ vocabulary embedding.""",
       ]

       A_   :						Union[str, Any]		= tokenizer(lowerCAmelCase__ ,  padding=lowerCAmelCase__   )
       A_   :						Optional[int]		= [tokenizer.decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__   ) for seq in encoding["""input_ids"""]]

       # fmt: off
       A_   :						List[str]		= {
           """input_ids""": [
               [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 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, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 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, 0, 0, 0, 0, 0, 0, 0],
               [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
           ],
           """token_type_ids""": [
               [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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
           ],
           """attention_mask""": [
               [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],
               [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, 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]
           ]
       }
       # fmt: on

       A_   :						List[str]		= [
           """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
           """ALBERT incorporates two parameter reduction techniques""",
           """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
           """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
           """ vocabulary embedding.""",
       ]

       self.assertDictEqual(encoding.data ,  lowerCAmelCase__   )

       for expected, decoded in zip(lowerCAmelCase__ ,  lowerCAmelCase__   ):
          self.assertEqual(lowerCAmelCase__ ,  lowerCAmelCase__   )

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      (						_UpperCAmelCase ):


			"""simple docstring"""






			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,   '''hidden_sizes'''						)						)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,   '''num_attention_heads'''						)						)




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=13      ,   lowerCAmelCase__=64      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=[1_28, 2_56, 3_84]      ,   lowerCAmelCase__=[4, 6, 8]      ,   lowerCAmelCase__=[2, 3, 4]      ,   lowerCAmelCase__=[16, 16, 16]      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=[2, 2, 2]      ,   lowerCAmelCase__=[2, 2, 2]      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=2      ,   )     ->					Tuple:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


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

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

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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return LevitConfig(
							    image_size=self.image_size      ,   num_channels=self.num_channels      ,   kernel_size=self.kernel_size      ,   stride=self.stride      ,   padding=self.padding      ,   patch_size=self.patch_size      ,   hidden_sizes=self.hidden_sizes      ,   num_attention_heads=self.num_attention_heads      ,   depths=self.depths      ,   key_dim=self.key_dim      ,   drop_path_rate=self.drop_path_rate      ,   mlp_ratio=self.mlp_ratio      ,   attention_ratio=self.attention_ratio      ,   initializer_range=self.initializer_range      ,   down_ops=self.down_ops      ,   )





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							LevitModel(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__						)
							__lowercase 			=							(self.image_size, self.image_size)
							__lowercase			,     __lowercase 			=							image_size[0], image_size[1]
							for _ in range(4						):
											__lowercase 			=							floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
											__lowercase 			=							floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
							self.parent.assertEqual(
							    result.last_hidden_state.shape      ,   (self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1])      ,   )





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					str:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


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




@require_torch
class    _UpperCamelCase      (						_UpperCAmelCase						,_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : int			 =						(
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			__a  : List[str]			 =						(
			    {
			        '''feature-extraction''': LevitModel,
			        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			__a  : int			 =						False
			__a  : Dict			 =						False
			__a  : Optional[Any]			 =						False
			__a  : Optional[int]			 =						False
			__a  : Dict			 =						False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							LevitModelTester(self						)
							__lowercase 			=							ConfigTester(self      ,   config_class=lowerCAmelCase__      ,   has_text_modality=lowerCAmelCase__      ,   hidden_size=37						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							return





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

							'''simple docstring'''


							pass





			@unittest.skip(reason='''Levit does not support input and output embeddings'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							pass





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

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowercase 			=							['''pixel_values''']
											self.assertListEqual(arg_names[:1]      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							def check_hidden_states_output(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						):
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.to(lowerCAmelCase__						)
											model.eval()

											with torch.no_grad():
															__lowercase 			=							model(**self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__						)						)

											__lowercase 			=							outputs.hidden_states

											__lowercase 			=							len(self.model_tester.depths						) + 1
											self.assertEqual(len(lowerCAmelCase__						)      ,   lowerCAmelCase__						)

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

							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowercase 			=							True
											check_hidden_states_output(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=False						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							if not self.model_tester.is_training:
											return

							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()
							__lowercase 			=							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
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.to(lowerCAmelCase__						)
											model.train()
											__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)
											__lowercase 			=							model(**lowerCAmelCase__						).loss
											loss.backward()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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

							__lowercase 			=							False
							__lowercase 			=							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
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.gradient_checkpointing_enable()
											model.to(lowerCAmelCase__						)
											model.train()
											__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)
											__lowercase 			=							model(**lowerCAmelCase__						).loss
											loss.backward()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							__lowercase 			=							[
							    {'''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']}"						):
																			__lowercase 			=							problem_type['''title''']
																			__lowercase 			=							problem_type['''num_labels''']

																			__lowercase 			=							model_class(lowerCAmelCase__						)
																			model.to(lowerCAmelCase__						)
																			model.train()

																			__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)

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

																			__lowercase 			=							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:
																							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


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





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



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




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


			"""simple docstring"""






			@cached_property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
							    lowerCAmelCase__						)

							__lowercase 			=							self.default_image_processor
							__lowercase 			=							prepare_img()
							__lowercase 			=							image_processor(images=lowerCAmelCase__      ,   return_tensors='''pt'''						).to(lowerCAmelCase__						)

							# forward pass
							with torch.no_grad():
											__lowercase 			=							model(**lowerCAmelCase__						)

							# verify the logits
							__lowercase 			=							torch.Size((1, 10_00)						)
							self.assertEqual(outputs.logits.shape      ,   lowerCAmelCase__						)

							__lowercase 			=							torch.tensor([1.0448, -0.3745, -1.8317]						).to(lowerCAmelCase__						)

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