"""simple docstring"""







import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class        UpperCamelCase		(			lowercase_						,      unittest.TestCase  ):
   lowercase							=  RobertaTokenizer
   lowercase							=  RobertaTokenizerFast
   lowercase							=  True
   lowercase							=  {'cls_token': '<s>'}
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase_	:				Any													=	[
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        lowercase_	:				List[Any]													=	dict(zip(__UpperCamelCase     ,range(len(__UpperCamelCase			)			)			)			)
        lowercase_	:				str													=	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        lowercase_	:				str													=	{'unk_token': '<unk>'}

        lowercase_	:				Tuple													=	os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['vocab_file']			)
        lowercase_	:				str													=	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(__UpperCamelCase			) + '\n'			)
        with open(self.merges_file     ,'w'     ,encoding='utf-8'			) as fp:
             fp.write('\n'.join(__UpperCamelCase			)			)
   def 			_UpperCAmelCase   (			self     ,**__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        kwargs.update(self.special_tokens_map			)
        return self.tokenizer_class.from_pretrained(self.tmpdirname     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,**__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        kwargs.update(self.special_tokens_map			)
        return RobertaTokenizerFast.from_pretrained(self.tmpdirname     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	'lower newer'
        lowercase_	:				Optional[Any]													=	'lower newer'
        return input_text, output_text
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.tokenizer_class(self.vocab_file     ,self.merges_file     ,**self.special_tokens_map			)
        lowercase_	:				str													=	'lower newer'
        lowercase_	:				str													=	['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
        lowercase_	:				List[str]													=	tokenizer.tokenize(__UpperCamelCase			)  # , add_prefix_space=True)
        self.assertListEqual(__UpperCamelCase     ,__UpperCamelCase			)

        lowercase_	:				Optional[Any]													=	tokens + [tokenizer.unk_token]
        lowercase_	:				Optional[Any]													=	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase			)     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				List[str]													=	self.get_tokenizer()

        self.assertListEqual(tokenizer.encode('Hello world!'     ,add_special_tokens=__UpperCamelCase			)     ,[0, 3_1414, 232, 328, 2]			)
        self.assertListEqual(
            tokenizer.encode('Hello world! cécé herlolip 418'     ,add_special_tokens=__UpperCamelCase			)     ,[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]     ,)
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.tokenizer_class.from_pretrained('roberta-base'			)

        lowercase_	:				List[str]													=	tokenizer.encode('sequence builders'     ,add_special_tokens=__UpperCamelCase			)
        lowercase_	:				List[str]													=	tokenizer.encode('multi-sequence build'     ,add_special_tokens=__UpperCamelCase			)

        lowercase_	:				Dict													=	tokenizer.encode(
            'sequence builders'     ,add_special_tokens=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase			)
        lowercase_	:				List[str]													=	tokenizer.encode(
            'sequence builders'     ,'multi-sequence build'     ,add_special_tokens=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase			)

        lowercase_	:				str													=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase			)
        lowercase_	:				Tuple													=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase     ,__UpperCamelCase			)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	self.get_tokenizer()

        lowercase_	:				Optional[int]													=	'Encode this sequence.'
        lowercase_	:				Dict													=	tokenizer.byte_encoder[' '.encode('utf-8'			)[0]]

        # Testing encoder arguments
        lowercase_	:				Dict													=	tokenizer.encode(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase			)
        lowercase_	:				int													=	tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
        self.assertNotEqual(__UpperCamelCase     ,__UpperCamelCase			)

        lowercase_	:				Optional[int]													=	tokenizer.encode(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase			)
        lowercase_	:				List[str]													=	tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
        self.assertEqual(__UpperCamelCase     ,__UpperCamelCase			)

        tokenizer.add_special_tokens({'bos_token': '<s>'}			)
        lowercase_	:				Any													=	tokenizer.encode(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
        lowercase_	:				int													=	tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
        self.assertNotEqual(__UpperCamelCase     ,__UpperCamelCase			)

        # Testing spaces after special tokens
        lowercase_	:				Union[str, Any]													=	'<mask>'
        tokenizer.add_special_tokens(
            {'mask_token': AddedToken(__UpperCamelCase     ,lstrip=__UpperCamelCase     ,rstrip=__UpperCamelCase			)}			)  # mask token has a left space
        lowercase_	:				Union[str, Any]													=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)

        lowercase_	:				str													=	'Encode <mask> sequence'
        lowercase_	:				Optional[Any]													=	'Encode <mask>sequence'

        lowercase_	:				int													=	tokenizer.encode(__UpperCamelCase			)
        lowercase_	:				List[str]													=	encoded.index(__UpperCamelCase			)
        lowercase_	:				str													=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
        self.assertEqual(__UpperCamelCase     ,__UpperCamelCase			)

        lowercase_	:				str													=	tokenizer.encode(__UpperCamelCase			)
        lowercase_	:				int													=	encoded.index(__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
        self.assertNotEqual(__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                  lowercase_	:				Optional[int]													=	self.rust_tokenizer_class.from_pretrained(__UpperCamelCase     ,**__UpperCamelCase			)
                  lowercase_	:				Union[str, Any]													=	self.tokenizer_class.from_pretrained(__UpperCamelCase     ,**__UpperCamelCase			)
                  lowercase_	:				Any													=	'A, <mask> AllenNLP sentence.'
                  lowercase_	:				Dict													=	tokenizer_r.encode_plus(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,return_token_type_ids=__UpperCamelCase			)
                  lowercase_	:				str													=	tokenizer_p.encode_plus(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,return_token_type_ids=__UpperCamelCase			)

                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r['token_type_ids']			)     ,sum(tokens_p['token_type_ids']			)			)

                  # attention_mask should put 1 everywhere, so sum over length should be 1
                  self.assertEqual(
                      sum(tokens_r['attention_mask']			) / len(tokens_r['attention_mask']			)     ,sum(tokens_p['attention_mask']			) / len(tokens_p['attention_mask']			)     ,)

                  lowercase_	:				Tuple													=	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
                  lowercase_	:				str													=	tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']			)

                  # Rust correctly handles the space before the mask while python doesnt
                  self.assertSequenceEqual(tokens_p['input_ids']     ,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]			)
                  self.assertSequenceEqual(tokens_r['input_ids']     ,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]			)

                  self.assertSequenceEqual(
                      __UpperCamelCase     ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
                  self.assertSequenceEqual(
                      __UpperCamelCase     ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        for trim_offsets, add_prefix_space in itertools.product([True, False]     ,repeat=2			):
             lowercase_	:				Any													=	self.rust_tokenizer_class.from_pretrained(
                 self.tmpdirname     ,use_fast=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase     ,trim_offsets=__UpperCamelCase			)

             lowercase_	:				Optional[int]													=	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
             lowercase_	:				Optional[Any]													=	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

             self.assertEqual(pre_tokenizer_state['add_prefix_space']     ,__UpperCamelCase			)

             self.assertEqual(post_processor_state['add_prefix_space']     ,__UpperCamelCase			)
             self.assertEqual(post_processor_state['trim_offsets']     ,__UpperCamelCase			)







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


        '''simple docstring'''


        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                  lowercase_	:				Union[str, Any]													=	'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                  lowercase_	:				Optional[int]													=	f'''{text_of_1_token} {text_of_1_token}'''

                  lowercase_	:				List[Any]													=	self.rust_tokenizer_class.from_pretrained(
                      __UpperCamelCase     ,use_fast=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase     ,trim_offsets=__UpperCamelCase			)
                  lowercase_	:				Optional[int]													=	tokenizer_r(__UpperCamelCase     ,return_offsets_mapping=__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
                  self.assertEqual(encoding.offset_mapping[0]     ,(0, len(__UpperCamelCase			))			)
                  self.assertEqual(
                      encoding.offset_mapping[1]     ,(len(__UpperCamelCase			) + 1, len(__UpperCamelCase			) + 1 + len(__UpperCamelCase			))     ,)

                  lowercase_	:				str													=	self.rust_tokenizer_class.from_pretrained(
                      __UpperCamelCase     ,use_fast=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase     ,trim_offsets=__UpperCamelCase			)
                  lowercase_	:				Tuple													=	tokenizer_r(__UpperCamelCase     ,return_offsets_mapping=__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
                  self.assertEqual(encoding.offset_mapping[0]     ,(0, len(__UpperCamelCase			))			)
                  self.assertEqual(
                      encoding.offset_mapping[1]     ,(len(__UpperCamelCase			) + 1, len(__UpperCamelCase			) + 1 + len(__UpperCamelCase			))     ,)

                  lowercase_	:				Dict													=	self.rust_tokenizer_class.from_pretrained(
                      __UpperCamelCase     ,use_fast=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase     ,trim_offsets=__UpperCamelCase			)
                  lowercase_	:				Dict													=	tokenizer_r(__UpperCamelCase     ,return_offsets_mapping=__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
                  self.assertEqual(encoding.offset_mapping[0]     ,(0, len(__UpperCamelCase			))			)
                  self.assertEqual(
                      encoding.offset_mapping[1]     ,(len(__UpperCamelCase			), len(__UpperCamelCase			) + 1 + len(__UpperCamelCase			))     ,)

                  lowercase_	:				Union[str, Any]													=	self.rust_tokenizer_class.from_pretrained(
                      __UpperCamelCase     ,use_fast=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase     ,trim_offsets=__UpperCamelCase			)
                  lowercase_	:				Dict													=	tokenizer_r(__UpperCamelCase     ,return_offsets_mapping=__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
                  self.assertEqual(encoding.offset_mapping[0]     ,(0, len(__UpperCamelCase			))			)
                  self.assertEqual(
                      encoding.offset_mapping[1]     ,(len(__UpperCamelCase			), len(__UpperCamelCase			) + 1 + len(__UpperCamelCase			))     ,)

                  lowercase_	:				List[str]													=	f''' {text}'''

                  # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                  #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                  # )
                  # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                  # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                  # self.assertEqual(
                  #     encoding.offset_mapping[1],
                  #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                  # )

                  lowercase_	:				Any													=	self.rust_tokenizer_class.from_pretrained(
                      __UpperCamelCase     ,use_fast=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase     ,trim_offsets=__UpperCamelCase			)
                  lowercase_	:				int													=	tokenizer_r(__UpperCamelCase     ,return_offsets_mapping=__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
                  self.assertEqual(encoding.offset_mapping[0]     ,(1, 1 + len(__UpperCamelCase			))			)
                  self.assertEqual(
                      encoding.offset_mapping[1]     ,(1 + len(__UpperCamelCase			) + 1, 1 + len(__UpperCamelCase			) + 1 + len(__UpperCamelCase			))     ,)

                  lowercase_	:				str													=	self.rust_tokenizer_class.from_pretrained(
                      __UpperCamelCase     ,use_fast=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase     ,trim_offsets=__UpperCamelCase			)
                  lowercase_	:				List[Any]													=	tokenizer_r(__UpperCamelCase     ,return_offsets_mapping=__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
                  self.assertEqual(encoding.offset_mapping[0]     ,(0, 1 + len(__UpperCamelCase			))			)
                  self.assertEqual(
                      encoding.offset_mapping[1]     ,(1 + len(__UpperCamelCase			), 1 + len(__UpperCamelCase			) + 1 + len(__UpperCamelCase			))     ,)

                  lowercase_	:				Optional[int]													=	self.rust_tokenizer_class.from_pretrained(
                      __UpperCamelCase     ,use_fast=__UpperCamelCase     ,add_prefix_space=__UpperCamelCase     ,trim_offsets=__UpperCamelCase			)
                  lowercase_	:				Optional[int]													=	tokenizer_r(__UpperCamelCase     ,return_offsets_mapping=__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
                  self.assertEqual(encoding.offset_mapping[0]     ,(0, 1 + len(__UpperCamelCase			))			)
                  self.assertEqual(
                      encoding.offset_mapping[1]     ,(1 + len(__UpperCamelCase			), 1 + len(__UpperCamelCase			) + 1 + len(__UpperCamelCase			))     ,)


"""simple docstring"""







import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           LevitForImageClassification,
           LevitForImageClassificationWithTeacher,
           LevitModel,
       )
       from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import LevitImageProcessor







class        UpperCamelCase		(			lowercase_  ):
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Any													=	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     ,)	->      str:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	parent
        lowercase_	:				Any													=	batch_size
        lowercase_	:				str													=	image_size
        lowercase_	:				List[Any]													=	num_channels
        lowercase_	:				Any													=	kernel_size
        lowercase_	:				Any													=	stride
        lowercase_	:				List[str]													=	padding
        lowercase_	:				Tuple													=	hidden_sizes
        lowercase_	:				List[Any]													=	num_attention_heads
        lowercase_	:				Optional[int]													=	depths
        lowercase_	:				Union[str, Any]													=	key_dim
        lowercase_	:				List[str]													=	drop_path_rate
        lowercase_	:				Dict													=	patch_size
        lowercase_	:				List[Any]													=	attention_ratio
        lowercase_	:				Optional[int]													=	mlp_ratio
        lowercase_	:				Any													=	initializer_range
        lowercase_	:				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],
        ]
        lowercase_	:				Dict													=	is_training
        lowercase_	:				Tuple													=	use_labels
        lowercase_	:				Dict													=	num_labels
        lowercase_	:				Union[str, Any]													=	initializer_range
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


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

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

        lowercase_	:				List[str]													=	self.get_config()
        return config, pixel_values, labels
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        return LevitConfig(
            image_size=self.image_size     ,num_channels=self.num_channels     ,kernel_size=self.kernel_size     ,stride=self.stride     ,padding=self.padding     ,patch_size=self.patch_size     ,hidden_sizes=self.hidden_sizes     ,num_attention_heads=self.num_attention_heads     ,depths=self.depths     ,key_dim=self.key_dim     ,drop_path_rate=self.drop_path_rate     ,mlp_ratio=self.mlp_ratio     ,attention_ratio=self.attention_ratio     ,initializer_range=self.initializer_range     ,down_ops=self.down_ops     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	LevitModel(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Optional[Any]													=	model(__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	(self.image_size, self.image_size)
        lowercase_							,  lowercase_	:				Tuple													=	image_size[0], image_size[1]
        for _ in range(4			):
             lowercase_	:				Optional[int]													=	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1			)
             lowercase_	:				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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				str													=	self.num_labels
        lowercase_	:				Optional[Any]													=	LevitForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Union[str, Any]													=	model(__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)			)







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


        '''simple docstring'''


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







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

   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	LevitModelTester(self			)
        lowercase_	:				Dict													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,has_text_modality=__UpperCamelCase     ,hidden_size=37			)
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        return
   @unittest.skip(reason='Levit does not use inputs_embeds'			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        pass
   @unittest.skip(reason='Levit does not support input and output embeddings'			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        pass
   @unittest.skip(reason='Levit does not output attentions'			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


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

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

             lowercase_	:				Optional[int]													=	['pixel_values']
             self.assertListEqual(arg_names[:1]     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


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

             with torch.no_grad():
                  lowercase_	:				Optional[int]													=	model(**self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase			)			)

             lowercase_	:				Dict													=	outputs.hidden_states

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

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

        lowercase_							,  lowercase_	:				List[str]													=	self.model_tester.prepare_config_and_inputs_for_common()

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

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_	:				Optional[int]													=	True

             check_hidden_states_output(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase=False			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	super()._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase     ,return_labels=__UpperCamelCase			)

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

        return inputs_dict
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        if not self.model_tester.is_training:
             return

        lowercase_							,  lowercase_	:				Union[str, Any]													=	self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_	:				Optional[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
             lowercase_	:				Dict													=	model_class(__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.train()
             lowercase_	:				List[Any]													=	self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase     ,return_labels=__UpperCamelCase			)
             lowercase_	:				Union[str, Any]													=	model(**__UpperCamelCase			).loss
             loss.backward()
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


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

        lowercase_	:				Union[str, Any]													=	False
        lowercase_	:				Dict													=	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
             lowercase_	:				Dict													=	model_class(__UpperCamelCase			)
             model.gradient_checkpointing_enable()
             model.to(__UpperCamelCase			)
             model.train()
             lowercase_	:				Tuple													=	self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase     ,return_labels=__UpperCamelCase			)
             lowercase_	:				Any													=	model(**__UpperCamelCase			).loss
             loss.backward()
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


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

        lowercase_	:				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(__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"]}'''			):
                       lowercase_	:				Any													=	problem_type['title']
                       lowercase_	:				Tuple													=	problem_type['num_labels']

                       lowercase_	:				Tuple													=	model_class(__UpperCamelCase			)
                       model.to(__UpperCamelCase			)
                       model.train()

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

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

                       lowercase_	:				str													=	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:
                            lowercase_	:				int													=	model(**__UpperCamelCase			).loss
                       for w in warning_list:
                            if "Using a target size that is different to the input size" in str(w.message			):
                                 raise ValueError(
                                     f'''Something is going wrong in the regression problem: intercepted {w.message}'''			)

                       loss.backward()







   @slow
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


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






def 			lowercase__(       ):
     lowercase_	:				Any													=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image







@require_torch
@require_vision
class        UpperCamelCase		(			unittest.TestCase  ):
   @cached_property
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Any													=	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(
            __UpperCamelCase			)

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

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

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

        lowercase_	:				Any													=	torch.tensor([1.0448, -0.3745, -1.8317]			).to(__UpperCamelCase			)

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

