'''simple docstring'''
import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_A		:      Tuple						   =get_tests_dir('''fixtures/spiece.model''')




@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase			(	snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						Any	 =       ErnieMTokenizer
      UpperCAmelCase_	:						str	 =       False
      UpperCAmelCase_	:						str	 =       True
      UpperCAmelCase_	:						Optional[int]	 =       False
      UpperCAmelCase_	:						Any	 =       False







      def      lowerCamelCase__      (  self     ):
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase				:							Tuple									=   ErnieMTokenizer(lowercase  ,    unk_token='''<unk>'''  ,    pad_token='''<pad>'''     )
        tokenizer.save_pretrained(self.tmpdirname     )







      def      lowerCamelCase__      (  self  ,    lowercase     ):
        lowercase				:							str									=   '''this is a test'''
        lowercase				:							str									=   '''this is a test'''
        return input_text, output_text







      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   '''<pad>'''
        lowercase				:							Optional[Any]									=   0

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







      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   list(self.get_tokenizer().get_vocab().keys()     )

        self.assertEqual(vocab_keys[0]  ,    '''<pad>'''     )
        self.assertEqual(vocab_keys[1]  ,    '''<unk>'''     )
        self.assertEqual(vocab_keys[-1]  ,    '''▁eloquent'''     )
        self.assertEqual(len(lowercase     )  ,    3_0000     )







      def      lowerCamelCase__      (  self     ):
        self.assertEqual(self.get_tokenizer().vocab_size  ,    3_0000     )







      def      lowerCamelCase__      (  self     ):
        if not self.test_rust_tokenizer:
          return

        lowercase				:							str									=   self.get_tokenizer()
        lowercase				:							Dict									=   self.get_rust_tokenizer()

        lowercase				:							Tuple									=   '''I was born in 92000, and this is falsé.'''

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

        lowercase				:							Union[str, Any]									=   tokenizer.encode(lowercase  ,    add_special_tokens=lowercase     )
        lowercase				:							Optional[int]									=   rust_tokenizer.encode(lowercase  ,    add_special_tokens=lowercase     )
        self.assertListEqual(lowercase  ,    lowercase     )

        lowercase				:							Tuple									=   self.get_rust_tokenizer()
        lowercase				:							Optional[Any]									=   tokenizer.encode(lowercase     )
        lowercase				:							List[str]									=   rust_tokenizer.encode(lowercase     )
        self.assertListEqual(lowercase  ,    lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Tuple									=   ErnieMTokenizer(lowercase  ,    do_lower_case=lowercase  ,    unk_token='''<unk>'''  ,    pad_token='''<pad>'''     )

        lowercase				:							List[Any]									=   tokenizer.tokenize('''This is a test'''     )
        self.assertListEqual(lowercase  ,    ['''▁this''', '''▁is''', '''▁a''', '''▁test''']     )

        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase     )  ,    [48, 25, 21, 1289]     )

        lowercase				:							Tuple									=   tokenizer.tokenize('''I was born in 92000, and this is falsé.'''     )
        # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
        self.assertListEqual(
            lowercase  ,    ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']     )
        lowercase				:							List[str]									=   tokenizer.convert_tokens_to_ids(lowercase     )
        self.assertListEqual(lowercase  ,    [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]     )

        lowercase				:							str									=   tokenizer.convert_ids_to_tokens(lowercase     )
        self.assertListEqual(
            lowercase  ,    ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']  ,    )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   ErnieMTokenizer(lowercase  ,    unk_token='''<unk>'''  ,    pad_token='''<pad>'''     )

        lowercase				:							Optional[Any]									=   tokenizer.encode('''sequence builders'''     )
        lowercase				:							List[Any]									=   tokenizer.encode('''multi-sequence build'''     )

        lowercase				:							Union[str, Any]									=   tokenizer.build_inputs_with_special_tokens(lowercase     )
        lowercase				:							str									=   tokenizer.build_inputs_with_special_tokens(lowercase  ,    lowercase     )

        assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
        assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
            tokenizer.sep_token_id
        ] + text_a + [tokenizer.sep_token_id]







      @slow
      def      lowerCamelCase__      (  self     ):
        # fmt: off
        lowercase				:							int									=   {'''input_ids''': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 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, 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], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1]]}
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase  ,    model_name='''susnato/ernie-m-base_pytorch'''  ,    sequences=[
                '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
                '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
                '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
                '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
                '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
                '''conditioning on both left and right context in all layers.''',
                '''The quick brown fox jumps over the lazy dog.''',
            ]  ,    )
'''simple docstring'''
import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import NatBackbone, NatForImageClassification, NatModel
 from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class _lowerCAmelCase			:







      def __init__(  self  ,    lowercase  ,    lowercase=13  ,    lowercase=64  ,    lowercase=4  ,    lowercase=3  ,    lowercase=16  ,    lowercase=[1, 2, 1]  ,    lowercase=[2, 4, 8]  ,    lowercase=3  ,    lowercase=2.0  ,    lowercase=True  ,    lowercase=0.0  ,    lowercase=0.0  ,    lowercase=0.1  ,    lowercase="gelu"  ,    lowercase=True  ,    lowercase=0.02  ,    lowercase=1e-5  ,    lowercase=True  ,    lowercase=None  ,    lowercase=True  ,    lowercase=10  ,    lowercase=["stage1", "stage2"]  ,    lowercase=[1, 2]  ,    ):
        lowercase				:							Any									=   parent
        lowercase				:							Optional[Any]									=   batch_size
        lowercase				:							Tuple									=   image_size
        lowercase				:							List[str]									=   patch_size
        lowercase				:							Optional[Any]									=   num_channels
        lowercase				:							str									=   embed_dim
        lowercase				:							List[Any]									=   depths
        lowercase				:							Any									=   num_heads
        lowercase				:							str									=   kernel_size
        lowercase				:							Any									=   mlp_ratio
        lowercase				:							str									=   qkv_bias
        lowercase				:							List[Any]									=   hidden_dropout_prob
        lowercase				:							List[str]									=   attention_probs_dropout_prob
        lowercase				:							Tuple									=   drop_path_rate
        lowercase				:							Dict									=   hidden_act
        lowercase				:							Union[str, Any]									=   patch_norm
        lowercase				:							int									=   layer_norm_eps
        lowercase				:							Any									=   initializer_range
        lowercase				:							Tuple									=   is_training
        lowercase				:							int									=   scope
        lowercase				:							Any									=   use_labels
        lowercase				:							Any									=   num_labels
        lowercase				:							List[Any]									=   out_features
        lowercase				:							List[Any]									=   out_indices







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[Any]									=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

        lowercase				:							List[str]									=   self.get_config()

        return config, pixel_values, labels







      def      lowerCamelCase__      (  self     ):
        return NatConfig(
            num_labels=self.num_labels  ,    image_size=self.image_size  ,    patch_size=self.patch_size  ,    num_channels=self.num_channels  ,    embed_dim=self.embed_dim  ,    depths=self.depths  ,    num_heads=self.num_heads  ,    kernel_size=self.kernel_size  ,    mlp_ratio=self.mlp_ratio  ,    qkv_bias=self.qkv_bias  ,    hidden_dropout_prob=self.hidden_dropout_prob  ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,    drop_path_rate=self.drop_path_rate  ,    hidden_act=self.hidden_act  ,    patch_norm=self.patch_norm  ,    layer_norm_eps=self.layer_norm_eps  ,    initializer_range=self.initializer_range  ,    out_features=self.out_features  ,    out_indices=self.out_indices  ,    )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Any									=   NatModel(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							int									=   model(lowercase     )

        lowercase				:							List[str]									=   (config.image_size // config.patch_size) // (2 ** (len(config.depths     ) - 1))
        lowercase				:							Optional[int]									=   int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

        self.parent.assertEqual(
            result.last_hidden_state.shape  ,    (self.batch_size, expected_height, expected_width, expected_dim)     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Optional[int]									=   NatForImageClassification(lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Dict									=   model(lowercase  ,    labels=lowercase     )
        self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_labels)     )

        # test greyscale images
        lowercase				:							Dict									=   1
        lowercase				:							Optional[Any]									=   NatForImageClassification(lowercase     )
        model.to(lowercase     )
        model.eval()

        lowercase				:							Tuple									=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
        lowercase				:							Dict									=   model(lowercase     )
        self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_labels)     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							int									=   NatBackbone(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							List[Any]									=   model(lowercase     )

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps     )  ,    len(config.out_features     )     )
        self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,    [self.batch_size, model.channels[0], 16, 16]     )

        # verify channels
        self.parent.assertEqual(len(model.channels     )  ,    len(config.out_features     )     )

        # verify backbone works with out_features=None
        lowercase				:							List[Any]									=   None
        lowercase				:							int									=   NatBackbone(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        lowercase				:							Optional[int]									=   model(lowercase     )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps     )  ,    1     )
        self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,    [self.batch_size, model.channels[-1], 4, 4]     )

        # verify channels
        self.parent.assertEqual(len(model.channels     )  ,    1     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   self.prepare_config_and_inputs()
        lowercase				,      lowercase				,      lowercase				:							Optional[int]									=   config_and_inputs
        lowercase				:							List[Any]									=   {'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_natten
@require_torch
class _lowerCAmelCase			(	snake_case							,   snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						List[str]	 =       (
          (
              NatModel,
              NatForImageClassification,
              NatBackbone,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_	:						List[str]	 =       (
          {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
          if is_torch_available()
          else {}
      )
      UpperCAmelCase_	:						List[str]	 =       False

      UpperCAmelCase_	:						str	 =       False
      UpperCAmelCase_	:						Any	 =       False
      UpperCAmelCase_	:						Dict	 =       False
      UpperCAmelCase_	:						Optional[Any]	 =       False







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   NatModelTester(self     )
        lowercase				:							Union[str, Any]									=   ConfigTester(self  ,    config_class=lowercase  ,    embed_dim=37     )







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







      def      lowerCamelCase__      (  self     ):
        return







      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[str]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Tuple									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Union[str, Any]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowercase     )







      @unittest.skip(reason='''Nat does not use inputs_embeds'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      @unittest.skip(reason='''Nat does not use feedforward chunking'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							List[str]									=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowercase				:							str									=   model_class(lowercase     )
          self.assertIsInstance(model.get_input_embeddings()  ,    (nn.Module)     )
          lowercase				:							Any									=   model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(lowercase  ,    nn.Linear     )     )







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							List[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase				:							Tuple									=   ['''pixel_values''']
          self.assertListEqual(arg_names[:1]  ,    lowercase     )







      def      lowerCamelCase__      (  self     ):
        self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Dict									=   model_class(lowercase     )
        model.to(lowercase     )
        model.eval()

        with torch.no_grad():
          lowercase				:							Tuple									=   model(**self._prepare_for_class(lowercase  ,    lowercase     )     )

        lowercase				:							List[Any]									=   outputs.hidden_states

        lowercase				:							Tuple									=   getattr(
            self.model_tester  ,    '''expected_num_hidden_layers'''  ,    len(self.model_tester.depths     ) + 1     )
        self.assertEqual(len(lowercase     )  ,    lowercase     )

        # Nat has a different seq_length
        lowercase				:							Optional[Any]									=   (
            config.patch_size
            if isinstance(config.patch_size  ,    collections.abc.Iterable     )
            else (config.patch_size, config.patch_size)
        )

        lowercase				:							Union[str, Any]									=   image_size[0] // patch_size[0]
        lowercase				:							List[str]									=   image_size[1] // patch_size[1]

        self.assertListEqual(
            list(hidden_states[0].shape[-3:]     )  ,    [height, width, self.model_tester.embed_dim]  ,    )

        if model_class.__name__ != "NatBackbone":
          lowercase				:							str									=   outputs.reshaped_hidden_states
          self.assertEqual(len(lowercase     )  ,    lowercase     )

          lowercase				,      lowercase				,      lowercase				,      lowercase				:							Any									=   reshaped_hidden_states[0].shape
          lowercase				:							Tuple									=   (
              reshaped_hidden_states[0].view(lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ).permute(0  ,    2  ,    3  ,    1     )
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-3:]     )  ,    [height, width, self.model_tester.embed_dim]  ,    )







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Optional[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

        lowercase				:							Any									=   (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,    collections.abc.Iterable     )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

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

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

          self.check_hidden_states_output(lowercase  ,    lowercase  ,    lowercase  ,    lowercase     )







      @slow
      def      lowerCamelCase__      (  self     ):
        for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase				:							Any									=   NatModel.from_pretrained(lowercase     )
          self.assertIsNotNone(lowercase     )







      def      lowerCamelCase__      (  self     ):
        lowercase				,      lowercase				:							Tuple									=   self.model_tester.prepare_config_and_inputs_for_common()

        lowercase				:							Dict									=   _config_zero_init(lowercase     )
        for model_class in self.all_model_classes:
          lowercase				:							int									=   model_class(config=lowercase     )
          for name, param in model.named_parameters():
            if "embeddings" not in name and param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()  ,    [0.0, 1.0]  ,    msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""  ,    )





@require_natten
@require_vision
@require_torch
class _lowerCAmelCase			(	unittest.TestCase					):







      @cached_property
      def      lowerCamelCase__      (  self     ):
        return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''     ) if is_vision_available() else None







      @slow
      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''     ).to(lowercase     )
        lowercase				:							Union[str, Any]									=   self.default_image_processor

        lowercase				:							Optional[int]									=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
        lowercase				:							str									=   image_processor(images=lowercase  ,    return_tensors='''pt'''     ).to(lowercase     )

        # forward pass
        with torch.no_grad():
          lowercase				:							Dict									=   model(**lowercase     )

        # verify the logits
        lowercase				:							List[Any]									=   torch.Size((1, 1000)     )
        self.assertEqual(outputs.logits.shape  ,    lowercase     )
        lowercase				:							Optional[int]									=   torch.tensor([0.3805, -0.8676, -0.3912]     ).to(lowercase     )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,    lowercase  ,    atol=1e-4     )     )





@require_torch
@require_natten
class _lowerCAmelCase			(	unittest.TestCase							,   snake_case					):
      UpperCAmelCase_	:						Union[str, Any]	 =       (NatBackbone,) if is_torch_available() else ()
      UpperCAmelCase_	:						Optional[int]	 =       NatConfig







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[int]									=   NatModelTester(self     )