






import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
 from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor





class     __a						:







 def __init__(	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     int							,     SCREAMING_SNAKE_CASE      :     List[str]=3							,     SCREAMING_SNAKE_CASE      :     Dict=32							,     SCREAMING_SNAKE_CASE      :     Dict=3							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=10							,     SCREAMING_SNAKE_CASE      :     List[str]=[10, 20, 30, 40]							,     SCREAMING_SNAKE_CASE      :     int=[1, 1, 2, 1]							,     SCREAMING_SNAKE_CASE      :     Optional[int]=True							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=True							,     SCREAMING_SNAKE_CASE      :     Dict="relu"							,     SCREAMING_SNAKE_CASE      :     List[Any]=3							,     SCREAMING_SNAKE_CASE      :     Any=None							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=["stage2", "stage3", "stage4"]							,     SCREAMING_SNAKE_CASE      :     int=[2, 3, 4]							,     ):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[Any]        =						parent
      UpperCamelCase__    :     Dict        =						batch_size
      UpperCamelCase__    :     List[str]        =						image_size
      UpperCamelCase__    :     List[Any]        =						num_channels
      UpperCamelCase__    :     Union[str, Any]        =						embeddings_size
      UpperCamelCase__    :     Optional[Any]        =						hidden_sizes
      UpperCamelCase__    :     str        =						depths
      UpperCamelCase__    :     Any        =						is_training
      UpperCamelCase__    :     Optional[int]        =						use_labels
      UpperCamelCase__    :     Tuple        =						hidden_act
      UpperCamelCase__    :     str        =						num_labels
      UpperCamelCase__    :     int        =						scope
      UpperCamelCase__    :     List[Any]        =						len(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        =						out_features
      UpperCamelCase__    :     Optional[Any]        =						out_indices







 def       __lowercase      (	self      :     str		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[Any]        =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

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

      return config, pixel_values, labels







 def       __lowercase      (	self      :     Tuple		):


      '''simple docstring'''






      return ResNetConfig(
          num_channels=self.num_channels							,     embeddings_size=self.embeddings_size							,     hidden_sizes=self.hidden_sizes							,     depths=self.depths							,     hidden_act=self.hidden_act							,     num_labels=self.num_labels							,     out_features=self.out_features							,     out_indices=self.out_indices							,     )







 def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     int							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     Any        =						ResNetModel(config=SCREAMING_SNAKE_CASE		)
      model.to(SCREAMING_SNAKE_CASE		)
      model.eval()
      UpperCamelCase__    :     Tuple        =						model(SCREAMING_SNAKE_CASE		)
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape							,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,     )







 def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     List[str]        =						self.num_labels
      UpperCamelCase__    :     Optional[int]        =						ResNetForImageClassification(SCREAMING_SNAKE_CASE		)
      model.to(SCREAMING_SNAKE_CASE		)
      model.eval()
      UpperCamelCase__    :     Any        =						model(SCREAMING_SNAKE_CASE							,     labels=SCREAMING_SNAKE_CASE		)
      self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_labels)		)







 def       __lowercase      (	self      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     Tuple		):


      '''simple docstring'''






      UpperCamelCase__    :     int        =						ResNetBackbone(config=SCREAMING_SNAKE_CASE		)
      model.to(SCREAMING_SNAKE_CASE		)
      model.eval()
      UpperCamelCase__    :     Tuple        =						model(SCREAMING_SNAKE_CASE		)

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps		)							,     len(config.out_features		)		)
      self.parent.assertListEqual(list(result.feature_maps[0].shape		)							,     [self.batch_size, self.hidden_sizes[1], 4, 4]		)

      # verify channels
      self.parent.assertEqual(len(model.channels		)							,     len(config.out_features		)		)
      self.parent.assertListEqual(model.channels							,     config.hidden_sizes[1:]		)

      # verify backbone works with out_features=None
      UpperCamelCase__    :     Any        =						None
      UpperCamelCase__    :     str        =						ResNetBackbone(config=SCREAMING_SNAKE_CASE		)
      model.to(SCREAMING_SNAKE_CASE		)
      model.eval()
      UpperCamelCase__    :     List[str]        =						model(SCREAMING_SNAKE_CASE		)

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

      # verify channels
      self.parent.assertEqual(len(model.channels		)							,     1		)
      self.parent.assertListEqual(model.channels							,     [config.hidden_sizes[-1]]		)







 def       __lowercase      (	self      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     Any        =						self.prepare_config_and_inputs()
      UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__    :     Tuple        =						config_and_inputs
      UpperCamelCase__    :     int        =						{"pixel_values": pixel_values}
      return config, inputs_dict







@require_torch
class     __a						(					A__ ,     A__ ,     unittest.TestCase ):
 _lowerCAmelCase : int 				=				(
     (
         ResNetModel,
         ResNetForImageClassification,
         ResNetBackbone,
     )
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase : str 				=				(
     {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase : List[Any] 				=				True
 _lowerCAmelCase : Union[str, Any] 				=				False
 _lowerCAmelCase : List[str] 				=				False
 _lowerCAmelCase : str 				=				False
 _lowerCAmelCase : List[str] 				=				False







 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      UpperCamelCase__    :     Union[str, Any]        =						ResNetModelTester(self		)
      UpperCamelCase__    :     Optional[Any]        =						ConfigTester(self							,     config_class=SCREAMING_SNAKE_CASE							,     has_text_modality=SCREAMING_SNAKE_CASE		)







 def       __lowercase      (	self      :     Dict		):


      '''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       __lowercase      (	self      :     int		):


      '''simple docstring'''






      return







 @unittest.skip(reason="ResNet does not use inputs_embeds"		)
 def       __lowercase      (	self      :     Tuple		):


      '''simple docstring'''






      pass







 @unittest.skip(reason="ResNet does not support input and output embeddings"		)
 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      pass







 def       __lowercase      (	self      :     Union[str, Any]		):


      '''simple docstring'''






      UpperCamelCase__       ,  UpperCamelCase__    :     int        =						self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__    :     str        =						["pixel_values"]
           self.assertListEqual(arg_names[:1]							,     SCREAMING_SNAKE_CASE		)







 def       __lowercase      (	self      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[str]        =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE		)







 def       __lowercase      (	self      :     List[Any]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE		)







 def       __lowercase      (	self      :     Any		):


      '''simple docstring'''






      UpperCamelCase__       ,  UpperCamelCase__    :     int        =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCamelCase__    :     str        =						model_class(config=SCREAMING_SNAKE_CASE		)
           for name, module in model.named_modules():
                if isinstance(SCREAMING_SNAKE_CASE							,     (nn.BatchNormad, nn.GroupNorm)		):
                     self.assertTrue(
                         torch.all(module.weight == 1		)							,     msg=F'Parameter {name} of model {model_class} seems not properly initialized'							,     )
                     self.assertTrue(
                         torch.all(module.bias == 0		)							,     msg=F'Parameter {name} of model {model_class} seems not properly initialized'							,     )







 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      def check_hidden_states_output(SCREAMING_SNAKE_CASE      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     Optional[int]		):
           UpperCamelCase__    :     Tuple        =						model_class(SCREAMING_SNAKE_CASE		)
           model.to(SCREAMING_SNAKE_CASE		)
           model.eval()

           with torch.no_grad():
                UpperCamelCase__    :     Dict        =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)		)

           UpperCamelCase__    :     Union[str, Any]        =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           UpperCamelCase__    :     List[Any]        =						self.model_tester.num_stages
           self.assertEqual(len(SCREAMING_SNAKE_CASE		)							,     expected_num_stages + 1		)

           # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]		)							,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,     )

      UpperCamelCase__       ,  UpperCamelCase__    :     Union[str, Any]        =						self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase__    :     Tuple        =						["basic", "bottleneck"]
      for model_class in self.all_model_classes:
           for layer_type in layers_type:
                UpperCamelCase__    :     Union[str, Any]        =						layer_type
                UpperCamelCase__    :     Any        =						True
                check_hidden_states_output(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)

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

                check_hidden_states_output(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)







 @unittest.skip(reason="ResNet does not use feedforward chunking"		)
 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      pass







 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      UpperCamelCase__    :     str        =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE		)







 @slow
 def       __lowercase      (	self      :     Dict		):


      '''simple docstring'''






      for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__    :     Tuple        =						ResNetModel.from_pretrained(SCREAMING_SNAKE_CASE		)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE		)







def 	SCREAMING_SNAKE_CASE							(      )				->			int:
     UpperCamelCase__    :     str        =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
     return image





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







 @cached_property
 def       __lowercase      (	self      :     Optional[Any]		):


      '''simple docstring'''






      return (
          AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
          if is_vision_available()
          else None
      )







 @slow
 def       __lowercase      (	self      :     Union[str, Any]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        =						ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     str        =						self.default_image_processor
      UpperCamelCase__    :     Any        =						prepare_img()
      UpperCamelCase__    :     List[str]        =						image_processor(images=SCREAMING_SNAKE_CASE							,     return_tensors="pt"		).to(SCREAMING_SNAKE_CASE		)

      # forward pass
      with torch.no_grad():
           UpperCamelCase__    :     int        =						model(**SCREAMING_SNAKE_CASE		)

      # verify the logits
      UpperCamelCase__    :     Optional[int]        =						torch.Size((1, 10_00)		)
      self.assertEqual(outputs.logits.shape							,     SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Optional[int]        =						torch.tensor([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]		).to(SCREAMING_SNAKE_CASE		)

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







@require_torch
class     __a						(					A__ ,     unittest.TestCase ):
 _lowerCAmelCase : str 				=				(ResNetBackbone,) if is_torch_available() else ()
 _lowerCAmelCase : Any 				=				False
 _lowerCAmelCase : Optional[Any] 				=				ResNetConfig







 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[int]        =						ResNetModelTester(self		)







import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:





 class     __a						:







  @staticmethod
  def       __lowercase      (	*SCREAMING_SNAKE_CASE      :     Optional[Any]							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]		):


       '''simple docstring'''






       pass





@is_pipeline_test
@require_torch_or_tf
@require_vision
class     __a						(					unittest.TestCase ):
 _lowerCAmelCase : Tuple 				=				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 _lowerCAmelCase : str 				=				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







 def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     Optional[Any]		):


      '''simple docstring'''






      UpperCamelCase__    :     Dict        =						ImageClassificationPipeline(model=SCREAMING_SNAKE_CASE							,     image_processor=SCREAMING_SNAKE_CASE							,     top_k=2		)
      UpperCamelCase__    :     Union[str, Any]        =						[
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples







 def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[Any]        =						image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"		)

      self.assertEqual(
          SCREAMING_SNAKE_CASE							,     [
              {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
          ]							,     )

      import datasets

      UpperCamelCase__    :     int        =						datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,     "image"							,     split="test"		)

      # Accepts URL + PIL.Image + lists
      UpperCamelCase__    :     Tuple        =						image_classifier(
          [
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["file"],
              # LA
              dataset[1]["file"],
              # L
              dataset[2]["file"],
          ]		)
      self.assertEqual(
          SCREAMING_SNAKE_CASE							,     [
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
          ]							,     )







 @require_torch
 def       __lowercase      (	self      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        =						"hf-internal-testing/tiny-random-vit"
      UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )

      UpperCamelCase__    :     Optional[Any]        =						image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]							,     top_k=2							,     )
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]							,     )







 @require_tf
 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      UpperCamelCase__    :     str        =						"hf-internal-testing/tiny-random-vit"
      UpperCamelCase__    :     int        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE							,     framework="tf"		)

      UpperCamelCase__    :     List[Any]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )

      UpperCamelCase__    :     int        =						image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]							,     top_k=2							,     )
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]							,     )







 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      UpperCamelCase__    :     Any        =						PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      UpperCamelCase__    :     str        =						pipeline(
          "image-classification"							,     model="hf-internal-testing/tiny-random-vit"							,     tokenizer=SCREAMING_SNAKE_CASE		)

      self.assertIs(image_classifier.tokenizer							,     SCREAMING_SNAKE_CASE		)







 @slow
 @require_torch
 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-conv"		)
      UpperCamelCase__    :     Any        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
              {"score": 0.3_2_1, "label": "tiger cat"},
              {"score": 0.0_5_0_2, "label": "Egyptian cat"},
              {"score": 0.0_1_3_7, "label": "crib, cot"},
              {"score": 0.0_0_7, "label": "radiator"},
          ]							,     )

      UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-fourier"		)
      UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
              {"score": 0.1_3_0_9, "label": "tiger cat"},
              {"score": 0.0_7_2_2, "label": "Egyptian cat"},
              {"score": 0.0_7_0_7, "label": "remote control, remote"},
              {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
          ]							,     )

      UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-learned"		)
      UpperCamelCase__    :     Optional[int]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
              {"score": 0.2_3_6_2, "label": "Egyptian cat"},
              {"score": 0.1_8_5_6, "label": "tiger cat"},
              {"score": 0.0_3_2_4, "label": "remote control, remote"},
              {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
          ]							,     )