




'''simple docstring'''



import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
 import torch

 from transformers import (
     MarkupLMForQuestionAnswering,
     MarkupLMForSequenceClassification,
     MarkupLMForTokenClassification,
     MarkupLMModel,
 )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			str						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[int]=1_3						,   __lowerCamelCase      :			List[Any]=7						,   __lowerCamelCase      :			int=True						,   __lowerCamelCase      :			List[str]=True						,   __lowerCamelCase      :			Tuple=True						,   __lowerCamelCase      :			Dict=True						,   __lowerCamelCase      :			Dict=9_9						,   __lowerCamelCase      :			List[Any]=3_2						,   __lowerCamelCase      :			List[Any]=5						,   __lowerCamelCase      :			int=4						,   __lowerCamelCase      :			Optional[Any]=3_7						,   __lowerCamelCase      :			Union[str, Any]="gelu"						,   __lowerCamelCase      :			List[str]=0.1						,   __lowerCamelCase      :			int=0.1						,   __lowerCamelCase      :			Union[str, Any]=5_1_2						,   __lowerCamelCase      :			Union[str, Any]=1_6						,   __lowerCamelCase      :			Tuple=2						,   __lowerCamelCase      :			Tuple=0.0_2						,   __lowerCamelCase      :			int=3						,   __lowerCamelCase      :			Dict=None						,   __lowerCamelCase      :			int=2_0						,   __lowerCamelCase      :			str=3_0						,   __lowerCamelCase      :			List[Any]=2						,   __lowerCamelCase      :			List[Any]=2						,   __lowerCamelCase      :			Any=1_0						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   parent
      _SCREAMING_SNAKE_CASE =   batch_size
      _SCREAMING_SNAKE_CASE =   seq_length
      _SCREAMING_SNAKE_CASE =   is_training
      _SCREAMING_SNAKE_CASE =   use_input_mask
      _SCREAMING_SNAKE_CASE =   use_token_type_ids
      _SCREAMING_SNAKE_CASE =   use_labels
      _SCREAMING_SNAKE_CASE =   vocab_size
      _SCREAMING_SNAKE_CASE =   hidden_size
      _SCREAMING_SNAKE_CASE =   num_hidden_layers
      _SCREAMING_SNAKE_CASE =   num_attention_heads
      _SCREAMING_SNAKE_CASE =   intermediate_size
      _SCREAMING_SNAKE_CASE =   hidden_act
      _SCREAMING_SNAKE_CASE =   hidden_dropout_prob
      _SCREAMING_SNAKE_CASE =   attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE =   max_position_embeddings
      _SCREAMING_SNAKE_CASE =   type_vocab_size
      _SCREAMING_SNAKE_CASE =   type_sequence_label_size
      _SCREAMING_SNAKE_CASE =   initializer_range
      _SCREAMING_SNAKE_CASE =   num_labels
      _SCREAMING_SNAKE_CASE =   scope
      _SCREAMING_SNAKE_CASE =   max_xpath_tag_unit_embeddings
      _SCREAMING_SNAKE_CASE =   max_xpath_subs_unit_embeddings
      _SCREAMING_SNAKE_CASE =   tag_pad_id
      _SCREAMING_SNAKE_CASE =   subs_pad_id
      _SCREAMING_SNAKE_CASE =   max_depth



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size					)

      _SCREAMING_SNAKE_CASE =   ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]						,   self.max_xpath_tag_unit_embeddings					)

      _SCREAMING_SNAKE_CASE =   ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]						,   self.max_xpath_subs_unit_embeddings					)

      _SCREAMING_SNAKE_CASE =   None
      if self.use_input_mask:
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   vocab_size=2					)

      _SCREAMING_SNAKE_CASE =   None
      if self.use_token_type_ids:
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size					)

      _SCREAMING_SNAKE_CASE =   None
      _SCREAMING_SNAKE_CASE =   None
      if self.use_labels:
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size]						,   self.type_sequence_label_size					)
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels					)

      _SCREAMING_SNAKE_CASE =   self.get_config()

      return (
          config,
          input_ids,
          xpath_tags_seq,
          xpath_subs_seq,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
      )



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      return MarkupLMConfig(
          vocab_size=self.vocab_size						,   hidden_size=self.hidden_size						,   num_hidden_layers=self.num_hidden_layers						,   num_attention_heads=self.num_attention_heads						,   intermediate_size=self.intermediate_size						,   hidden_act=self.hidden_act						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   initializer_range=self.initializer_range						,   max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings						,   max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings						,   tag_pad_id=self.tag_pad_id						,   subs_pad_id=self.subs_pad_id						,   max_depth=self.max_depth						,   )



   def 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			str						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   MarkupLMModel(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      print("Configs:"						,   model.config.tag_pad_id						,   model.config.subs_pad_id					)
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)					)
      self.parent.assertEqual(result.pooler_output.shape						,   (self.batch_size, self.hidden_size)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Tuple						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_labels
      _SCREAMING_SNAKE_CASE =   MarkupLMForSequenceClassification(__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   xpath_tags_seq=__lowerCamelCase						,   xpath_subs_seq=__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase						,   )
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)					)



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_labels
      _SCREAMING_SNAKE_CASE =   MarkupLMForTokenClassification(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   xpath_tags_seq=__lowerCamelCase						,   xpath_subs_seq=__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase						,   )
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.num_labels)					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Optional[Any]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   MarkupLMForQuestionAnswering(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   xpath_tags_seq=__lowerCamelCase						,   xpath_subs_seq=__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   start_positions=__lowerCamelCase						,   end_positions=__lowerCamelCase						,   )
      self.parent.assertEqual(result.start_logits.shape						,   (self.batch_size, self.seq_length)					)
      self.parent.assertEqual(result.end_logits.shape						,   (self.batch_size, self.seq_length)					)



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.prepare_config_and_inputs()
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   config_and_inputs
      _SCREAMING_SNAKE_CASE =   {
          "input_ids": input_ids,
          "xpath_tags_seq": xpath_tags_seq,
          "xpath_subs_seq": xpath_subs_seq,
          "token_type_ids": token_type_ids,
          "attention_mask": input_mask,
      }
      return config, inputs_dict







@require_torch
class 	lowercase_					(				A  ,						A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		(
       (
           MarkupLMModel,
           MarkupLMForSequenceClassification,
           MarkupLMForTokenClassification,
           MarkupLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   lowerCamelCase_       						=		(
       {
           '''feature-extraction''': MarkupLMModel,
           '''question-answering''': MarkupLMForQuestionAnswering,
           '''text-classification''': MarkupLMForSequenceClassification,
           '''token-classification''': MarkupLMForTokenClassification,
           '''zero-shot''': MarkupLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Tuple					):

      """simple docstring"""
      # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
      # (batch of pretokenized examples).
      return True



   def 							lowerCAmelCase_    (					self      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   MarkupLMModelTester(self					)
      _SCREAMING_SNAKE_CASE =   ConfigTester(self						,   config_class=__lowerCamelCase						,   hidden_size=3_7					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      self.config_tester.run_common_tests()



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase					)



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase					)


def 				SCREAMING_SNAKE_CASE_				(							)				->				int:
   _SCREAMING_SNAKE_CASE =   "\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    "

   return html_string







@require_torch
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @cached_property
   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      # TODO use from_pretrained here
      _SCREAMING_SNAKE_CASE =   MarkupLMFeatureExtractor()
      _SCREAMING_SNAKE_CASE =   MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"					)

      return MarkupLMProcessor(__lowerCamelCase						,   __lowerCamelCase					)



   @slow
   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   MarkupLMModel.from_pretrained("microsoft/markuplm-base"					).to(__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   self.default_processor

      _SCREAMING_SNAKE_CASE =   processor(prepare_html_string()						,   return_tensors="pt"					)
      _SCREAMING_SNAKE_CASE =   inputs.to(__lowerCamelCase					)

      # forward pass
      with torch.no_grad():
         _SCREAMING_SNAKE_CASE =   model(**__lowerCamelCase					)

      # verify the last hidden states
      _SCREAMING_SNAKE_CASE =   torch.Size([1, 1_4, 7_6_8]					)
      self.assertEqual(outputs.last_hidden_state.shape						,   __lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.tensor(
          [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]					).to(__lowerCamelCase					)

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







'''simple docstring'''



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 	lowercase_					:


    """simple docstring"""

    @staticmethod
    def 							lowerCAmelCase_    (					*__lowerCamelCase      :			Union[str, Any]						,   **__lowerCamelCase      :			str					):

       """simple docstring"""
       pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   lowerCamelCase_       						=		TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ImageClassificationPipeline(model=__lowerCamelCase						,   image_processor=__lowerCamelCase						,   top_k=2					)
      _SCREAMING_SNAKE_CASE =   [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"					)

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

      import datasets

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

      # Accepts URL + PIL.Image + lists
      _SCREAMING_SNAKE_CASE =   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(
          __lowerCamelCase						,   [
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
          ]						,   )



   @require_torch
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase					)

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

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   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 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase						,   framework="tf"					)

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

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   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 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   PreTrainedTokenizer()

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

      self.assertIs(image_classifier.tokenizer						,   __lowerCamelCase					)



   @slow
   @require_torch
   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      # Perceiver is not tested by `run_pipeline_test` properly.
      # That is because the type of feature_extractor and model preprocessor need to be kept
      # in sync, which is not the case in the current design
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-conv"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   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"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-fourier"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   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"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-learned"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   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"},
          ]						,   )

