





import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

     if is_vision_available():
          from transformers import OneFormerImageProcessor
          from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
          from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
     from PIL import Image
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       List[str]="shi-labs/oneformer_demo"						):





  """simple docstring"""



  with open(hf_hub_download(snake_case__	,	snake_case__	,	repo_type="dataset"						)	,	"r"						) as f:
    a__     : Optional[int]  =json.load(snake_case__						)
  a__     : Optional[int]  ={}
  a__     : Dict  =[]
  a__     : Any  =[]
  for key, info in class_info.items():
    a__     : List[Any]  =info["name"]
    class_names.append(info["name"]						)
    if info["isthing"]:
      thing_ids.append(int(snake_case__						)						)
  a__     : Optional[Any]  =thing_ids
  a__     : Union[str, Any]  =class_names
  return metadata







class     __lowerCAmelCase	(   unittest.TestCase):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=7		,				lowerCAmelCase__=3		,				lowerCAmelCase__=3_0		,				lowerCAmelCase__=4_0_0		,				lowerCAmelCase__=None		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				lowerCAmelCase__=[0.5, 0.5, 0.5]		,				lowerCAmelCase__=[0.5, 0.5, 0.5]		,				lowerCAmelCase__=1_0		,				lowerCAmelCase__=False		,				lowerCAmelCase__=2_5_5		,				lowerCAmelCase__="shi-labs/oneformer_demo"		,				lowerCAmelCase__="ade20k_panoptic.json"		,				lowerCAmelCase__=1_0		,				)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Dict  =parent
        a__     : Optional[Any]  =batch_size
        a__     : str  =num_channels
        a__     : Optional[Any]  =min_resolution
        a__     : Any  =max_resolution
        a__     : List[str]  =do_resize
        a__     : Dict  ={"shortest_edge": 3_2, "longest_edge": 1_3_3_3} if size is None else size
        a__     : List[Any]  =do_normalize
        a__     : Dict  =image_mean
        a__     : Union[str, Any]  =image_std
        a__     : str  =class_info_file
        a__     : Optional[Any]  =prepare_metadata(lowerCAmelCase_		,				lowerCAmelCase_							)
        a__     : List[str]  =num_text
        a__     : List[Any]  =repo_path

        # for the post_process_functions
        a__     : int  =2
        a__     : Optional[Any]  =1_0
        a__     : List[str]  =1_0
        a__     : Any  =3
        a__     : str  =4
        a__     : List[str]  =num_labels
        a__     : Optional[Any]  =do_reduce_labels
        a__     : Union[str, Any]  =ignore_index







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



        '''simple docstring'''




        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "num_labels": self.num_labels,
            "do_reduce_labels": self.do_reduce_labels,
            "ignore_index": self.ignore_index,
            "class_info_file": self.class_info_file,
            "metadata": self.metadata,
            "num_text": self.num_text,
        }







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__=False							)       ->						Any:



        '''simple docstring'''




        if not batched:
          a__     : List[str]  =image_inputs[0]
          if isinstance(lowerCAmelCase_		,				Image.Image							):
            a__ ,							a__     : Union[str, Any]  =image.size
          else:
            a__ ,							a__     : Union[str, Any]  =image.shape[1], image.shape[2]
          if w < h:
            a__     : Dict  =int(self.size["shortest_edge"] * h / w							)
            a__     : List[str]  =self.size["shortest_edge"]
          elif w > h:
            a__     : List[Any]  =self.size["shortest_edge"]
            a__     : List[str]  =int(self.size["shortest_edge"] * w / h							)
          else:
            a__     : int  =self.size["shortest_edge"]
            a__     : Optional[Any]  =self.size["shortest_edge"]

        else:
          a__     : str  =[]
          for image in image_inputs:
            a__ ,							a__     : int  =self.get_expected_values([image]							)
            expected_values.append((expected_height, expected_width)							)
          a__     : Any  =max(lowerCAmelCase_		,				key=lambda lowerCAmelCase__							: item[0]							)[0]
          a__     : Any  =max(lowerCAmelCase_		,				key=lambda lowerCAmelCase__							: item[1]							)[1]

        return expected_height, expected_width







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        return OneFormerForUniversalSegmentationOutput(
            # +1 for null class
            class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)							)		,				masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)							)		,				)







@require_torch
@require_vision
class     __lowerCAmelCase	(   __lowerCAmelCase						,    unittest.TestCase):
      _lowercase    : Optional[int]            =      OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      _lowercase    : Any            =      image_processing_class







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Optional[int]  =OneFormerImageProcessorTester(self							)







      @property
      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        return self.image_processing_tester.prepare_image_processor_dict()







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



        '''simple docstring'''




        a__     : int  =self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"image_mean"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"image_std"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"do_normalize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"size"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"ignore_index"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"class_info_file"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"num_text"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"repo_path"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"metadata"							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"do_reduce_labels"							)							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        pass







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



        '''simple docstring'''




        a__     : Dict  =self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__     : Dict  =prepare_image_inputs(self.image_processing_tester		,				equal_resolution=lowerCAmelCase_							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase_		,				Image.Image							)

        # Test not batched input
        a__     : Optional[int]  =image_processor(image_inputs[0]		,				["semantic"]		,				return_tensors="pt"							).pixel_values

        a__ ,							a__     : str  =self.image_processing_tester.get_expected_values(lowerCAmelCase_							)

        self.assertEqual(
            encoded_images.shape		,				(1, self.image_processing_tester.num_channels, expected_height, expected_width)		,				)

        # Test batched
        a__ ,							a__     : Tuple  =self.image_processing_tester.get_expected_values(lowerCAmelCase_		,				batched=lowerCAmelCase_							)

        a__     : List[str]  =image_processor(
            lowerCAmelCase_		,				["semantic"] * len(lowerCAmelCase_							)		,				return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processing_tester.batch_size,
                self.image_processing_tester.num_channels,
                expected_height,
                expected_width,
            )		,				)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : int  =self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__     : Any  =prepare_image_inputs(self.image_processing_tester		,				equal_resolution=lowerCAmelCase_		,				numpify=lowerCAmelCase_							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase_		,				np.ndarray							)

        # Test not batched input
        a__     : Dict  =image_processor(image_inputs[0]		,				["semantic"]		,				return_tensors="pt"							).pixel_values

        a__ ,							a__     : List[Any]  =self.image_processing_tester.get_expected_values(lowerCAmelCase_							)

        self.assertEqual(
            encoded_images.shape		,				(1, self.image_processing_tester.num_channels, expected_height, expected_width)		,				)

        # Test batched
        a__ ,							a__     : Optional[Any]  =self.image_processing_tester.get_expected_values(lowerCAmelCase_		,				batched=lowerCAmelCase_							)

        a__     : List[str]  =image_processor(
            lowerCAmelCase_		,				["semantic"] * len(lowerCAmelCase_							)		,				return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processing_tester.batch_size,
                self.image_processing_tester.num_channels,
                expected_height,
                expected_width,
            )		,				)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Optional[int]  =self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__     : str  =prepare_image_inputs(self.image_processing_tester		,				equal_resolution=lowerCAmelCase_		,				torchify=lowerCAmelCase_							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase_		,				torch.Tensor							)

        # Test not batched input
        a__     : List[Any]  =image_processor(image_inputs[0]		,				["semantic"]		,				return_tensors="pt"							).pixel_values

        a__ ,							a__     : int  =self.image_processing_tester.get_expected_values(lowerCAmelCase_							)

        self.assertEqual(
            encoded_images.shape		,				(1, self.image_processing_tester.num_channels, expected_height, expected_width)		,				)

        # Test batched
        a__ ,							a__     : Optional[Any]  =self.image_processing_tester.get_expected_values(lowerCAmelCase_		,				batched=lowerCAmelCase_							)

        a__     : Optional[Any]  =image_processor(
            lowerCAmelCase_		,				["semantic"] * len(lowerCAmelCase_							)		,				return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processing_tester.batch_size,
                self.image_processing_tester.num_channels,
                expected_height,
                expected_width,
            )		,				)







      def        _lowercase  (   self		,				lowerCAmelCase__=False		,				lowerCAmelCase__=False		,				lowerCAmelCase__="np"							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.image_processing_class(**self.image_processor_dict							)
        # prepare image and target
        a__     : Any  =self.image_processing_tester.num_labels
        a__     : Union[str, Any]  =None
        a__     : str  =None
        a__     : Optional[int]  =prepare_image_inputs(self.image_processing_tester		,				equal_resolution=lowerCAmelCase_							)
        if with_segmentation_maps:
          a__     : str  =num_labels
          if is_instance_map:
            a__     : int  =list(range(lowerCAmelCase_							)							) * 2
            a__     : List[Any]  =dict(enumerate(lowerCAmelCase_							)							)
          a__     : str  =[
              np.random.randint(0		,				high * 2		,				(img.size[1], img.size[0])							).astype(np.uinta							) for img in image_inputs
          ]
          if segmentation_type == "pil":
            a__     : Tuple  =[Image.fromarray(lowerCAmelCase_							) for annotation in annotations]

        a__     : Tuple  =image_processor(
            lowerCAmelCase_		,				["semantic"] * len(lowerCAmelCase_							)		,				lowerCAmelCase_		,				return_tensors="pt"		,				instance_id_to_semantic_id=lowerCAmelCase_		,				pad_and_return_pixel_mask=lowerCAmelCase_		,				)

        return inputs







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



        '''simple docstring'''




        pass







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        def common(lowerCAmelCase__=False		,				lowerCAmelCase__=None							):
          a__     : List[str]  =self.comm_get_image_processor_inputs(
              with_segmentation_maps=lowerCAmelCase_		,				is_instance_map=lowerCAmelCase_		,				segmentation_type=lowerCAmelCase_							)

          a__     : Tuple  =inputs["mask_labels"]
          a__     : Dict  =inputs["class_labels"]
          a__     : Optional[Any]  =inputs["pixel_values"]
          a__     : Dict  =inputs["text_inputs"]

          # check the batch_size
          for mask_label, class_label, text_input in zip(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							):
            self.assertEqual(mask_label.shape[0]		,				class_label.shape[0]							)
            # this ensure padding has happened
            self.assertEqual(mask_label.shape[1:]		,				pixel_values.shape[2:]							)
            self.assertEqual(len(lowerCAmelCase_							)		,				self.image_processing_tester.num_text							)

        common()
        common(is_instance_map=lowerCAmelCase_							)
        common(is_instance_map=lowerCAmelCase_		,				segmentation_type="pil"							)
        common(is_instance_map=lowerCAmelCase_		,				segmentation_type="pil"							)







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



        '''simple docstring'''




        a__     : Tuple  =np.zeros((2_0, 5_0)							)
        a__     : Union[str, Any]  =1
        a__     : Optional[int]  =1
        a__     : Optional[int]  =1

        a__     : Tuple  =binary_mask_to_rle(lowerCAmelCase_							)
        self.assertEqual(len(lowerCAmelCase_							)		,				4							)
        self.assertEqual(rle[0]		,				2_1							)
        self.assertEqual(rle[1]		,				4_5							)







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



        '''simple docstring'''




        a__     : int  =self.image_processing_class(
            num_labels=self.image_processing_tester.num_classes		,				max_seq_length=7_7		,				task_seq_length=7_7		,				class_info_file="ade20k_panoptic.json"		,				num_text=self.image_processing_tester.num_text		,				repo_path="shi-labs/oneformer_demo"		,				)
        a__     : Tuple  =self.image_processing_tester.get_fake_oneformer_outputs()

        a__     : Any  =fature_extractor.post_process_semantic_segmentation(lowerCAmelCase_							)

        self.assertEqual(len(lowerCAmelCase_							)		,				self.image_processing_tester.batch_size							)
        self.assertEqual(
            segmentation[0].shape		,				(
                self.image_processing_tester.height,
                self.image_processing_tester.width,
            )		,				)

        a__     : Optional[Any]  =[(1, 4) for i in range(self.image_processing_tester.batch_size							)]
        a__     : str  =fature_extractor.post_process_semantic_segmentation(lowerCAmelCase_		,				target_sizes=lowerCAmelCase_							)

        self.assertEqual(segmentation[0].shape		,				target_sizes[0]							)







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : Tuple  =self.image_processing_class(
            num_labels=self.image_processing_tester.num_classes		,				max_seq_length=7_7		,				task_seq_length=7_7		,				class_info_file="ade20k_panoptic.json"		,				num_text=self.image_processing_tester.num_text		,				repo_path="shi-labs/oneformer_demo"		,				)
        a__     : Optional[Any]  =self.image_processing_tester.get_fake_oneformer_outputs()
        a__     : Tuple  =image_processor.post_process_instance_segmentation(lowerCAmelCase_		,				threshold=0							)

        self.assertTrue(len(lowerCAmelCase_							) == self.image_processing_tester.batch_size							)
        for el in segmentation:
          self.assertTrue("segmentation" in el							)
          self.assertTrue("segments_info" in el							)
          self.assertEqual(type(el["segments_info"]							)		,				lowerCAmelCase_							)
          self.assertEqual(
              el["segmentation"].shape		,				(self.image_processing_tester.height, self.image_processing_tester.width)							)







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



        '''simple docstring'''




        a__     : List[Any]  =self.image_processing_class(
            num_labels=self.image_processing_tester.num_classes		,				max_seq_length=7_7		,				task_seq_length=7_7		,				class_info_file="ade20k_panoptic.json"		,				num_text=self.image_processing_tester.num_text		,				repo_path="shi-labs/oneformer_demo"		,				)
        a__     : Optional[Any]  =self.image_processing_tester.get_fake_oneformer_outputs()
        a__     : Union[str, Any]  =image_processor.post_process_panoptic_segmentation(lowerCAmelCase_		,				threshold=0							)

        self.assertTrue(len(lowerCAmelCase_							) == self.image_processing_tester.batch_size							)
        for el in segmentation:
          self.assertTrue("segmentation" in el							)
          self.assertTrue("segments_info" in el							)
          self.assertEqual(type(el["segments_info"]							)		,				lowerCAmelCase_							)
          self.assertEqual(
              el["segmentation"].shape		,				(self.image_processing_tester.height, self.image_processing_tester.width)							)











import inspect
import unittest

from transformers import MobileNetVaConfig
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_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 MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor







class     __lowerCAmelCase	(   UpperCamelCase__):







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : Optional[int]  =self.config_class(**self.inputs_dict							)
        self.parent.assertTrue(hasattr(lowerCAmelCase__		,				"tf_padding"							)							)
        self.parent.assertTrue(hasattr(lowerCAmelCase__		,				"depth_multiplier"							)							)







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=1_3		,				lowerCAmelCase__=3		,				lowerCAmelCase__=3_2		,				lowerCAmelCase__=0.25		,				lowerCAmelCase__=8		,				lowerCAmelCase__=True		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=3_2		,				lowerCAmelCase__="relu6"		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				lowerCAmelCase__=1_0		,				lowerCAmelCase__=None		,				)       ->						Dict:



        '''simple docstring'''




        a__     : int  =parent
        a__     : Optional[Any]  =batch_size
        a__     : Tuple  =num_channels
        a__     : Dict  =image_size
        a__     : Union[str, Any]  =depth_multiplier
        a__     : List[str]  =min_depth
        a__     : Dict  =tf_padding
        a__     : Any  =int(last_hidden_size * depth_multiplier							)
        a__     : Tuple  =output_stride
        a__     : Optional[Any]  =hidden_act
        a__     : str  =classifier_dropout_prob
        a__     : int  =use_labels
        a__     : List[Any]  =is_training
        a__     : List[str]  =num_labels
        a__     : Dict  =initializer_range
        a__     : Tuple  =scope







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



        '''simple docstring'''




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

        a__     : Any  =None
        a__     : List[Any]  =None
        if self.use_labels:
          a__     : Dict  =ids_tensor([self.batch_size]		,				self.num_labels							)
          a__     : Tuple  =ids_tensor([self.batch_size, self.image_size, self.image_size]		,				self.num_labels							)

        a__     : Optional[Any]  =self.get_config()

        return config, pixel_values, labels, pixel_labels







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        return MobileNetVaConfig(
            num_channels=self.num_channels		,				image_size=self.image_size		,				depth_multiplier=self.depth_multiplier		,				min_depth=self.min_depth		,				tf_padding=self.tf_padding		,				hidden_act=self.hidden_act		,				classifier_dropout_prob=self.classifier_dropout_prob		,				initializer_range=self.initializer_range		,				)







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



        '''simple docstring'''




        a__     : Union[str, Any]  =MobileNetVaModel(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : str  =model(lowerCAmelCase__							)
        self.parent.assertEqual(
            result.last_hidden_state.shape		,				(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )		,				)







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



        '''simple docstring'''




        a__     : Any  =self.num_labels
        a__     : Dict  =MobileNetVaForImageClassification(lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : Optional[int]  =model(lowerCAmelCase__		,				labels=lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : str  =self.prepare_config_and_inputs()
        a__ ,							a__ ,							a__ ,							a__     : List[str]  =config_and_inputs
        a__     : Tuple  ={"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class     __lowerCAmelCase	(   UpperCamelCase__						,    UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : List[str]            =      (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
      _lowercase    : Optional[int]            =      (
          {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
          if is_torch_available()
          else {}
      )

      _lowercase    : Union[str, Any]            =      False
      _lowercase    : Optional[int]            =      False
      _lowercase    : List[str]            =      False
      _lowercase    : str            =      False







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



        '''simple docstring'''




        a__     : Optional[Any]  =MobileNetVaModelTester(self							)
        a__     : Optional[Any]  =MobileNetVaConfigTester(self		,				config_class=lowerCAmelCase__		,				has_text_modality=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        self.config_tester.run_common_tests()







      @unittest.skip(reason="MobileNetV1 does not use inputs_embeds"							)
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        pass







      @unittest.skip(reason="MobileNetV1 does not support input and output embeddings"							)
      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        pass







      @unittest.skip(reason="MobileNetV1 does not output attentions"							)
      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        pass







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




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

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

          a__     : Union[str, Any]  =["pixel_values"]
          self.assertListEqual(arg_names[:1]		,				lowerCAmelCase__							)







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



        '''simple docstring'''




        a__     : Optional[Any]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__							)







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



        '''simple docstring'''




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

          with torch.no_grad():
            a__     : List[Any]  =model(**self._prepare_for_class(lowerCAmelCase__		,				lowerCAmelCase__							)							)

          a__     : Optional[int]  =outputs.hidden_states

          a__     : Optional[int]  =2_6
          self.assertEqual(len(lowerCAmelCase__							)		,				lowerCAmelCase__							)

        a__ ,							a__     : Optional[Any]  =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__     : Optional[int]  =True
          check_hidden_states_output(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)

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

          check_hidden_states_output(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : Dict  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)







      @slow
      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__     : List[str]  =MobileNetVaModel.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
def 					_A	(		):





  """simple docstring"""



  a__     : Optional[Any]  =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
  return image







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







      @cached_property
      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        return (
            MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"							) if is_vision_available() else None
        )







      @slow
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : str  =MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"							).to(lowerCAmelCase__							)

        a__     : Optional[Any]  =self.default_image_processor
        a__     : Optional[int]  =prepare_img()
        a__     : Optional[int]  =image_processor(images=lowerCAmelCase__		,				return_tensors="pt"							).to(lowerCAmelCase__							)

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

        # verify the logits
        a__     : str  =torch.Size((1, 1_0_0_1)							)
        self.assertEqual(outputs.logits.shape		,				lowerCAmelCase__							)

        a__     : int  =torch.tensor([-4.17_39, -1.12_33, 3.12_05]							).to(lowerCAmelCase__							)

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






