
"""simple docstring"""

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     lowercase__						(					lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					List[str]="shi-labs/oneformer_demo" ) ->      Union[str, Any]:






    """simple docstring"""




    with open(hf_hub_download(lowerCAmelCase ,				lowerCAmelCase ,				repo_type='dataset' ) ,				'r' ) as f:
        UpperCAmelCase									=					json.load(lowerCAmelCase )
    UpperCAmelCase									=					{}
    UpperCAmelCase									=					[]
    UpperCAmelCase									=					[]
    for key, info in class_info.items():
        UpperCAmelCase									=					info['name']
        class_names.append(info['name'] )
        if info["isthing"]:
            thing_ids.append(int(lowerCAmelCase ) )
    UpperCAmelCase									=					thing_ids
    UpperCAmelCase									=					class_names
    return metadata
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=7   ,       lowercase_=3   ,       lowercase_=3_0   ,       lowercase_=4_0_0   ,       lowercase_=None   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=[0.5, 0.5, 0.5]   ,       lowercase_=[0.5, 0.5, 0.5]   ,       lowercase_=1_0   ,       lowercase_=False   ,       lowercase_=2_5_5   ,       lowercase_="shi-labs/oneformer_demo"   ,       lowercase_="ade20k_panoptic.json"   ,       lowercase_=1_0   ,       )						->					Dict:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					num_channels
          UpperCAmelCase									=					min_resolution
          UpperCAmelCase									=					max_resolution
          UpperCAmelCase									=					do_resize
          UpperCAmelCase									=					{'shortest_edge': 3_2, 'longest_edge': 1_3_3_3} if size is None else size
          UpperCAmelCase									=					do_normalize
          UpperCAmelCase									=					image_mean
          UpperCAmelCase									=					image_std
          UpperCAmelCase									=					class_info_file
          UpperCAmelCase									=					prepare_metadata(lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					num_text
          UpperCAmelCase									=					repo_path

          # for the post_process_functions
          UpperCAmelCase									=					2
          UpperCAmelCase									=					1_0
          UpperCAmelCase									=					1_0
          UpperCAmelCase									=					3
          UpperCAmelCase									=					4
          UpperCAmelCase									=					num_labels
          UpperCAmelCase									=					do_reduce_labels
          UpperCAmelCase									=					ignore_index




      def     a_      (							self							)						->					Any:
          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     a_      (							self   ,       lowercase_   ,       lowercase_=False							)						->					Dict:
          if not batched:
              UpperCAmelCase									=					image_inputs[0]
              if isinstance(lowercase_   ,       Image.Image							):
                  UpperCAmelCase			,							UpperCAmelCase									=					image.size
              else:
                  UpperCAmelCase			,							UpperCAmelCase									=					image.shape[1], image.shape[2]
              if w < h:
                  UpperCAmelCase									=					int(self.size['shortest_edge'] * h / w							)
                  UpperCAmelCase									=					self.size['shortest_edge']
              elif w > h:
                  UpperCAmelCase									=					self.size['shortest_edge']
                  UpperCAmelCase									=					int(self.size['shortest_edge'] * w / h							)
              else:
                  UpperCAmelCase									=					self.size['shortest_edge']
                  UpperCAmelCase									=					self.size['shortest_edge']

          else:
              UpperCAmelCase									=					[]
              for image in image_inputs:
                  UpperCAmelCase			,							UpperCAmelCase									=					self.get_expected_values([image]							)
                  expected_values.append((expected_height, expected_width)							)
              UpperCAmelCase									=					max(lowercase_   ,       key=lambda lowercase_							: item[0]							)[0]
              UpperCAmelCase									=					max(lowercase_   ,       key=lambda lowercase_							: item[1]							)[1]

          return expected_height, expected_width




      def     a_      (							self							)						->					List[Any]:
          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      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Any							      =	OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	image_processing_class




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					OneFormerImageProcessorTester(self							)




      @property
      def     a_      (							self							)						->					Tuple:
          return self.image_processing_tester.prepare_image_processor_dict()




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(lowercase_   ,       'image_mean'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'image_std'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_normalize'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_resize'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'size'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'ignore_index'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'class_info_file'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'num_text'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'repo_path'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'metadata'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'do_reduce_labels'							)							)




      def     a_      (							self							)						->					Dict:
          pass




      def     a_      (							self							)						->					List[Any]:
          # Initialize image_processor
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          UpperCAmelCase									=					prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       Image.Image							)

          # Test not batched input
          UpperCAmelCase									=					image_processor(image_inputs[0]   ,       ['semantic']   ,       return_tensors='pt'							).pixel_values

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_							)

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

          # Test batched
          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

          UpperCAmelCase									=					image_processor(
              lowercase_   ,       ['semantic'] * len(lowercase_							)   ,       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     a_      (							self							)						->					Dict:
          # Initialize image_processor
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          UpperCAmelCase									=					prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=lowercase_   ,       numpify=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       np.ndarray							)

          # Test not batched input
          UpperCAmelCase									=					image_processor(image_inputs[0]   ,       ['semantic']   ,       return_tensors='pt'							).pixel_values

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_							)

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

          # Test batched
          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

          UpperCAmelCase									=					image_processor(
              lowercase_   ,       ['semantic'] * len(lowercase_							)   ,       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     a_      (							self							)						->					Union[str, Any]:
          # Initialize image_processor
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          UpperCAmelCase									=					prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=lowercase_   ,       torchify=lowercase_							)
          for image in image_inputs:
              self.assertIsInstance(lowercase_   ,       torch.Tensor							)

          # Test not batched input
          UpperCAmelCase									=					image_processor(image_inputs[0]   ,       ['semantic']   ,       return_tensors='pt'							).pixel_values

          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_							)

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

          # Test batched
          UpperCAmelCase			,							UpperCAmelCase									=					self.image_processing_tester.get_expected_values(lowercase_   ,       batched=lowercase_							)

          UpperCAmelCase									=					image_processor(
              lowercase_   ,       ['semantic'] * len(lowercase_							)   ,       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     a_      (							self   ,       lowercase_=False   ,       lowercase_=False   ,       lowercase_="np"							)						->					int:
          UpperCAmelCase									=					self.image_processing_class(**self.image_processor_dict							)
          # prepare image and target
          UpperCAmelCase									=					self.image_processing_tester.num_labels
          UpperCAmelCase									=					None
          UpperCAmelCase									=					None
          UpperCAmelCase									=					prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=lowercase_							)
          if with_segmentation_maps:
              UpperCAmelCase									=					num_labels
              if is_instance_map:
                  UpperCAmelCase									=					list(range(lowercase_							)							) * 2
                  UpperCAmelCase									=					dict(enumerate(lowercase_							)							)
              UpperCAmelCase									=					[
                  np.random.randint(0   ,       high * 2   ,       (img.size[1], img.size[0])							).astype(np.uinta							) for img in image_inputs
              ]
              if segmentation_type == "pil":
                  UpperCAmelCase									=					[Image.fromarray(lowercase_							) for annotation in annotations]

          UpperCAmelCase									=					image_processor(
              lowercase_   ,       ['semantic'] * len(lowercase_							)   ,       lowercase_   ,       return_tensors='pt'   ,       instance_id_to_semantic_id=lowercase_   ,       pad_and_return_pixel_mask=lowercase_   ,       )

          return inputs




      def     a_      (							self							)						->					List[Any]:
          pass




      def     a_      (							self							)						->					int:
          def common(lowercase_=False   ,       lowercase_=None							):
              UpperCAmelCase									=					self.comm_get_image_processor_inputs(
                  with_segmentation_maps=lowercase_   ,       is_instance_map=lowercase_   ,       segmentation_type=lowercase_							)

              UpperCAmelCase									=					inputs['mask_labels']
              UpperCAmelCase									=					inputs['class_labels']
              UpperCAmelCase									=					inputs['pixel_values']
              UpperCAmelCase									=					inputs['text_inputs']

              # check the batch_size
              for mask_label, class_label, text_input in zip(lowercase_   ,       lowercase_   ,       lowercase_							):
                  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(lowercase_							)   ,       self.image_processing_tester.num_text							)

          common()
          common(is_instance_map=lowercase_							)
          common(is_instance_map=lowercase_   ,       segmentation_type='pil'							)
          common(is_instance_map=lowercase_   ,       segmentation_type='pil'							)




      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					np.zeros((2_0, 5_0)							)
          UpperCAmelCase									=					1
          UpperCAmelCase									=					1
          UpperCAmelCase									=					1

          UpperCAmelCase									=					binary_mask_to_rle(lowercase_							)
          self.assertEqual(len(lowercase_							)   ,       4							)
          self.assertEqual(rle[0]   ,       2_1							)
          self.assertEqual(rle[1]   ,       4_5							)




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					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'   ,       )
          UpperCAmelCase									=					self.image_processing_tester.get_fake_oneformer_outputs()

          UpperCAmelCase									=					fature_extractor.post_process_semantic_segmentation(lowercase_							)

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

          UpperCAmelCase									=					[(1, 4) for i in range(self.image_processing_tester.batch_size							)]
          UpperCAmelCase									=					fature_extractor.post_process_semantic_segmentation(lowercase_   ,       target_sizes=lowercase_							)

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




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					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'   ,       )
          UpperCAmelCase									=					self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCAmelCase									=					image_processor.post_process_instance_segmentation(lowercase_   ,       threshold=0							)

          self.assertTrue(len(lowercase_							) == 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']							)   ,       lowercase_							)
              self.assertEqual(
                  el['segmentation'].shape   ,       (self.image_processing_tester.height, self.image_processing_tester.width)							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					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'   ,       )
          UpperCAmelCase									=					self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCAmelCase									=					image_processor.post_process_panoptic_segmentation(lowercase_   ,       threshold=0							)

          self.assertTrue(len(lowercase_							) == 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']							)   ,       lowercase_							)
              self.assertEqual(
                  el['segmentation'].shape   ,       (self.image_processing_tester.height, self.image_processing_tester.width)							)





"""simple docstring"""

import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=3   ,       lowercase_=3_2   ,       lowercase_=3   ,       lowercase_=1_0   ,       lowercase_=[1_0, 2_0, 3_0, 4_0]   ,       lowercase_=[1, 1, 2, 1]   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_="relu"   ,       lowercase_=3   ,       lowercase_=None   ,       )						->					str:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					image_size
          UpperCAmelCase									=					num_channels
          UpperCAmelCase									=					embeddings_size
          UpperCAmelCase									=					hidden_sizes
          UpperCAmelCase									=					depths
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					num_labels
          UpperCAmelCase									=					scope
          UpperCAmelCase									=					len(lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          UpperCAmelCase									=					self.get_config()

          return config, pixel_values




      def     a_      (							self							)						->					Optional[Any]:
          return RegNetConfig(
              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   ,       image_size=self.image_size   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					List[Any]:
          UpperCAmelCase									=					FlaxRegNetModel(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)

          # Output shape (b, c, h, w)
          self.parent.assertEqual(
              result.last_hidden_state.shape   ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,       )




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.num_labels
          UpperCAmelCase									=					FlaxRegNetForImageClassification(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)
          self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
          UpperCAmelCase									=					{'pixel_values': pixel_values}
          return config, inputs_dict

@require_flax
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	False
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	False
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	False




      def     a_      (							self							)						->					None:
          UpperCAmelCase									=					FlaxRegNetModelTester(self							)
          UpperCAmelCase									=					ConfigTester(self   ,       config_class=lowercase_   ,       has_text_modality=lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          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     a_      (							self							)						->					Optional[Any]:
          return




      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_							)




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_							)




      @unittest.skip(reason='RegNet does not use inputs_embeds'							)
      def     a_      (							self							)						->					List[str]:
          pass




      @unittest.skip(reason='RegNet does not support input and output embeddings'							)
      def     a_      (							self							)						->					Optional[int]:
          pass




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase									=					model_class(lowercase_							)
              UpperCAmelCase									=					inspect.signature(model.__call__							)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase									=					[*signature.parameters.keys()]

              UpperCAmelCase									=					['pixel_values']
              self.assertListEqual(arg_names[:1]   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          def check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					model_class(lowercase_							)

              UpperCAmelCase									=					model(**self._prepare_for_class(lowercase_   ,       lowercase_							)							)

              UpperCAmelCase									=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              UpperCAmelCase									=					self.model_tester.num_stages
              self.assertEqual(len(lowercase_							)   ,       expected_num_stages + 1							)

          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

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

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase									=					True

              check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__							):
                  UpperCAmelCase									=					self._prepare_for_class(lowercase_   ,       lowercase_							)
                  UpperCAmelCase									=					model_class(lowercase_							)

                  @jax.jit
                  def model_jitted(lowercase_   ,       **lowercase_							):
                      return model(pixel_values=lowercase_   ,       **lowercase_							)

                  with self.subTest('JIT Enabled'							):
                      UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

                  self.assertEqual(len(lowercase_							)   ,       len(lowercase_							)							)
                  for jitted_output, output in zip(lowercase_   ,       lowercase_							):
                      self.assertEqual(jitted_output.shape   ,       output.shape							)




def     lowercase__						(					) ->      Tuple:






    """simple docstring"""




    UpperCAmelCase									=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image







@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




      @cached_property
      def     a_      (							self							)						->					List[str]:
          return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'							) if is_vision_available() else None




      @slow
      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'							)

          UpperCAmelCase									=					self.default_image_processor
          UpperCAmelCase									=					prepare_img()
          UpperCAmelCase									=					image_processor(images=lowercase_   ,       return_tensors='np'							)

          UpperCAmelCase									=					model(**lowercase_							)

          # verify the logits
          UpperCAmelCase									=					(1, 1_0_0_0)
          self.assertEqual(outputs.logits.shape   ,       lowercase_							)

          UpperCAmelCase									=					jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]							)

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



