





import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor

class 			__A(    unittest.TestCase			):

     def __init__(   self,							A,							A=3,							A=32,							A=3,							A=10,							A=[10, 20, 30, 40],							A=[1, 1, 2, 1],							A=True,							A=True,							A="relu",							A=3,							A=None,							):





            """simple docstring"""

            _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(A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

            _UpperCamelCase														=    self.get_config()

            return config, pixel_values

     def 						_UpperCamelCase  (   self   ):





            """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,							image_size=self.image_size,							)

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    FlaxResNetModel(config=A   )
            _UpperCamelCase														=    model(A   )

            # Output shape (b, c, h, w)
            self.parent.assertEqual(
                result.last_hidden_state.shape,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),							)

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.num_labels
            _UpperCamelCase														=    FlaxResNetForImageClassification(config=A   )
            _UpperCamelCase														=    model(A   )
            self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.num_labels)   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.prepare_config_and_inputs()
            _UpperCamelCase							,		_UpperCamelCase														=    config_and_inputs
            _UpperCamelCase														=    {'''pixel_values''': pixel_values}
            return config, inputs_dict






@require_flax
class 			__A(    __UpperCAmelCase    ,      unittest.TestCase			):
     __A							=							(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

     __A							=							False
     __A							=							False
     __A							=							False

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    FlaxResNetModelTester(self   )
            _UpperCamelCase														=    ConfigTester(self,							config_class=A,							has_text_modality=A   )

     def 						_UpperCamelCase  (   self   ):





            """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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*A   )

     @unittest.skip(reason='''ResNet does not use inputs_embeds'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     @unittest.skip(reason='''ResNet does not support input and output embeddings'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _UpperCamelCase														=    model_class(A   )
                   _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],							A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            def check_hidden_states_output(A,							A,							A   ):
                   _UpperCamelCase														=    model_class(A   )

                   _UpperCamelCase														=    model(**self._prepare_for_class(A,							A   )   )

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

                   _UpperCamelCase														=    self.model_tester.num_stages
                   self.assertEqual(len(A   ),							expected_num_stages + 1   )

     @unittest.skip(reason='''ResNet does not use feedforward chunking'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _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(A,							A   )
                          _UpperCamelCase														=    model_class(A   )

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

                          with self.subTest('''JIT Enabled'''   ):
                                 _UpperCamelCase														=    model_jitted(**A   ).to_tuple()

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

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






def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
       return image

@require_flax
class 			__A(    unittest.TestCase			):

     @cached_property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return AutoImageProcessor.from_pretrained('''microsoft/resnet-50'''   ) if is_vision_available() else None



     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50'''   )

            _UpperCamelCase														=    self.default_image_processor
            _UpperCamelCase														=    prepare_img()
            _UpperCamelCase														=    image_processor(images=A,							return_tensors='''np'''   )

            _UpperCamelCase														=    model(**A   )

            # verify the logits
            _UpperCamelCase														=    (1, 1000)
            self.assertEqual(outputs.logits.shape,							A   )

            _UpperCamelCase														=    jnp.array([-11.1_069, -9.7_877, -8.3_777]   )

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













def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    index
       _UpperCamelCase														=    2 * index + 1
       _UpperCamelCase														=    2 * index + 2
       if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
              _UpperCamelCase														=    left_index

       if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
              _UpperCamelCase														=    right_index

       if largest != index:
              _UpperCamelCase							,		_UpperCamelCase														=    unsorted[index], unsorted[largest]
              heapify(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       _UpperCamelCase														=    len(lowerCAmelCase       )
       for i in range(n // 2 - 1     ,							-1     ,							-1       ):
              heapify(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
       for i in range(n - 1     ,							0     ,							-1       ):
              _UpperCamelCase							,		_UpperCamelCase														=    unsorted[i], unsorted[0]
              heapify(lowerCAmelCase     ,							0     ,							lowerCAmelCase       )
       return unsorted


if __name__ == "__main__":
       lowercase     :			Union[str, Any]     						=	input("""Enter numbers separated by a comma:\n""").strip()
       lowercase     :			Dict     						=	[int(item) for item in user_input.split(""",""")]
       print(heap_sort(unsorted))






