




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase  :      Union[str, Any]						=      {
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :      int						=      [
      'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GraphormerForGraphClassification',
      'GraphormerModel',
      'GraphormerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_graphormer import (
        GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        GraphormerForGraphClassification,
        GraphormerModel,
        GraphormerPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase  :      Union[str, Any]						=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












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     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=3,__lowerCamelCase=32,__lowerCamelCase=3,__lowerCamelCase=10,__lowerCamelCase=[10, 20, 30, 40],__lowerCamelCase=[1, 1, 2, 1],__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=3,__lowerCamelCase=None,):
        A__											=						parent
        A__											=						batch_size
        A__											=						image_size
        A__											=						num_channels
        A__											=						embeddings_size
        A__											=						hidden_sizes
        A__											=						depths
        A__											=						is_training
        A__											=						use_labels
        A__											=						hidden_act
        A__											=						num_labels
        A__											=						scope
        A__											=						len(__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        A__											=						self.get_config()

        return config, pixel_values



 def 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						FlaxRegNetModel(config=__lowerCamelCase )
        A__											=						model(__lowerCamelCase )

        # 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,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.num_labels
        A__											=						FlaxRegNetForImageClassification(config=__lowerCamelCase )
        A__											=						model(__lowerCamelCase )
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )




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




@require_flax
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						FlaxRegNetModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        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 ):
        return



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )



 @unittest.skip(reason='''RegNet does not use inputs_embeds''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''RegNet does not support input and output embeddings''' )
 def 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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

               A__											=						['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        def check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
               A__											=						model_class(__lowerCamelCase )

               A__											=						model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase ) )

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

               A__											=						self.model_tester.num_stages
               self.assertEqual(len(__lowerCamelCase ),expected_num_stages + 1 )

        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               A__											=						True
               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )

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

               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )




 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                      A__											=						self._prepare_for_class(__lowerCamelCase,__lowerCamelCase )
                      A__											=						model_class(__lowerCamelCase )

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

                      with self.subTest('''JIT Enabled''' ):
                             A__											=						model_jitted(**__lowerCamelCase ).to_tuple()

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

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


def 	UpperCamelCase__(       )->Optional[int]:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




@require_flax
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @cached_property
 def 				UpperCamelCase			(							self ):
        return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None




 @slow
 def 				UpperCamelCase			(							self ):
        A__											=						FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )

        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(images=__lowerCamelCase,return_tensors='''np''' )

        A__											=						model(**__lowerCamelCase )

        # verify the logits
        A__											=						(1, 1000)
        self.assertEqual(outputs.logits.shape,__lowerCamelCase )

        A__											=						jnp.array([-0.4180, -1.5051, -3.4836] )

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





