

"""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 					a						(       unittest.TestCase				):



    """simple docstring"""



    def __init__(     self: Dict    ,						UpperCamelCase: List[Any]    ,						UpperCamelCase: List[str]=3    ,						UpperCamelCase: Any=32    ,						UpperCamelCase: Dict=3    ,						UpperCamelCase: Optional[int]=10    ,						UpperCamelCase: Dict=[10, 20, 30, 40]    ,						UpperCamelCase: List[str]=[1, 1, 2, 1]    ,						UpperCamelCase: int=True    ,						UpperCamelCase: str=True    ,						UpperCamelCase: List[str]="relu"    ,						UpperCamelCase: Optional[int]=3    ,						UpperCamelCase: List[Any]=None    ,						):






         """simple docstring"""


         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(__A       )

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         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: Optional[int]       ):






         """simple docstring"""


         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: str    ,						UpperCamelCase: int    ,						UpperCamelCase: str       ):






         """simple docstring"""


         A__										=  FlaxRegNetModel(config=__A       )
         A__										=  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: Optional[Any]    ,						UpperCamelCase: Dict    ,						UpperCamelCase: Any       ):






         """simple docstring"""


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





    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


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



@require_flax
class 					a						(       UpperCamelCase__,	unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False

    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__										=  FlaxRegNetModelTester(self       )
         A__										=  ConfigTester(self    ,						config_class=__A    ,						has_text_modality=__A       )

    def 				UpperCamelCase	(     self: List[str]       ):






         """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: List[str]       ):






         """simple docstring"""


         return

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__A       )

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__A       )

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






         """simple docstring"""


         pass

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






         """simple docstring"""


         pass

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              A__										=  model_class(__A       )
              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]    ,						__A       )

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






         """simple docstring"""


         def check_hidden_states_output(UpperCamelCase: Optional[int]    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]       ):
              A__										=  model_class(__A       )

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

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

              A__										=  self.model_tester.num_stages
              self.assertEqual(len(__A       )    ,						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(__A    ,						__A    ,						__A       )

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

              check_hidden_states_output(__A    ,						__A    ,						__A       )





    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         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(__A    ,						__A       )
                   A__										=  model_class(__A       )

                   @jax.jit
                   def model_jitted(UpperCamelCase: Dict    ,						**UpperCamelCase: Any       ):
                        return model(pixel_values=__A    ,						**__A       )

                   with self.subTest("""JIT Enabled"""       ):
                        A__										=  model_jitted(**__A       ).to_tuple()

                   with self.subTest("""JIT Disabled"""       ):
                        with jax.disable_jit():
                             A__										=  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  _snake_case     (    ):
     A__										=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
     return image



@require_flax
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @cached_property
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""       ) if is_vision_available() else None





    @slow
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""       )

         A__										=  self.default_image_processor
         A__										=  prepare_img()
         A__										=  image_processor(images=__A    ,						return_tensors="""np"""       )

         A__										=  model(**__A       )

         # verify the logits
         A__										=  (1, 10_00)
         self.assertEqual(outputs.logits.shape    ,						__A       )

         A__										=  jnp.array([-0.4_180, -1.5_051, -3.4_836]       )

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









"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


SCREAMING_SNAKE_CASE_					:	List[Any]							  =						logging.get_logger(__name__)



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Tuple    ,						*UpperCamelCase: Optional[int]    ,						**UpperCamelCase: Tuple       ):






         """simple docstring"""


         warnings.warn(
             """The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
             """ Please use VideoMAEImageProcessor instead."""    ,						UpperCamelCase    ,						)
         super().__init__(*UpperCamelCase    ,						**UpperCamelCase       )






