


"""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 snake_case      (  unittest.TestCase):







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





      '''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  a_ (					self      :       List[Any]		)       ->						List[str]:





      '''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  a_ (					self      :       str		)       ->						Union[str, Any]:





      '''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  a_ (					self      :       List[Any]    ,   a__      :       Union[str, Any]    ,   a__      :       Optional[Any]		)       ->						Tuple:





      '''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  a_ (					self      :       List[Any]    ,   a__      :       List[str]    ,   a__      :       Optional[Any]		)       ->						Dict:





      '''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  a_ (					self      :       Any		)       ->						int:





      '''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 snake_case      (  _UpperCamelCase     ,				unittest.TestCase):
  __UpperCamelCase           =			(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

  __UpperCamelCase           =			False
  __UpperCamelCase           =			False
  __UpperCamelCase           =			False







  def  a_ (					self      :       Dict		)       ->						None:





      '''simple docstring'''







      _A					  =			FlaxRegNetModelTester(self		)
      _A					  =			ConfigTester(self    ,   config_class=a__    ,   has_text_modality=a__		)







  def  a_ (					self      :       str		)       ->						Any:





      '''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  a_ (					self      :       int		)       ->						Dict:





      '''simple docstring'''







      return







  def  a_ (					self      :       Optional[int]		)       ->						Union[str, Any]:





      '''simple docstring'''







      _A					  =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*a__		)







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





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





      '''simple docstring'''







      pass







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





      '''simple docstring'''







      pass







  def  a_ (					self      :       int		)       ->						int:





      '''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  a_ (					self      :       str		)       ->						Optional[Any]:





      '''simple docstring'''







      def check_hidden_states_output(a__      :       List[Any]    ,   a__      :       str    ,   a__      :       Union[str, 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  a_ (					self      :       Union[str, Any]		)       ->						List[str]:





      '''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(a__      :       Dict    ,   **a__      :       List[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      a__ (				)	->	Optional[int]:
    _A					  =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
    return image




@require_flax
class snake_case      (  unittest.TestCase):







  @cached_property
  def  a_ (					self      :       Optional[int]		)       ->						Optional[int]:





      '''simple docstring'''







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







  @slow
  def  a_ (					self      :       str		)       ->						List[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_1_8_0, -1.5_0_5_1, -3.4_8_3_6]		)

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



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







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