

'''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[str]				,				UpperCAmelCase__    : List[Any]=3				,				UpperCAmelCase__    : Tuple=3_2				,				UpperCAmelCase__    : Optional[int]=3				,				UpperCAmelCase__    : Optional[Any]=1_0				,				UpperCAmelCase__    : Optional[Any]=[1_0, 2_0, 3_0, 4_0]				,				UpperCAmelCase__    : Optional[int]=[1, 1, 2, 1]				,				UpperCAmelCase__    : Union[str, Any]=True				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : List[Any]="relu"				,				UpperCAmelCase__    : List[Any]=3				,				UpperCAmelCase__    : Union[str, Any]=None				,				)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   parent
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   batch_size
        SCREAMING_SNAKE_CASE :      Any       =   image_size
        SCREAMING_SNAKE_CASE :      str       =   num_channels
        SCREAMING_SNAKE_CASE :      str       =   embeddings_size
        SCREAMING_SNAKE_CASE :      List[Any]       =   hidden_sizes
        SCREAMING_SNAKE_CASE :      Dict       =   depths
        SCREAMING_SNAKE_CASE :      int       =   is_training
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   use_labels
        SCREAMING_SNAKE_CASE :      Dict       =   hidden_act
        SCREAMING_SNAKE_CASE :      List[Any]       =   num_labels
        SCREAMING_SNAKE_CASE :      int       =   scope
        SCREAMING_SNAKE_CASE :      Tuple       =   len(UpperCAmelCase__		)


  def  _lowercase      (			self    : Optional[Any]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.get_config()

        return config, pixel_values


  def  _lowercase      (			self    : str		)					->Optional[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  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Any		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   FlaxRegNetModel(config=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Dict       =   model(UpperCAmelCase__		)

        # 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  _lowercase      (			self    : List[Any]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Optional[Any]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   self.num_labels
        SCREAMING_SNAKE_CASE :      Optional[int]       =   FlaxRegNetForImageClassification(config=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   model(UpperCAmelCase__		)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)		)




  def  _lowercase      (			self    : List[str]		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Union[str, Any]       =   config_and_inputs
        SCREAMING_SNAKE_CASE :      str       =   {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_flax
class        a__       (   UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[int]			       =(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

  UpperCAmelCase__      : Optional[int]			       =False
  UpperCAmelCase__      : List[str]			       =False
  UpperCAmelCase__      : Dict			       =False


  def  _lowercase      (			self    : Union[str, Any]		)					->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   FlaxRegNetModelTester(self		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ConfigTester(self				,				config_class=UpperCAmelCase__				,				has_text_modality=UpperCAmelCase__		)


  def  _lowercase      (			self    : List[str]		)					->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  _lowercase      (			self    : Tuple		)					->Tuple:
        """simple docstring"""







        return


  def  _lowercase      (			self    : Optional[Any]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__		)


  def  _lowercase      (			self    : Any		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)


  @unittest.skip(reason="""RegNet does not use inputs_embeds"""		)
  def  _lowercase      (			self    : str		)					->Optional[Any]:
        """simple docstring"""







        pass


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







        pass


  def  _lowercase      (			self    : Any		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      int       =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   model_class(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Dict       =   inspect.signature(model.__call__		)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE :      List[str]       =   [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]				,				UpperCAmelCase__		)


  def  _lowercase      (			self    : Tuple		)					->int:
        """simple docstring"""







        def check_hidden_states_output(UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : int		):
              SCREAMING_SNAKE_CASE :      Any       =   model_class(UpperCAmelCase__		)

              SCREAMING_SNAKE_CASE :      Optional[int]       =   model(**self._prepare_for_class(UpperCAmelCase__				,				UpperCAmelCase__		)		)

              SCREAMING_SNAKE_CASE :      Optional[Any]       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              SCREAMING_SNAKE_CASE :      Dict       =   self.model_tester.num_stages
              self.assertEqual(len(UpperCAmelCase__		)				,				expected_num_stages + 1		)

        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Optional[int]       =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   True
              check_hidden_states_output(UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__		)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE :      List[Any]       =   True

              check_hidden_states_output(UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__		)




  def  _lowercase      (			self    : Union[str, Any]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[str]       =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__		):
                    SCREAMING_SNAKE_CASE :      List[Any]       =   self._prepare_for_class(UpperCAmelCase__				,				UpperCAmelCase__		)
                    SCREAMING_SNAKE_CASE :      List[str]       =   model_class(UpperCAmelCase__		)

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

                    with self.subTest("""JIT Enabled"""		):
                          SCREAMING_SNAKE_CASE :      int       =   model_jitted(**UpperCAmelCase__		).to_tuple()

                    with self.subTest("""JIT Disabled"""		):
                          with jax.disable_jit():
                                SCREAMING_SNAKE_CASE :      Any       =   model_jitted(**UpperCAmelCase__		).to_tuple()

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






def 					__lowercase      (     )     ->						Union[str, Any]:
      SCREAMING_SNAKE_CASE :      int       =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
      return image







@require_flax
class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  @cached_property
  def  _lowercase      (			self    : Dict		)					->int:
        """simple docstring"""







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




  @slow
  def  _lowercase      (			self    : Union[str, Any]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""		)

        SCREAMING_SNAKE_CASE :      str       =   self.default_image_processor
        SCREAMING_SNAKE_CASE :      int       =   prepare_img()
        SCREAMING_SNAKE_CASE :      Optional[int]       =   image_processor(images=UpperCAmelCase__				,				return_tensors="""np"""		)

        SCREAMING_SNAKE_CASE :      str       =   model(**UpperCAmelCase__		)

        # verify the logits
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (1, 1_0_0_0)
        self.assertEqual(outputs.logits.shape				,				UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      List[Any]       =   jnp.array([-0.41_80, -1.50_51, -3.48_36]		)

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







from __future__ import annotations






def 					__lowercase      (     _A						,    _A			)     ->						list[str]:
      if nth_term == "":
            return [""]
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      list[str]       =   []
      for temp in range(int(_A			)			):
            series.append(F"1 / {pow(temp + 1						,    int(_A			)			)}" if series else """1"""			)
      return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase__ :     Dict				    =    int(input("""Enter the last number (nth term) of the P-Series"""))
  UpperCAmelCase__ :     Optional[int]				    =    int(input("""Enter the power for  P-Series"""))
  print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
  print(p_series(nth_term, power))




