

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     :						Dict     ,    UpperCAmelCase     :						List[Any]     ,    UpperCAmelCase     :						Tuple=3     ,    UpperCAmelCase     :						Any=3_2     ,    UpperCAmelCase     :						List[Any]=3     ,    UpperCAmelCase     :						Union[str, Any]=1_0     ,    UpperCAmelCase     :						Optional[Any]=[1_0, 2_0, 3_0, 4_0]     ,    UpperCAmelCase     :						List[str]=[1, 1, 2, 1]     ,    UpperCAmelCase     :						Tuple=True     ,    UpperCAmelCase     :						Dict=True     ,    UpperCAmelCase     :						Union[str, Any]="relu"     ,    UpperCAmelCase     :						Optional[int]=3     ,    UpperCAmelCase     :						int=None     ,    )				->      Dict:
      __lowerCAmelCase:  str        = parent
      __lowerCAmelCase:  Optional[int]        = batch_size
      __lowerCAmelCase:  Any        = image_size
      __lowerCAmelCase:  int        = num_channels
      __lowerCAmelCase:  Optional[int]        = embeddings_size
      __lowerCAmelCase:  Dict        = hidden_sizes
      __lowerCAmelCase:  int        = depths
      __lowerCAmelCase:  Union[str, Any]        = is_training
      __lowerCAmelCase:  Tuple        = use_labels
      __lowerCAmelCase:  int        = hidden_act
      __lowerCAmelCase:  Optional[int]        = num_labels
      __lowerCAmelCase:  Optional[int]        = scope
      __lowerCAmelCase:  Optional[Any]        = len(UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						List[Any]   )				->      List[str]:
      __lowerCAmelCase:  Optional[int]        = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      __lowerCAmelCase:  int        = self.get_config()

      return config, pixel_values


     def     UpperCAmelCase    (				self     :						Tuple   )				->      List[str]:
      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     :						int     ,    UpperCAmelCase     :						List[Any]     ,    UpperCAmelCase     :						Any   )				->      Tuple:
      __lowerCAmelCase:  Any        = FlaxResNetModel(config=UpperCAmelCase   )
      __lowerCAmelCase:  int        = 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     UpperCAmelCase    (				self     :						List[Any]     ,    UpperCAmelCase     :						Union[str, Any]     ,    UpperCAmelCase     :						List[Any]   )				->      Optional[Any]:
      __lowerCAmelCase:  Any        = self.num_labels
      __lowerCAmelCase:  Optional[int]        = FlaxResNetForImageClassification(config=UpperCAmelCase   )
      __lowerCAmelCase:  int        = model(UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.num_labels)   )


     def     UpperCAmelCase    (				self     :						List[Any]   )				->      Dict:
      __lowerCAmelCase:  List[Any]        = self.prepare_config_and_inputs()
      __lowerCAmelCase      ,				__lowerCAmelCase:  int        = config_and_inputs
      __lowerCAmelCase:  List[Any]        = {'pixel_values': pixel_values}
      return config, inputs_dict







@require_flax
class 				A_					(						snake_case__				,      unittest.TestCase   ):
     _lowercase     :	str        =      (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

     _lowercase     :	int        =      False
     _lowercase     :	str        =      False
     _lowercase     :	int        =      False


     def     UpperCAmelCase    (				self     :						int   )				->      List[str]:
      __lowerCAmelCase:  List[str]        = FlaxResNetModelTester(self   )
      __lowerCAmelCase:  Optional[Any]        = ConfigTester(self     ,    config_class=UpperCAmelCase     ,    has_text_modality=UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Any   )				->      str:
      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     :						str   )				->      str:
      return


     def     UpperCAmelCase    (				self     :						str   )				->      Any:
      __lowerCAmelCase:  List[str]        = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						List[Any]   )				->      Tuple:
      __lowerCAmelCase:  Tuple        = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase   )


     @unittest.skip(reason='ResNet does not use inputs_embeds'   )
     def     UpperCAmelCase    (				self     :						Optional[Any]   )				->      str:
      pass


     @unittest.skip(reason='ResNet does not support input and output embeddings'   )
     def     UpperCAmelCase    (				self     :						int   )				->      List[str]:
      pass


     def     UpperCAmelCase    (				self     :						Any   )				->      Tuple:
      __lowerCAmelCase      ,				__lowerCAmelCase:  str        = self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowerCAmelCase:  Dict        = ['pixel_values']
       self.assertListEqual(arg_names[:1]     ,    UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Dict   )				->      Optional[Any]:







      def check_hidden_states_output(UpperCAmelCase     :						str     ,    UpperCAmelCase     :						Optional[int]     ,    UpperCAmelCase     :						Optional[Any]   ):
       __lowerCAmelCase:  Optional[Any]        = model_class(UpperCAmelCase   )

       __lowerCAmelCase:  Union[str, Any]        = model(**self._prepare_for_class(UpperCAmelCase     ,    UpperCAmelCase   )   )

       __lowerCAmelCase:  Dict        = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       __lowerCAmelCase:  Dict        = self.model_tester.num_stages
       self.assertEqual(len(UpperCAmelCase   )     ,    expected_num_stages + 1   )


     @unittest.skip(reason='ResNet does not use feedforward chunking'   )
     def     UpperCAmelCase    (				self     :						Optional[Any]   )				->      Dict:
      pass


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Optional[Any]:
      __lowerCAmelCase      ,				__lowerCAmelCase:  Optional[int]        = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__   ):
        __lowerCAmelCase:  Dict        = self._prepare_for_class(UpperCAmelCase     ,    UpperCAmelCase   )
        __lowerCAmelCase:  str        = model_class(UpperCAmelCase   )

        @jax.jit
        def model_jitted(UpperCAmelCase     :						Union[str, Any]     ,    **UpperCAmelCase     :						Optional[int]   ):
         return model(pixel_values=UpperCAmelCase     ,    **UpperCAmelCase   )

        with self.subTest('JIT Enabled'   ):
         __lowerCAmelCase:  List[str]        = model_jitted(**UpperCAmelCase   ).to_tuple()

        with self.subTest('JIT Disabled'   ):
         with jax.disable_jit():
          __lowerCAmelCase:  List[str]        = 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 _a					(			)       ->       Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:  List[Any]        = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
 return image






@require_flax
class 				A_					(						unittest.TestCase   ):


     @cached_property
     def     UpperCAmelCase    (				self     :						Optional[int]   )				->      Union[str, Any]:
      return AutoImageProcessor.from_pretrained('microsoft/resnet-50'   ) if is_vision_available() else None


     @slow
     def     UpperCAmelCase    (				self     :						Dict   )				->      Union[str, Any]:
      __lowerCAmelCase:  Optional[int]        = FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'   )

      __lowerCAmelCase:  int        = self.default_image_processor
      __lowerCAmelCase:  int        = prepare_img()
      __lowerCAmelCase:  Optional[int]        = image_processor(images=UpperCAmelCase     ,    return_tensors='np'   )

      __lowerCAmelCase:  Union[str, Any]        = model(**UpperCAmelCase   )

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

      __lowerCAmelCase:  Union[str, Any]        = jnp.array([-11.1069, -9.7877, -8.3777]   )

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













from __future__ import annotations


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						)->    list[float | int]:





						"""simple docstring"""



						if not all((nth_term, start_term_a, common_ratio_r)      ):
												return []
						_UpperCAmelCase							   =     []
						_UpperCAmelCase							   =     1
						_UpperCAmelCase							   =     common_ratio_r
						for _ in range(int(__lowerCAmelCase      )      ):
												if not series:
																		series.append(__lowerCAmelCase      )
												else:
																		power += 1
																		series.append(float(start_term_a * multiple      )      )
																		_UpperCAmelCase							   =     pow(float(__lowerCAmelCase      )					,						__lowerCAmelCase      )
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_a       =    float(input('''Enter the last number (n term) of the Geometric Series'''))
		_a       =    float(input('''Enter the starting term (a) of the Geometric Series'''))
		_a       =    float(
		    input('''Enter the common ratio between two terms (r) of the Geometric Series''')
		)
		print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))
