
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        lowercase      (	unittest.TestCase     ):



		def __init__(				self       ,   A_       ,   A_=3       ,   A_=32       ,   A_=3       ,   A_=10       ,   A_=[10, 20, 30, 40]       ,   A_=[1, 1, 2, 1]       ,   A_=True       ,   A_=True       ,   A_="relu"       ,   A_=3       ,   A_=None       ,   )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  image_size
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  embeddings_size
				UpperCamelCase						=  hidden_sizes
				UpperCamelCase						=  depths
				UpperCamelCase						=  is_training
				UpperCamelCase						=  use_labels
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  num_labels
				UpperCamelCase						=  scope
				UpperCamelCase						=  len(A_ )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

				UpperCamelCase						=  self.get_config()

				return config, pixel_values



		def __UpperCamelCase				(				self )						->      List[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 __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  FlaxRegNetModel(config=A_ )
				UpperCamelCase						=  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       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.num_labels
				UpperCamelCase						=  FlaxRegNetForImageClassification(config=A_ )
				UpperCamelCase						=  model(A_ )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels) )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				UpperCamelCase			,						UpperCamelCase						=  config_and_inputs
				UpperCamelCase						=  {'pixel_values': pixel_values}
				return config, inputs_dict







@require_flax
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : List[Any]			  =       (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

		__lowercase : int			  =       False
		__lowercase : Dict			  =       False
		__lowercase : Optional[Any]			  =       False



		def __UpperCamelCase				(				self )						->      None:
				"""simple docstring"""
				UpperCamelCase						=  FlaxRegNetModelTester(self )
				UpperCamelCase						=  ConfigTester(self       ,   config_class=A_       ,   has_text_modality=A_ )



		def __UpperCamelCase				(				self )						->      Optional[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 __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				return



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  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 )						->      Optional[int]:
				"""simple docstring"""
				pass



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



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase						=  ['pixel_values']
						self.assertListEqual(arg_names[:1]       ,   A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				def check_hidden_states_output(A_       ,   A_       ,   A_ ):
						UpperCamelCase						=  model_class(A_ )

						UpperCamelCase						=  model(**self._prepare_for_class(A_       ,   A_ ) )

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

						UpperCamelCase						=  self.model_tester.num_stages
						self.assertEqual(len(A_ )       ,   expected_num_stages + 1 )

				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase						=  True
						check_hidden_states_output(A_       ,   A_       ,   A_ )

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

						check_hidden_states_output(A_       ,   A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__ ):
								UpperCamelCase						=  self._prepare_for_class(A_       ,   A_ )
								UpperCamelCase						=  model_class(A_ )

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

								with self.subTest('JIT Enabled' ):
										UpperCamelCase						=  model_jitted(**A_ ).to_tuple()

								with self.subTest('JIT Disabled' ):
										with jax.disable_jit():
												UpperCamelCase						=  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 (      )     ->						List[str]:
		'''simple docstring'''

		UpperCamelCase						=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
		return image







@require_flax
class        lowercase      (	unittest.TestCase     ):



		@cached_property
		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				return AutoImageProcessor.from_pretrained('facebook/regnet-y-040' ) if is_vision_available() else None



		@slow
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040' )

				UpperCamelCase						=  self.default_image_processor
				UpperCamelCase						=  prepare_img()
				UpperCamelCase						=  image_processor(images=A_       ,   return_tensors='np' )

				UpperCamelCase						=  model(**A_ )

				# verify the logits
				UpperCamelCase						=  (1, 1_000)
				self.assertEqual(outputs.logits.shape       ,   A_ )

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

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


from collections.abc import Callable


def    A (      lowercase					, lowercase					, lowercase				)     ->						float:
		'''simple docstring'''

		UpperCamelCase						=  a
		UpperCamelCase						=  b
		if function(lowercase				) == 0:  # one of the a or b is a root for the function
				return a
		elif function(lowercase				) == 0:
				return b
		elif (
		    function(lowercase				) * function(lowercase				) > 0
		):  # if none of these are root and they are both positive or negative,
				# then this algorithm can't find the root
				raise ValueError('could not find root in given interval.'				)
		else:
				UpperCamelCase						=  start + (end - start) / 2.0
				while abs(start - mid				) > 10**-7:  # until precisely equals to 10^-7
						if function(lowercase				) == 0:
								return mid
						elif function(lowercase				) * function(lowercase				) < 0:
								UpperCamelCase						=  mid
						else:
								UpperCamelCase						=  mid
						UpperCamelCase						=  start + (end - start) / 2.0
				return mid


def    A (      lowercase				)     ->						float:
		'''simple docstring'''

		return x**3 - 2 * x - 5


if __name__ == "__main__":
					print(bisection(f, 1, 1_000))

					import doctest

					doctest.testmod()
