


'''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 							__UpperCamelCase    (		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						,    ):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	Any    =  self.get_config()

						return config, pixel_values




	def 				__UpperCAmelCase							(       self							):

						'''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							):

						'''simple docstring'''





						__a      :	Dict    =  FlaxRegNetModel(config=__a							)
						__a      :	List[str]    =  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							):

						'''simple docstring'''





						__a      :	Dict    =  self.num_labels
						__a      :	Union[str, Any]    =  FlaxRegNetForImageClassification(config=__a							)
						__a      :	Any    =  model(__a							)
						self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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





@require_flax
class 							__UpperCamelCase    (		lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

	A_           =   False
	A_           =   False
	A_           =   False




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  FlaxRegNetModelTester(self							)
						__a      :	str    =  ConfigTester(self						,    config_class=__a						,    has_text_modality=__a							)




	def 				__UpperCAmelCase							(       self							):

						'''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							):

						'''simple docstring'''





						return




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  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							):

						'''simple docstring'''





						pass




	@unittest.skip(reason='RegNet does not support input and output embeddings'							)
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a   ,    __a      :	List[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()

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

											__a      :	int    =  ['pixel_values']
											self.assertListEqual(arg_names[:1]						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						def check_hidden_states_output(__a						,    __a						,    __a							):
											__a      :	List[Any]    =  model_class(__a							)

											__a      :	str    =  model(**self._prepare_for_class(__a						,    __a							)							)

											__a      :	List[str]    =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											__a      :	Union[str, Any]    =  self.model_tester.num_stages
											self.assertEqual(len(__a							)						,    expected_num_stages + 1							)

						__a   ,    __a      :	Optional[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__a      :	Union[str, Any]    =  True
											check_hidden_states_output(__a						,    __a						,    __a							)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											__a      :	Dict    =  True

											check_hidden_states_output(__a						,    __a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a   ,    __a      :	Tuple    =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

																with self.subTest('JIT Enabled'							):
																					__a      :	Optional[Any]    =  model_jitted(**__a							).to_tuple()

																with self.subTest('JIT Disabled'							):
																					with jax.disable_jit():
																										__a      :	List[str]    =  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   lowerCamelCase							():
					__a      :	Union[str, Any]    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
					return image





@require_flax
class 							__UpperCamelCase    (		unittest.TestCase ):




	@cached_property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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





	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'							)

						__a      :	List[Any]    =  self.default_image_processor
						__a      :	Tuple    =  prepare_img()
						__a      :	Dict    =  image_processor(images=__a						,    return_tensors='np'							)

						__a      :	Tuple    =  model(**__a							)

						# verify the logits
						__a      :	Dict    =  (1, 1000)
						self.assertEqual(outputs.logits.shape						,    __a							)

						__a      :	int    =  jnp.array([-0.4180, -1.5051, -3.4836]							)

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







'''simple docstring'''



import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
	# Create universe of discourse in Python using linspace ()
	__lowercase     :						int	=     np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

	# Create two fuzzy sets by defining any membership function
	# (trapmf(), gbellmf(), gaussmf(), etc).
	__lowercase     :						Any	=     [0, 25, 50]
	__lowercase     :						int	=     [25, 50, 75]
	__lowercase     :						List[str]	=     fuzz.membership.trimf(X, abca)
	__lowercase     :						Any	=     fuzz.membership.trimf(X, abca)

	# Compute the different operations using inbuilt functions.
	__lowercase     :						List[Any]	=     np.ones(75)
	__lowercase     :						Any	=     np.zeros((75,))
	# 1. Union = max(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_or(X, young, X, middle_aged)[1]
	# 2. Intersection = min(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_and(X, young, X, middle_aged)[1]
	# 3. Complement (A) = (1- min(µA(x))
	__lowercase     :						str	=     fuzz.fuzzy_not(young)
	# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
	__lowercase     :						List[Any]	=     fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
	# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
	__lowercase     :						Optional[Any]	=     young + middle_aged - (young * middle_aged)
	# 6. Algebraic Product = (µA(x) * µB(x))
	__lowercase     :						str	=     young * middle_aged
	# 7. Bounded Sum = min[1,(µA(x), µB(x))]
	__lowercase     :						Optional[Any]	=     fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
	# 8. Bounded difference = min[0,(µA(x), µB(x))]
	__lowercase     :						Union[str, Any]	=     fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

	# max-min composition
	# max-product composition

	# Plot each set A, set B and each operation result using plot() and subplot().
	from matplotlib import pyplot as plt

	plt.figure()

	plt.subplot(4, 3, 1)
	plt.plot(X, young)
	plt.title('Young')
	plt.grid(True)

	plt.subplot(4, 3, 2)
	plt.plot(X, middle_aged)
	plt.title('Middle aged')
	plt.grid(True)

	plt.subplot(4, 3, 3)
	plt.plot(X, union)
	plt.title('union')
	plt.grid(True)

	plt.subplot(4, 3, 4)
	plt.plot(X, intersection)
	plt.title('intersection')
	plt.grid(True)

	plt.subplot(4, 3, 5)
	plt.plot(X, complement_a)
	plt.title('complement_a')
	plt.grid(True)

	plt.subplot(4, 3, 6)
	plt.plot(X, difference)
	plt.title('difference a/b')
	plt.grid(True)

	plt.subplot(4, 3, 7)
	plt.plot(X, alg_sum)
	plt.title('alg_sum')
	plt.grid(True)

	plt.subplot(4, 3, 8)
	plt.plot(X, alg_product)
	plt.title('alg_product')
	plt.grid(True)

	plt.subplot(4, 3, 9)
	plt.plot(X, bdd_sum)
	plt.title('bdd_sum')
	plt.grid(True)

	plt.subplot(4, 3, 10)
	plt.plot(X, bdd_difference)
	plt.title('bdd_difference')
	plt.grid(True)

	plt.subplots_adjust(hspace=0.5)
	plt.show()



