
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  ):




	'''simple docstring'''





	def __init__(	self :			Optional[Any]							,A :			int							,A :			List[Any]=3							,A :			Any=32							,A :			List[str]=3							,A :			List[str]=10							,A :			List[str]=[10, 20, 30, 40]							,A :			List[Any]=[1, 1, 2, 1]							,A :			Dict=True							,A :			List[str]=True							,A :			Optional[int]="relu"							,A :			List[Any]=3							,A :			int=None							,):
					__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     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

					__A             =			self.get_config()

					return config, pixel_values






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					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 :			List[Any]							,A :			Dict							,A :			int			):
					__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     UpperCamelCase_  (	self :			Dict							,A :			Optional[Any]							,A :			int			):
					__A             =			self.num_labels
					__A             =			FlaxRegNetForImageClassification(config=A			)
					__A             =			model(A			)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)			)







	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			self.prepare_config_and_inputs()
					__A      ,							__A             =			config_and_inputs
					__A             =			{"pixel_values": pixel_values}
					return config, inputs_dict





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




	'''simple docstring'''





	snake_case_    =					(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

	snake_case_    =					False
	snake_case_    =					False
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			FlaxRegNetModelTester(self			)
					__A             =			ConfigTester(self							,config_class=A							,has_text_modality=A			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					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			):
					return






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A			)






	def     UpperCamelCase_  (	self :			Dict			):
					__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     UpperCamelCase_  (	self :			List[str]			):
					pass






	@unittest.skip(reason="RegNet does not support input and output embeddings"			)
	def     UpperCamelCase_  (	self :			Any			):
					pass






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__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     UpperCamelCase_  (	self :			str			):
					def check_hidden_states_output(A :			List[Any]							,A :			List[Any]							,A :			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     UpperCamelCase_  (	self :			Any			):
					__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 :			List[str]							,**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 						UpperCAmelCase      (     )		->     List[str]:







				"""simple docstring"""



				__A             =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image





@require_flax
class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	@cached_property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"			) if is_vision_available() else None







	@slow
	def     UpperCamelCase_  (	self :			str			):
					__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([-0.41_80, -1.50_51, -3.48_36]			)

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


from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				return np.linalg.norm(np.array(a_						) - np.array(a_						)						)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



				__A             =			zip(a_		,       a_						)
				# List of distances of all points from the point to be classified
				__A             =			[]
				for data_point in data:
								__A             =			euclidean_distance(data_point[0]		,       a_						)
								distances.append((distance, data_point[1])						)
				# Choosing 'k' points with the least distances.
				__A             =			[i[1] for i in sorted(a_						)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				__A             =			Counter(a_						).most_common(1						)[0][0]
				return classes[result]


if __name__ == "__main__":
					print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
