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        lowercase_  (  unittest.TestCase						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=3		,      __SCREAMING_SNAKE_CASE=32		,      __SCREAMING_SNAKE_CASE=3		,      __SCREAMING_SNAKE_CASE=10		,      __SCREAMING_SNAKE_CASE=[10, 20, 30, 40]		,      __SCREAMING_SNAKE_CASE=[1, 1, 2, 1]		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE="relu"		,      __SCREAMING_SNAKE_CASE=3		,      __SCREAMING_SNAKE_CASE=None		,      )						->Optional[int]:
								lowerCAmelCase     		=		parent
								lowerCAmelCase     		=		batch_size
								lowerCAmelCase     		=		image_size
								lowerCAmelCase     		=		num_channels
								lowerCAmelCase     		=		embeddings_size
								lowerCAmelCase     		=		hidden_sizes
								lowerCAmelCase     		=		depths
								lowerCAmelCase     		=		is_training
								lowerCAmelCase     		=		use_labels
								lowerCAmelCase     		=		hidden_act
								lowerCAmelCase     		=		num_labels
								lowerCAmelCase     		=		scope
								lowerCAmelCase     		=		len(_lowerCamelCase    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase     		=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

								lowerCAmelCase     		=		self.get_config()

								return config, pixel_values


	def       SCREAMING_SNAKE_CASE_    (							self    )						->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       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->List[str]:
								lowerCAmelCase     		=		FlaxResNetModel(config=_lowerCamelCase    )
								lowerCAmelCase     		=		model(_lowerCamelCase    )

								# 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       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								lowerCAmelCase     		=		self.num_labels
								lowerCAmelCase     		=		FlaxResNetForImageClassification(config=_lowerCamelCase    )
								lowerCAmelCase     		=		model(_lowerCamelCase    )
								self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)    )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[str]:
								lowerCAmelCase     		=		self.prepare_config_and_inputs()
								lowerCAmelCase		,				lowerCAmelCase     		=		config_and_inputs
								lowerCAmelCase     		=		{'''pixel_values''': pixel_values}
								return config, inputs_dict




@require_flax
class        lowercase_  (  _lowerCAmelCase				,					unittest.TestCase						):



	"""simple docstring"""





	UpperCAmelCase_  : List[str]       =    (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

	UpperCAmelCase_  : str       =    False
	UpperCAmelCase_  : Dict       =    False
	UpperCAmelCase_  : Tuple       =    False


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:
								lowerCAmelCase     		=		FlaxResNetModelTester(self    )
								lowerCAmelCase     		=		ConfigTester(self		,      config_class=_lowerCamelCase		,      has_text_modality=_lowerCamelCase    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->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       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								return


	def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
								lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCamelCase    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
								lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase    )


	@unittest.skip(reason='''ResNet does not use inputs_embeds'''    )
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								pass


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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs_for_common()

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

															lowerCAmelCase     		=		['''pixel_values''']
															self.assertListEqual(arg_names[:1]		,      _lowerCamelCase    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
								def check_hidden_states_output(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    ):
															lowerCAmelCase     		=		model_class(_lowerCamelCase    )

															lowerCAmelCase     		=		model(**self._prepare_for_class(_lowerCamelCase		,      _lowerCamelCase    )    )

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

															lowerCAmelCase     		=		self.model_tester.num_stages
															self.assertEqual(len(_lowerCamelCase    )		,      expected_num_stages + 1    )


	@unittest.skip(reason='''ResNet does not use feedforward chunking'''    )
	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								pass




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__    ):
																						lowerCAmelCase     		=		self._prepare_for_class(_lowerCamelCase		,      _lowerCamelCase    )
																						lowerCAmelCase     		=		model_class(_lowerCamelCase    )

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

																						with self.subTest('''JIT Enabled'''    ):
																													lowerCAmelCase     		=		model_jitted(**_lowerCamelCase    ).to_tuple()

																						with self.subTest('''JIT Disabled'''    ):
																													with jax.disable_jit():
																																				lowerCAmelCase     		=		model_jitted(**_lowerCamelCase    ).to_tuple()

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

def        SCREAMING_SNAKE_CASE_					(						)					->     Optional[int]:
							lowerCAmelCase     		=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							return image






@require_flax
class        lowercase_  (  unittest.TestCase						):



	"""simple docstring"""





	@cached_property
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:
								return AutoImageProcessor.from_pretrained('''microsoft/resnet-50'''    ) if is_vision_available() else None




	@slow
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:
								lowerCAmelCase     		=		FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50'''    )

								lowerCAmelCase     		=		self.default_image_processor
								lowerCAmelCase     		=		prepare_img()
								lowerCAmelCase     		=		image_processor(images=_lowerCamelCase		,      return_tensors='''np'''    )

								lowerCAmelCase     		=		model(**_lowerCamelCase    )

								# verify the logits
								lowerCAmelCase     		=		(1, 1000)
								self.assertEqual(outputs.logits.shape		,      _lowerCamelCase    )

								lowerCAmelCase     		=		jnp.array([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]    )

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




from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__      :    int          =     load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
lowercase__     ,   lowercase__     ,   lowercase__     ,   lowercase__      :    Any          =     train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

lowercase__      :    int          =     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__      :    List[str]          =     [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__      :    int          =     knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)



