



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 				__A					( unittest.TestCase      ):





			def __init__(self							,    __magic_name__							,    __magic_name__=3							,    __magic_name__=32							,    __magic_name__=3							,    __magic_name__=10							,    __magic_name__=[10, 20, 30, 40]							,    __magic_name__=[1, 1, 2, 1]							,    __magic_name__=True							,    __magic_name__=True							,    __magic_name__="relu"							,    __magic_name__=3							,    __magic_name__=None							,    ):
						lowerCamelCase__ :      Union[str, Any]								=		parent
						lowerCamelCase__ :      Optional[Any]								=		batch_size
						lowerCamelCase__ :      Any								=		image_size
						lowerCamelCase__ :      str								=		num_channels
						lowerCamelCase__ :      Optional[int]								=		embeddings_size
						lowerCamelCase__ :      Optional[Any]								=		hidden_sizes
						lowerCamelCase__ :      int								=		depths
						lowerCamelCase__ :      str								=		is_training
						lowerCamelCase__ :      Tuple								=		use_labels
						lowerCamelCase__ :      List[str]								=		hidden_act
						lowerCamelCase__ :      Optional[int]								=		num_labels
						lowerCamelCase__ :      Tuple								=		scope
						lowerCamelCase__ :      List[Any]								=		len(__magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						lowerCamelCase__ :      str								=		self.get_config()

						return config, pixel_values





			def        _snake_case    (self				):
						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        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      List[Any]								=		FlaxRegNetModel(config=__magic_name__				)
						lowerCamelCase__ :      Union[str, Any]								=		model(__magic_name__				)

						# 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        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      List[Any]								=		self.num_labels
						lowerCamelCase__ :      Any								=		FlaxRegNetForImageClassification(config=__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		model(__magic_name__				)
						self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		self.prepare_config_and_inputs()
						lowerCamelCase__							,lowerCamelCase__ :      Dict								=		config_and_inputs
						lowerCamelCase__ :      int								=		{"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_flax
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:Any     =			(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

			UpperCamelCase					:Union[str, Any]     =			False
			UpperCamelCase					:Optional[Any]     =			False
			UpperCamelCase					:str     =			False





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		FlaxRegNetModelTester(self				)
						lowerCamelCase__ :      Tuple								=		ConfigTester(self							,    config_class=__magic_name__							,    has_text_modality=__magic_name__				)





			def        _snake_case    (self				):
						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        _snake_case    (self				):
						return





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__magic_name__				)





			@unittest.skip(reason="""RegNet does not use inputs_embeds"""				)
			def        _snake_case    (self				):
						pass





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





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		self.model_tester.prepare_config_and_inputs_for_common()

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

									lowerCamelCase__ :      Optional[int]								=		["""pixel_values"""]
									self.assertListEqual(arg_names[:1]							,    __magic_name__				)





			def        _snake_case    (self				):
						def check_hidden_states_output(__magic_name__							,    __magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Dict								=		model_class(__magic_name__				)

									lowerCamelCase__ :      str								=		model(**self._prepare_for_class(__magic_name__							,    __magic_name__				)				)

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

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

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

						for model_class in self.all_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		True
									check_hidden_states_output(__magic_name__							,    __magic_name__							,    __magic_name__				)

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

									check_hidden_states_output(__magic_name__							,    __magic_name__							,    __magic_name__				)





			def        _snake_case    (self				):
						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__ :      Optional[Any]								=		self._prepare_for_class(__magic_name__							,    __magic_name__				)
												lowerCamelCase__ :      int								=		model_class(__magic_name__				)

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

												with self.subTest("""JIT Enabled"""				):
															lowerCamelCase__ :      str								=		model_jitted(**__magic_name__				).to_tuple()

												with self.subTest("""JIT Disabled"""				):
															with jax.disable_jit():
																		lowerCamelCase__ :      List[str]								=		model_jitted(**__magic_name__				).to_tuple()

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







def      _A				()  ->int:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
			return image
@require_flax
class 				__A					( unittest.TestCase      ):





			@cached_property
			def        _snake_case    (self				):
						return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""				) if is_vision_available() else None





			@slow
			def        _snake_case    (self				):
						lowerCamelCase__ :      Any								=		FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""				)

						lowerCamelCase__ :      Optional[Any]								=		self.default_image_processor
						lowerCamelCase__ :      Any								=		prepare_img()
						lowerCamelCase__ :      Tuple								=		image_processor(images=__magic_name__							,    return_tensors="""np"""				)

						lowerCamelCase__ :      Union[str, Any]								=		model(**__magic_name__				)

						# verify the logits
						lowerCamelCase__ :      Optional[Any]								=		(1, 1000)
						self.assertEqual(outputs.logits.shape							,    __magic_name__				)

						lowerCamelCase__ :      Any								=		jnp.array([-0.41_80, -1.50_51, -3.48_36]				)

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








from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def      _A				(UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->np.ndarray:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		cva.getAffineTransform(UpperCamelCase      ,			UpperCamelCase )
			return cva.warpAffine(UpperCamelCase      ,			UpperCamelCase      ,			(rows, cols) )


if __name__ == "__main__":
				# read original image
				_lowercase     							=						cva.imread(
				    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
				)
				# turn image in gray scale value
				_lowercase     							=						cva.cvtColor(image, cva.COLOR_BGR2GRAY)
				# get image shape
				_lowercase							,				_lowercase     							=						gray_img.shape

				# set different points to rotate image
				_lowercase     							=						np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
				_lowercase     							=						np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
				_lowercase     							=						np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
				_lowercase     							=						np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

				# add all rotated images in a list
				_lowercase     							=						[
				    gray_img,
				    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
				    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
				    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
				]

				# plot different image rotations
				_lowercase     							=						plt.figure(1)
				_lowercase     							=						['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
				for i, image in enumerate(images):
								plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
								plt.title(titles[i])
								plt.axis('''off''')
								plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
				plt.show()



