





"""simple docstring"""






import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
		import jax

		from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor




class 			__lowercase     (     unittest.TestCase							):







					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=100		,     _UpperCAmelCase=13		,     _UpperCAmelCase=30		,     _UpperCAmelCase=2		,     _UpperCAmelCase=3		,     _UpperCAmelCase=True		,     _UpperCAmelCase=True		,     _UpperCAmelCase=32		,     _UpperCAmelCase=5		,     _UpperCAmelCase=4		,     _UpperCAmelCase=37		,     _UpperCAmelCase="gelu"		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=10		,     _UpperCAmelCase=0.0_2		,     _UpperCAmelCase=3		,     ):
									__a	:  Optional[Any]         =					parent
									__a	:  Tuple         =					vocab_size
									__a	:  str         =					batch_size
									__a	:  List[str]         =					image_size
									__a	:  Any         =					patch_size
									__a	:  Any         =					num_channels
									__a	:  Dict         =					is_training
									__a	:  Optional[int]         =					use_labels
									__a	:  Optional[int]         =					hidden_size
									__a	:  str         =					num_hidden_layers
									__a	:  Any         =					num_attention_heads
									__a	:  Optional[int]         =					intermediate_size
									__a	:  Dict         =					hidden_act
									__a	:  Optional[int]         =					hidden_dropout_prob
									__a	:  str         =					attention_probs_dropout_prob
									__a	:  Dict         =					type_sequence_label_size
									__a	:  Optional[int]         =					initializer_range

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									__a	:  Union[str, Any]         =					(image_size // patch_size) ** 2
									__a	:  Tuple         =					num_patches + 1







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

									__a	:  str         =					None
									if self.use_labels:
													__a	:  Any         =					ids_tensor([self.batch_size]		,     self.type_sequence_label_size       )

									__a	:  Optional[Any]         =					BeitConfig(
									    vocab_size=self.vocab_size		,     image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     is_decoder=SCREAMING_SNAKE_CASE_		,     initializer_range=self.initializer_range		,     )

									return config, pixel_values, labels







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[Any]         =					FlaxBeitModel(config=SCREAMING_SNAKE_CASE_       )
									__a	:  int         =					model(SCREAMING_SNAKE_CASE_       )
									self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  List[Any]         =					FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_       )
									__a	:  Dict         =					model(SCREAMING_SNAKE_CASE_       )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length - 1, self.vocab_size)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  Optional[Any]         =					self.type_sequence_label_size
									__a	:  Tuple         =					FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE_       )
									__a	:  Union[str, Any]         =					model(SCREAMING_SNAKE_CASE_       )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)       )

									# test greyscale images
									__a	:  Union[str, Any]         =					1
									__a	:  Tuple         =					FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE_       )

									__a	:  str         =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
									__a	:  Optional[int]         =					model(SCREAMING_SNAKE_CASE_       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					self.prepare_config_and_inputs()
									(
									    __a    
									)	:  List[str]         =					config_and_inputs
									__a	:  List[str]         =					{"""pixel_values""": pixel_values}
									return config, inputs_dict






@require_flax
class 			__lowercase     (     _lowerCAmelCase				, unittest.TestCase							):
					__lowerCAmelCase					=					(
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					FlaxBeitModelTester(self       )
									__a	:  Tuple         =					ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE_		,     has_text_modality=SCREAMING_SNAKE_CASE_		,     hidden_size=37       )







					def 	_lowerCamelCase						(  self       ):
									self.config_tester.run_common_tests()







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

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

													__a	:  str         =					["""pixel_values"""]
													self.assertListEqual(arg_names[:1]		,     SCREAMING_SNAKE_CASE_       )







					def 	_lowerCamelCase						(  self       ):
									__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	:  str         =					self._prepare_for_class(SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_       )
																	__a	:  List[Any]         =					model_class(SCREAMING_SNAKE_CASE_       )

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

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

																	with self.subTest('''JIT Disabled'''       ):
																					with jax.disable_jit():
																									__a	:  int         =					model_jitted(**SCREAMING_SNAKE_CASE_       ).to_tuple()

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







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )







					@slow
					def 	_lowerCamelCase						(  self       ):
									for model_class_name in self.all_model_classes:
													__a	:  List[Any]         =					model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''       )
													__a	:  str         =					model(np.ones((1, 3, 224, 224)       )       )
													self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )






def       __A (     )						->						int:
				__a	:  Union[str, Any]         =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image






@require_vision
@require_flax
class 			__lowercase     (     unittest.TestCase							):







					@cached_property
					def 	_lowerCamelCase						(  self       ):
									return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''       ) if is_vision_available() else None







					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''       )

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

									# prepare bool_masked_pos
									__a	:  Any         =					np.ones((1, 196)		,     dtype=SCREAMING_SNAKE_CASE_       )

									# forward pass
									__a	:  List[str]         =					model(pixel_values=SCREAMING_SNAKE_CASE_		,     bool_masked_pos=SCREAMING_SNAKE_CASE_       )
									__a	:  List[str]         =					outputs.logits

									# verify the logits
									__a	:  Union[str, Any]         =					(1, 196, 8192)
									self.assertEqual(logits.shape		,     SCREAMING_SNAKE_CASE_       )

									__a	:  Union[str, Any]         =					np.array(
									    [[-3.2_4_3_7, 0.5_0_7_2, -13.9174], [-3.2_4_5_6, 0.4_9_4_8, -13.9401], [-3.2_0_3_3, 0.5_1_2_1, -13.8550]]       )

									self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]		,     SCREAMING_SNAKE_CASE_		,     atol=1e-2       )       )







					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''       )

									__a	:  Optional[Any]         =					self.default_image_processor
									__a	:  Dict         =					prepare_img()
									__a	:  Union[str, Any]         =					image_processor(images=SCREAMING_SNAKE_CASE_		,     return_tensors='''np'''       )

									# forward pass
									__a	:  Union[str, Any]         =					model(**SCREAMING_SNAKE_CASE_       )
									__a	:  List[str]         =					outputs.logits

									# verify the logits
									__a	:  List[str]         =					(1, 1000)
									self.assertEqual(logits.shape		,     SCREAMING_SNAKE_CASE_       )

									__a	:  str         =					np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]       )

									self.assertTrue(np.allclose(logits[0, :3]		,     SCREAMING_SNAKE_CASE_		,     atol=1e-4       )       )

									__a	:  Any         =					281
									self.assertEqual(logits.argmax(-1       ).item()		,     SCREAMING_SNAKE_CASE_       )







					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''       )

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

									# forward pass
									__a	:  Optional[Any]         =					model(**SCREAMING_SNAKE_CASE_       )
									__a	:  List[str]         =					outputs.logits

									# verify the logits
									__a	:  Optional[Any]         =					(1, 21841)
									self.assertEqual(logits.shape		,     SCREAMING_SNAKE_CASE_       )

									__a	:  List[str]         =					np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]       )

									self.assertTrue(np.allclose(logits[0, :3]		,     SCREAMING_SNAKE_CASE_		,     atol=1e-4       )       )

									__a	:  List[str]         =					2396
									self.assertEqual(logits.argmax(-1       ).item()		,     SCREAMING_SNAKE_CASE_       )






"""simple docstring"""






from pathlib import Path

import numpy as np
from PIL import Image

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				__a    ,  __a    ,  __a	:  Union[str, Any]         =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
				return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				return (gray > 1_27) & (gray <= 2_55)

def       __A (     a_  :np.ndarray				,    a_  :np.ndarray)						->						np.ndarray:
				__a	:  Optional[int]         =					np.zeros_like(a_)
				__a	:  Dict         =					np.zeros(
				    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1))

				# Copy image to padded image
				__a	:  int         =					image

				# Iterate over image & apply kernel
				for x in range(image.shape[1]):
								for y in range(image.shape[0]):
												__a	:  Optional[Any]         =					(
												    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
												).sum()
												__a	:  Any         =					int(summation > 0)
				return output


if __name__ == "__main__":
		# read original image
		A	     =					Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
		A	     =					np.array(Image.open(lena_path))
		# kernel to be applied
		A	     =					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
		A	     =					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
		# Save the output image
		A	     =					Image.fromarray(output).convert('''RGB''')
		pil_img.save('''result_dilation.png''')