
'''simple docstring'''







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
						import torch


class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase=14       ,    UpperCamelCase=7       ,    UpperCamelCase=True       ,    UpperCamelCase=True       ,    UpperCamelCase=False       ,    UpperCamelCase=True       ,    UpperCamelCase=99       ,    UpperCamelCase=32       ,    UpperCamelCase=4       ,    UpperCamelCase=4       ,    UpperCamelCase=[[["global", "local"], 2]]       ,    UpperCamelCase=37       ,    UpperCamelCase="gelu"       ,    UpperCamelCase=0.1       ,    UpperCamelCase=0.1       ,    UpperCamelCase=512       ,    UpperCamelCase=7       ,    UpperCamelCase=0.02       ,    ):
								_SCREAMING_SNAKE_CASE					=			parent
								_SCREAMING_SNAKE_CASE					=			batch_size
								_SCREAMING_SNAKE_CASE					=			seq_length
								_SCREAMING_SNAKE_CASE					=			is_training
								_SCREAMING_SNAKE_CASE					=			use_input_mask
								_SCREAMING_SNAKE_CASE					=			use_token_type_ids
								_SCREAMING_SNAKE_CASE					=			use_labels
								_SCREAMING_SNAKE_CASE					=			vocab_size
								_SCREAMING_SNAKE_CASE					=			hidden_size
								_SCREAMING_SNAKE_CASE					=			num_hidden_layers
								_SCREAMING_SNAKE_CASE					=			num_attention_heads
								_SCREAMING_SNAKE_CASE					=			attention_types
								_SCREAMING_SNAKE_CASE					=			intermediate_size
								_SCREAMING_SNAKE_CASE					=			hidden_act
								_SCREAMING_SNAKE_CASE					=			hidden_dropout_prob
								_SCREAMING_SNAKE_CASE					=			attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE					=			max_position_embeddings
								_SCREAMING_SNAKE_CASE					=			window_size
								_SCREAMING_SNAKE_CASE					=			initializer_range
								_SCREAMING_SNAKE_CASE					=			None
								_SCREAMING_SNAKE_CASE					=			vocab_size - 1
								_SCREAMING_SNAKE_CASE					=			vocab_size - 1
								_SCREAMING_SNAKE_CASE					=			vocab_size - 1




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size   )

								_SCREAMING_SNAKE_CASE					=			None
								if self.use_input_mask:
														_SCREAMING_SNAKE_CASE					=			random_attention_mask([self.batch_size, self.seq_length]   )

								_SCREAMING_SNAKE_CASE					=			GPTNeoConfig(
								    vocab_size=self.vocab_size       ,    hidden_size=self.hidden_size       ,    num_layers=self.num_hidden_layers       ,    num_heads=self.num_attention_heads       ,    max_position_embeddings=self.max_position_embeddings       ,    use_cache=UpperCamelCase       ,    bos_token_id=self.bos_token_id       ,    eos_token_id=self.eos_token_id       ,    pad_token_id=self.pad_token_id       ,    window_size=self.window_size       ,    attention_types=self.attention_types       ,    )

								return (config, input_ids, input_mask)




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.prepare_config_and_inputs()
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			config_and_inputs
								_SCREAMING_SNAKE_CASE					=			{"input_ids": input_ids, "attention_mask": attention_mask}
								return config, inputs_dict




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			20
								_SCREAMING_SNAKE_CASE					=			model_class_name(UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			model.init_cache(input_ids.shape[0]       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			jnp.ones((input_ids.shape[0], max_decoder_length)       ,    dtype="i4"   )

								_SCREAMING_SNAKE_CASE					=			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1   )[None, :]       ,    (input_ids.shape[0], input_ids.shape[-1] - 1)   )
								_SCREAMING_SNAKE_CASE					=			model(
								    input_ids[:, :-1]       ,    attention_mask=UpperCamelCase       ,    past_key_values=UpperCamelCase       ,    position_ids=UpperCamelCase       ,    )

								_SCREAMING_SNAKE_CASE					=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,    dtype="i4"   )
								_SCREAMING_SNAKE_CASE					=			model(
								    input_ids[:, -1:]       ,    attention_mask=UpperCamelCase       ,    past_key_values=outputs_cache.past_key_values       ,    position_ids=UpperCamelCase       ,    )

								_SCREAMING_SNAKE_CASE					=			model(UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
								self.parent.assertTrue(diff < 1e-3       ,    msg=F'Max diff is {diff}'   )




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			20
								_SCREAMING_SNAKE_CASE					=			model_class_name(UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			jnp.concatenate(
								    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]       ,    axis=-1       ,    )

								_SCREAMING_SNAKE_CASE					=			model.init_cache(input_ids.shape[0]       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1   )[None, :]       ,    (input_ids.shape[0], input_ids.shape[-1] - 1)   )

								_SCREAMING_SNAKE_CASE					=			model(
								    input_ids[:, :-1]       ,    attention_mask=UpperCamelCase       ,    past_key_values=UpperCamelCase       ,    position_ids=UpperCamelCase       ,    )
								_SCREAMING_SNAKE_CASE					=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,    dtype="i4"   )
								_SCREAMING_SNAKE_CASE					=			model(
								    input_ids[:, -1:]       ,    past_key_values=outputs_cache.past_key_values       ,    attention_mask=UpperCamelCase       ,    position_ids=UpperCamelCase       ,    )

								_SCREAMING_SNAKE_CASE					=			model(UpperCamelCase       ,    attention_mask=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
								self.parent.assertTrue(diff < 1e-3       ,    msg=F'Max diff is {diff}'   )


@require_flax
class       lowerCAmelCase			(					__UpperCAmelCase		,  __UpperCAmelCase		,  unittest.TestCase      ):
		a				:       int							=       (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
		a				:       Dict							=       (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			FlaxGPTNeoModelTester(self   )




		def      lowercase							(					self   ):
								for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )




		def      lowercase							(					self   ):
								for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )




		@slow
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			GPTaTokenizer.from_pretrained("gpt2"       ,    pad_token="<|endoftext|>"       ,    padding_side="left"   )
								_SCREAMING_SNAKE_CASE					=			tokenizer(["Hello this is a long string", "Hey"]       ,    return_tensors="np"       ,    padding=UpperCamelCase       ,    truncation=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"   )
								_SCREAMING_SNAKE_CASE					=			False
								_SCREAMING_SNAKE_CASE					=			model.config.eos_token_id

								_SCREAMING_SNAKE_CASE					=			jax.jit(model.generate   )

								_SCREAMING_SNAKE_CASE					=			jit_generate(
								    inputs["input_ids"]       ,    attention_mask=inputs["attention_mask"]       ,    pad_token_id=tokenizer.pad_token_id   ).sequences

								_SCREAMING_SNAKE_CASE					=			tokenizer.batch_decode(UpperCamelCase       ,    skip_special_tokens=UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			[
								    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
								    "Hey, I'm a little late to the party. I'm going to",
								]

								self.assertListEqual(UpperCamelCase       ,    UpperCamelCase   )




		@is_pt_flax_cross_test
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__   ):
																				# prepare inputs
																				_SCREAMING_SNAKE_CASE					=			self._prepare_for_class(UpperCamelCase       ,    UpperCamelCase   )
																				_SCREAMING_SNAKE_CASE					=			{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																				# load corresponding PyTorch class
																				_SCREAMING_SNAKE_CASE					=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																				_SCREAMING_SNAKE_CASE					=			getattr(UpperCamelCase       ,    UpperCamelCase   )

																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			pt_inputs["input_ids"].shape
																				_SCREAMING_SNAKE_CASE					=			np.random.randint(0       ,    seq_length - 1       ,    size=(batch_size,)   )
																				for batch_idx, start_index in enumerate(UpperCamelCase   ):
																										_SCREAMING_SNAKE_CASE					=			0
																										_SCREAMING_SNAKE_CASE					=			1
																										_SCREAMING_SNAKE_CASE					=			0
																										_SCREAMING_SNAKE_CASE					=			1
																				_SCREAMING_SNAKE_CASE					=			pt_model_class(UpperCamelCase   ).eval()
																				_SCREAMING_SNAKE_CASE					=			model_class(UpperCamelCase       ,    dtype=jnp.floataa   )

																				_SCREAMING_SNAKE_CASE					=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,    UpperCamelCase   )
																				_SCREAMING_SNAKE_CASE					=			fx_state

																				with torch.no_grad():
																										_SCREAMING_SNAKE_CASE					=			pt_model(**UpperCamelCase   ).to_tuple()

																				_SCREAMING_SNAKE_CASE					=			fx_model(**UpperCamelCase   ).to_tuple()
																				self.assertEqual(len(UpperCamelCase   )       ,    len(UpperCamelCase   )       ,    "Output lengths differ between Flax and PyTorch"   )
																				for fx_output, pt_output in zip(UpperCamelCase       ,    UpperCamelCase   ):
																										self.assert_almost_equals(fx_output[:, -1]       ,    pt_output[:, -1].numpy()       ,    4e-2   )

																				with tempfile.TemporaryDirectory() as tmpdirname:
																										pt_model.save_pretrained(UpperCamelCase   )
																										_SCREAMING_SNAKE_CASE					=			model_class.from_pretrained(UpperCamelCase       ,    from_pt=UpperCamelCase   )

																				_SCREAMING_SNAKE_CASE					=			fx_model_loaded(**UpperCamelCase   ).to_tuple()
																				self.assertEqual(
																				    len(UpperCamelCase   )       ,    len(UpperCamelCase   )       ,    "Output lengths differ between Flax and PyTorch"   )
																				for fx_output_loaded, pt_output in zip(UpperCamelCase       ,    UpperCamelCase   ):
																										self.assert_almost_equals(fx_output_loaded[:, -1]       ,    pt_output[:, -1].numpy()       ,    4e-2   )




		@is_pt_flax_cross_test
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__   ):
																				# prepare inputs
																				_SCREAMING_SNAKE_CASE					=			self._prepare_for_class(UpperCamelCase       ,    UpperCamelCase   )
																				_SCREAMING_SNAKE_CASE					=			{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																				# load corresponding PyTorch class
																				_SCREAMING_SNAKE_CASE					=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																				_SCREAMING_SNAKE_CASE					=			getattr(UpperCamelCase       ,    UpperCamelCase   )

																				_SCREAMING_SNAKE_CASE					=			pt_model_class(UpperCamelCase   ).eval()
																				_SCREAMING_SNAKE_CASE					=			model_class(UpperCamelCase       ,    dtype=jnp.floataa   )

																				_SCREAMING_SNAKE_CASE					=			load_flax_weights_in_pytorch_model(UpperCamelCase       ,    fx_model.params   )
																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			pt_inputs["input_ids"].shape
																				_SCREAMING_SNAKE_CASE					=			np.random.randint(0       ,    seq_length - 1       ,    size=(batch_size,)   )
																				for batch_idx, start_index in enumerate(UpperCamelCase   ):
																										_SCREAMING_SNAKE_CASE					=			0
																										_SCREAMING_SNAKE_CASE					=			1
																										_SCREAMING_SNAKE_CASE					=			0
																										_SCREAMING_SNAKE_CASE					=			1

																				# make sure weights are tied in PyTorch
																				pt_model.tie_weights()

																				with torch.no_grad():
																										_SCREAMING_SNAKE_CASE					=			pt_model(**UpperCamelCase   ).to_tuple()

																				_SCREAMING_SNAKE_CASE					=			fx_model(**UpperCamelCase   ).to_tuple()
																				self.assertEqual(len(UpperCamelCase   )       ,    len(UpperCamelCase   )       ,    "Output lengths differ between Flax and PyTorch"   )
																				for fx_output, pt_output in zip(UpperCamelCase       ,    UpperCamelCase   ):
																										self.assert_almost_equals(fx_output[:, -1]       ,    pt_output[:, -1].numpy()       ,    4e-2   )

																				with tempfile.TemporaryDirectory() as tmpdirname:
																										fx_model.save_pretrained(UpperCamelCase   )
																										_SCREAMING_SNAKE_CASE					=			pt_model_class.from_pretrained(UpperCamelCase       ,    from_flax=UpperCamelCase   )

																				with torch.no_grad():
																										_SCREAMING_SNAKE_CASE					=			pt_model_loaded(**UpperCamelCase   ).to_tuple()

																				self.assertEqual(
																				    len(UpperCamelCase   )       ,    len(UpperCamelCase   )       ,    "Output lengths differ between Flax and PyTorch"   )
																				for fx_output, pt_output in zip(UpperCamelCase       ,    UpperCamelCase   ):
																										self.assert_almost_equals(fx_output[:, -1]       ,    pt_output[:, -1].numpy()       ,    4e-2   )




		@slow
		def      lowercase							(					self   ):
								for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE					=			model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"   )
														_SCREAMING_SNAKE_CASE					=			model(np.ones((1, 1)   )   )
														self.assertIsNotNone(UpperCamelCase   )

'''simple docstring'''







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_snake_case					: Union[str, Any]     			=					180



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			k_size // 2
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_SCREAMING_SNAKE_CASE					=			(
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE	) + np.square(_SCREAMING_SNAKE_CASE	)) / (2 * np.square(_SCREAMING_SNAKE_CASE	))	)
						)
						return g



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		_SCREAMING_SNAKE_CASE					=			gradient_direction[row, col]

																		if (
																		    0 <= direction < PI / 8
																		    or 15 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    PI / 8 <= direction < 3 * PI / 8
																		    or 9 * PI / 8 <= direction < 11 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col + 1]
																								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    3 * PI / 8 <= direction < 5 * PI / 8
																		    or 11 * PI / 8 <= direction < 13 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col]
																								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    5 * PI / 8 <= direction < 7 * PI / 8
																		    or 13 * PI / 8 <= direction < 15 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col + 1]
																								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

						return destination



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		if destination[row, col] >= threshold_high:
																								_SCREAMING_SNAKE_CASE					=			strong
																		elif destination[row, col] <= threshold_low:
																								_SCREAMING_SNAKE_CASE					=			0
																		else:
																								_SCREAMING_SNAKE_CASE					=			weak



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Any	):
						for row in range(1						,      image_shape[0]	):
												for col in range(1						,      image_shape[1]	):
																		if destination[row, col] == weak:
																								if 255 in (
																								    destination[row, col + 1],
																								    destination[row, col - 1],
																								    destination[row - 1, col],
																								    destination[row + 1, col],
																								    destination[row - 1, col - 1],
																								    destination[row + 1, col - 1],
																								    destination[row - 1, col + 1],
																								    destination[row + 1, col + 1],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: List[str]=15						,      _SCREAMING_SNAKE_CASE		: List[Any]=30						,      _SCREAMING_SNAKE_CASE		: Optional[int]=128						,      _SCREAMING_SNAKE_CASE		: List[Any]=255	):
						# gaussian_filter
						_SCREAMING_SNAKE_CASE					=			img_convolve(_SCREAMING_SNAKE_CASE						,      gen_gaussian_kernel(9						,      sigma=1.4	)	)
						# get the gradient and degree by sobel_filter
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			sobel_filter(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			PI + np.radadeg(_SCREAMING_SNAKE_CASE	)

						_SCREAMING_SNAKE_CASE					=			suppress_non_maximum(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						detect_high_low_threshold(
						    image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						track_edge(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						return destination


if __name__ == "__main__":
						# read original image in gray mode
						_snake_case					: Any     			=					cva.imread(R"""../image_data/lena.jpg""", 0)
						# canny edge detection
						_snake_case					: int     			=					canny(lena)
						cva.imshow("""canny""", canny_destination)
						cva.waitKey(0)