
'''simple docstring'''







import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						_snake_case					: Any     			=					False


if is_vision_available():
						from PIL import Image
else:


						class       lowerCAmelCase			:




								@staticmethod
								def      lowercase							(					*UpperCamelCase       ,    **UpperCamelCase   ):
														pass


@is_pipeline_test
@require_vision
class       lowerCAmelCase			(					unittest.TestCase      ):
		a				:       int							=       MODEL_FOR_VISION_2_SEQ_MAPPING
		a				:       List[Any]							=       TF_MODEL_FOR_VISION_2_SEQ_MAPPING




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model=UpperCamelCase       ,    tokenizer=UpperCamelCase       ,    image_processor=UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			[
								    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   ),
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]
								return pipe, examples




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase   )
								self.assertEqual(
								    UpperCamelCase       ,    [
								        [{"generated_text": ANY(UpperCamelCase   )}],
								        [{"generated_text": ANY(UpperCamelCase   )}],
								    ]       ,    )




		@require_tf
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="hf-internal-testing/tiny-random-vit-gpt2"       ,    framework="tf"   )
								_SCREAMING_SNAKE_CASE					=			"./tests/fixtures/tests_samples/COCO/000000039769.png"

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase   )
								self.assertEqual(
								    UpperCamelCase       ,    [
								        {
								            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
								        },
								    ]       ,    )

								_SCREAMING_SNAKE_CASE					=			pipe([image, image]   )
								self.assertEqual(
								    UpperCamelCase       ,    [
								        [
								            {
								                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
								            }
								        ],
								        [
								            {
								                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
								            }
								        ],
								    ]       ,    )

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase       ,    max_new_tokens=1   )
								self.assertEqual(
								    UpperCamelCase       ,    [{"generated_text": "growth"}]       ,    )




		@require_torch
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="hf-internal-testing/tiny-random-vit-gpt2"   )
								_SCREAMING_SNAKE_CASE					=			"./tests/fixtures/tests_samples/COCO/000000039769.png"

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase   )
								self.assertEqual(
								    UpperCamelCase       ,    [
								        {
								            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
								        },
								    ]       ,    )

								_SCREAMING_SNAKE_CASE					=			pipe([image, image]   )
								self.assertEqual(
								    UpperCamelCase       ,    [
								        [
								            {
								                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
								            }
								        ],
								        [
								            {
								                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
								            }
								        ],
								    ]       ,    )




		@require_torch
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="hf-internal-testing/tiny-random-BlipForConditionalGeneration"   )
								_SCREAMING_SNAKE_CASE					=			"./tests/fixtures/tests_samples/COCO/000000039769.png"
								_SCREAMING_SNAKE_CASE					=			"a photo of"

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase       ,    prompt=UpperCamelCase   )
								self.assertTrue(outputs[0]["generated_text"].startswith(UpperCamelCase   )   )




		@slow
		@require_torch
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="ydshieh/vit-gpt2-coco-en"   )
								_SCREAMING_SNAKE_CASE					=			"./tests/fixtures/tests_samples/COCO/000000039769.png"

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase   )
								self.assertEqual(UpperCamelCase       ,    [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]   )

								_SCREAMING_SNAKE_CASE					=			pipe([image, image]   )
								self.assertEqual(
								    UpperCamelCase       ,    [
								        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
								        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
								    ]       ,    )




		@slow
		@require_torch
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="Salesforce/blip-image-captioning-base"   )
								_SCREAMING_SNAKE_CASE					=			"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
								_SCREAMING_SNAKE_CASE					=			Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase   )
								self.assertEqual(UpperCamelCase       ,    [{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}]   )




		@slow
		@require_torch
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="microsoft/git-base-coco"   )
								_SCREAMING_SNAKE_CASE					=			"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
								_SCREAMING_SNAKE_CASE					=			Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase   )
								self.assertEqual(UpperCamelCase       ,    [{"generated_text": "a cartoon of a purple character."}]   )




		@slow
		@require_torch
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="Salesforce/blip-image-captioning-base"   )
								_SCREAMING_SNAKE_CASE					=			"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
								_SCREAMING_SNAKE_CASE					=			Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )

								_SCREAMING_SNAKE_CASE					=			"a photography of"

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase       ,    prompt=UpperCamelCase   )
								self.assertEqual(UpperCamelCase       ,    [{"generated_text": "a photography of a volcano"}]   )

								with self.assertRaises(UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			pipe([image, image]       ,    prompt=[prompt, prompt]   )




		@slow
		@require_torch
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="microsoft/git-base-coco"   )
								_SCREAMING_SNAKE_CASE					=			"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
								_SCREAMING_SNAKE_CASE					=			Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )

								_SCREAMING_SNAKE_CASE					=			"a photo of a"

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase       ,    prompt=UpperCamelCase   )
								self.assertEqual(UpperCamelCase       ,    [{"generated_text": "a photo of a tent with a tent and a tent in the background."}]   )

								with self.assertRaises(UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			pipe([image, image]       ,    prompt=[prompt, prompt]   )




		@unittest.skipIf(
		    not is_torch_greater_or_equal_than_1_11       ,    reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   )
		@slow
		@require_torch
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="google/pix2struct-ai2d-base"   )
								_SCREAMING_SNAKE_CASE					=			"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
								_SCREAMING_SNAKE_CASE					=			Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )

								_SCREAMING_SNAKE_CASE					=			"What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase       ,    prompt=UpperCamelCase   )
								self.assertEqual(UpperCamelCase       ,    [{"generated_text": "ash cloud"}]   )

								with self.assertRaises(UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			pipe([image, image]       ,    prompt=[prompt, prompt]   )




		@slow
		@require_tf
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			pipeline("image-to-text"       ,    model="ydshieh/vit-gpt2-coco-en"       ,    framework="tf"   )
								_SCREAMING_SNAKE_CASE					=			"./tests/fixtures/tests_samples/COCO/000000039769.png"

								_SCREAMING_SNAKE_CASE					=			pipe(UpperCamelCase   )
								self.assertEqual(UpperCamelCase       ,    [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]   )

								_SCREAMING_SNAKE_CASE					=			pipe([image, image]   )
								self.assertEqual(
								    UpperCamelCase       ,    [
								        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
								        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
								    ]       ,    )

'''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)