



'''simple docstring'''

import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
	from PIL import Image
else:






	class 						a_		:


								@staticmethod
								def    UpperCamelCase     (				*a_   :						List[str]      ,						**a_   :						Tuple			)			->					str:
											pass







def a_     (    __UpperCAmelCase					)				->					str:





			"""simple docstring"""







			snake_case:    Any      							=hashlib.mda(image.tobytes()					)
			return m.hexdigest()[:10]







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    List[Any]      							=np.array(__UpperCAmelCase					)
			snake_case:    Union[str, Any]      							=npimg.shape
			return {"hash": hashimage(__UpperCAmelCase					), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class 						a_		(     unittest.TestCase    ):
							UpperCAmelCase			:		List[str]        =				dict(
							    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()    ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])    )
							UpperCAmelCase			:		List[str]        =				dict(
							    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()    ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])    )


							def    UpperCamelCase     (				self   :						int      ,						a_   :						List[Any]      ,						a_   :						str      ,						a_   :						Dict			)			->					Dict:
										snake_case:    Union[str, Any]      							=MaskGenerationPipeline(model=a_      ,						image_processor=a_			)
										return image_segmenter, [
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										]


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						Union[str, Any]      ,						a_   :						Optional[int]			)			->					List[str]:
										pass


							@require_tf
							@unittest.skip('Image segmentation not implemented in TF'			)
							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					List[str]:
										pass


							@slow
							@require_torch
							def    UpperCamelCase     (				self   :						Tuple			)			->					Dict:
										snake_case:    Any      							=pipeline('mask-generation'      ,						model='facebook/sam-vit-huge'			)

										snake_case:    List[Any]      							=image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'      ,						points_per_batch=2_5_6			)

										# Shortening by hashing
										snake_case:    Any      							=[]
										for i, o in enumerate(outputs['masks']			):
													new_outupt += [{"mask": mask_to_test_readable(a_			), "scores": outputs["scores"][i]}]

										# fmt: off
										self.assertEqual(
										    nested_simplify(a_      ,						decimals=4			)      ,						[
										        {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_4_4_4},
										        {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_2_1},
										        {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_1_6_7},
										        {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_1_3_2},
										        {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_0_5_3},
										        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_9_6_7},
										        {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_9_3},
										        {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_9_0_9},
										        {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_8_7_9},
										        {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_8_3_4},
										        {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_7_1_6},
										        {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_6_1_2},
										        {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_5_9_9},
										        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_5_5_2},
										        {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_5_3_2},
										        {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_5_1_6},
										        {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_4_9_9},
										        {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_4_8_3},
										        {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_4_6_4},
										        {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_4_3},
										        {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_4_3},
										        {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_4_0_8},
										        {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_3_3_5},
										        {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_3_2_6},
										        {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_2_6_2},
										        {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_9_9_9},
										        {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_9_8_6},
										        {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_9_8_4},
										        {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_8_7_3},
										        {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_8_7_1}
										    ]      ,						)
										# fmt: on




							@require_torch
							@slow
							def    UpperCamelCase     (				self   :						Any			)			->					List[str]:
										snake_case:    Union[str, Any]      							='facebook/sam-vit-huge'
										snake_case:    List[Any]      							=pipeline('mask-generation'      ,						model=a_			)

										snake_case:    List[Any]      							=image_segmenter(
										    'http://images.cocodataset.org/val2017/000000039769.jpg'      ,						pred_iou_thresh=1      ,						points_per_batch=2_5_6			)

										# Shortening by hashing
										snake_case:    Dict      							=[]
										for i, o in enumerate(outputs['masks']			):
													new_outupt += [{"mask": mask_to_test_readable(a_			), "scores": outputs["scores"][i]}]

										self.assertEqual(
										    nested_simplify(a_      ,						decimals=4			)      ,						[
										        {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_4_4_4},
										        {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_2_1_0},
										        {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_1_6_7},
										        {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_1_3_2},
										        {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_0_5_3},
										    ]      ,						)









'''simple docstring'''

import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
a							   =				'.'


if __name__ == "__main__":
	a							   =				os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
	a							   =				[]
	a							   =				[]
	with open(doctest_file_path) as fp:
		for line in fp:
			a							   =				line.strip()
			a							   =				os.path.join(REPO_PATH, line)
			if not (os.path.isfile(path) or os.path.isdir(path)):
				non_existent_paths.append(line)
			all_paths.append(path)
	if len(non_existent_paths) > 0:
		a							   =				'\n'.join(non_existent_paths)
		raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
	if all_paths != sorted(all_paths):
		raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')




