



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  __magic_name__   :

				'''simple docstring'''





				@staticmethod
				def SCREAMING_SNAKE_CASE_			(						*UpperCamelCase__      :     str				,		**UpperCamelCase__      :     Any      )				->					int:
										'''simple docstring'''







										pass

def    lowerCamelCase_(lowerCamelCase_    ) ->       str:
						UpperCAmelCase         =     hashlib.mda(image.tobytes()    )
						return m.hexdigest()[:10]

def    lowerCamelCase_(lowerCamelCase_    ) ->       Dict:
						UpperCAmelCase         =     np.array(lowerCamelCase_    )
						UpperCAmelCase         =     npimg.shape
						return {"hash": hashimage(lowerCamelCase_    ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class  __magic_name__   (							unittest.TestCase						):

		'''simple docstring'''





		lowercase      :       int          =dict(
		    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if MODEL_FOR_MASK_GENERATION_MAPPING else [])						)
		lowercase      :       List[Any]          =dict(
		    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])						)






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     List[str]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     MaskGenerationPipeline(model=A__				,		image_processor=A__      )
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     List[str]      )				->					Any:
								'''simple docstring'''







								pass






		@require_tf
		@unittest.skip("Image segmentation not implemented in TF"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Dict:
								'''simple docstring'''







								pass






		@slow
		@require_torch
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     pipeline("mask-generation"				,		model="facebook/sam-vit-huge"      )

								UpperCAmelCase         =     image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"				,		points_per_batch=2_56      )

								# Shortening by hashing
								UpperCAmelCase         =     []
								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_80, 6_40)}, "scores": 1.04_44},
								        {"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0_21},
								        {"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.01_67},
								        {"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.01_32},
								        {"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.00_53},
								        {"mask": {"hash": "e2d0b7a0b7", "shape": (4_80, 6_40)}, "scores": 0.99_67},
								        {"mask": {"hash": "453c7844bd", "shape": (4_80, 6_40)}, "scores": 0.9_93},
								        {"mask": {"hash": "3d44f2926d", "shape": (4_80, 6_40)}, "scores": 0.99_09},
								        {"mask": {"hash": "64033ddc3f", "shape": (4_80, 6_40)}, "scores": 0.98_79},
								        {"mask": {"hash": "801064ff79", "shape": (4_80, 6_40)}, "scores": 0.98_34},
								        {"mask": {"hash": "6172f276ef", "shape": (4_80, 6_40)}, "scores": 0.97_16},
								        {"mask": {"hash": "b49e60e084", "shape": (4_80, 6_40)}, "scores": 0.96_12},
								        {"mask": {"hash": "a811e775fd", "shape": (4_80, 6_40)}, "scores": 0.95_99},
								        {"mask": {"hash": "a6a8ebcf4b", "shape": (4_80, 6_40)}, "scores": 0.95_52},
								        {"mask": {"hash": "9d8257e080", "shape": (4_80, 6_40)}, "scores": 0.95_32},
								        {"mask": {"hash": "32de6454a8", "shape": (4_80, 6_40)}, "scores": 0.95_16},
								        {"mask": {"hash": "af3d4af2c8", "shape": (4_80, 6_40)}, "scores": 0.94_99},
								        {"mask": {"hash": "3c6db475fb", "shape": (4_80, 6_40)}, "scores": 0.94_83},
								        {"mask": {"hash": "c290813fb9", "shape": (4_80, 6_40)}, "scores": 0.94_64},
								        {"mask": {"hash": "b6f0b8f606", "shape": (4_80, 6_40)}, "scores": 0.9_43},
								        {"mask": {"hash": "92ce16bfdf", "shape": (4_80, 6_40)}, "scores": 0.9_43},
								        {"mask": {"hash": "c749b25868", "shape": (4_80, 6_40)}, "scores": 0.94_08},
								        {"mask": {"hash": "efb6cab859", "shape": (4_80, 6_40)}, "scores": 0.93_35},
								        {"mask": {"hash": "1ff2eafb30", "shape": (4_80, 6_40)}, "scores": 0.93_26},
								        {"mask": {"hash": "788b798e24", "shape": (4_80, 6_40)}, "scores": 0.92_62},
								        {"mask": {"hash": "abea804f0e", "shape": (4_80, 6_40)}, "scores": 0.89_99},
								        {"mask": {"hash": "7b9e8ddb73", "shape": (4_80, 6_40)}, "scores": 0.89_86},
								        {"mask": {"hash": "cd24047c8a", "shape": (4_80, 6_40)}, "scores": 0.89_84},
								        {"mask": {"hash": "6943e6bcbd", "shape": (4_80, 6_40)}, "scores": 0.88_73},
								        {"mask": {"hash": "b5f47c9191", "shape": (4_80, 6_40)}, "scores": 0.88_71}
								    ]				,		)
								# fmt: on







		@require_torch
		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     "facebook/sam-vit-huge"
								UpperCAmelCase         =     pipeline("mask-generation"				,		model=A__      )

								UpperCAmelCase         =     image_segmenter(
								    "http://images.cocodataset.org/val2017/000000039769.jpg"				,		pred_iou_thresh=1				,		points_per_batch=2_56      )

								# Shortening by hashing
								UpperCAmelCase         =     []
								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_80, 6_40)}, "scores": 1.04_44},
								        {"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.02_10},
								        {"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.01_67},
								        {"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.01_32},
								        {"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.00_53},
								    ]				,		)








import fire

from utils import calculate_rouge, save_json

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=None    ,   **lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     [x.strip() for x in open(lowerCamelCase_    ).readlines()]
						UpperCAmelCase         =     [x.strip() for x in open(lowerCamelCase_    ).readlines()][: len(lowerCamelCase_    )]
						UpperCAmelCase         =     calculate_rouge(lowerCamelCase_    ,   lowerCamelCase_    ,   **lowerCamelCase_    )
						if save_path is not None:
												save_json(lowerCamelCase_    ,   lowerCamelCase_    ,   indent=lowerCamelCase_    )
						return metrics  # these print nicely


if __name__ == "__main__":
		fire.Fire(calculate_rouge_path)



