
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 						lowerCamelCase_				:







									@staticmethod
									def 			A	(						*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
													"""simple docstring"""
													pass

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      hashlib.mda(image.tobytes() )
				return m.hexdigest()[:1_0]

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:int        =      np.array(snake_case )
				__magic_name__		:Optional[Any]        =      npimg.shape
				return {"hash": hashimage(snake_case ), "shape": shape}

@is_pipeline_test
@require_vision
@require_torch
class 						lowerCamelCase_				(      unittest.TestCase ):
				a__					       =  dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
				a__					       =  dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      MaskGenerationPipeline(model=__lowerCAmelCase       ,					image_processor=__lowerCAmelCase     )
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								pass







				@require_tf
				@unittest.skip('''Image segmentation not implemented in TF'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				@slow
				@require_torch
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      pipeline('''mask-generation'''       ,					model='''facebook/sam-vit-huge'''     )

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

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

								# fmt: off
								self.assertEqual(
								    nested_simplify(__lowerCAmelCase       ,					decimals=4     )       ,					[
								        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0444},
								        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.021},
								        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0167},
								        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0132},
								        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0053},
								        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9967},
								        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.993},
								        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9909},
								        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9879},
								        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9834},
								        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9716},
								        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9612},
								        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9599},
								        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9552},
								        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9532},
								        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9516},
								        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9499},
								        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9483},
								        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9464},
								        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.943},
								        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.943},
								        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9408},
								        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9335},
								        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9326},
								        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9262},
								        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8999},
								        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8986},
								        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8984},
								        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8873},
								        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8871}
								    ]       ,					)
								# fmt: on







				@require_torch
				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      '''facebook/sam-vit-huge'''
								__magic_name__		:Tuple        =      pipeline('''mask-generation'''       ,					model=__lowerCAmelCase     )

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

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

								self.assertEqual(
								    nested_simplify(__lowerCAmelCase       ,					decimals=4     )       ,					[
								        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0444},
								        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0210},
								        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0167},
								        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0132},
								        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0053},
								    ]       ,					)














import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      1_0







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      [1, 2, 3, 4]
								__magic_name__		:Union[str, Any]        =      [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								__magic_name__		:Optional[Any]        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
								__magic_name__		:str        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      '''It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this.'''
								__magic_name__     ,				__magic_name__		:Optional[int]        =      process_story(__lowerCAmelCase     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      ''''''
								__magic_name__     ,				__magic_name__		:Optional[int]        =      process_story(__lowerCAmelCase     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      (
								    '''It was the year of Our Lord one thousand seven hundred and '''
								    '''seventy-five\n\nSpiritual revelations were conceded to England '''
								    '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
								)
								__magic_name__     ,				__magic_name__		:str        =      process_story(__lowerCAmelCase     )

								__magic_name__		:Optional[int]        =      [
								    '''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
								    '''Spiritual revelations were conceded to England at that favoured period, as at this.''',
								]
								self.assertEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )

								__magic_name__		:Union[str, Any]        =      ['''It was the best of times.''']
								self.assertEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      torch.tensor([1, 2, 3, 4]     )
								__magic_name__		:List[str]        =      torch.tensor([1, 1, 1, 1]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					0     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]     )
								__magic_name__		:Union[str, Any]        =      torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					2_3     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      torch.tensor([8, 2, 3, 4, 1, 1, 1]     )
								__magic_name__		:Dict        =      torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					1     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      1_0_1
								__magic_name__		:List[Any]        =      torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]     )
								__magic_name__		:str        =      torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]     )

								__magic_name__		:Tuple        =      compute_token_type_ids(__lowerCAmelCase       ,					__lowerCAmelCase     )
								np.testing.assert_array_equal(__lowerCAmelCase       ,					__lowerCAmelCase     )






