

import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


a__				=		namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def    __UpperCAmelCase (	__a      :			Dict		,__a      :			List[Any]	)			->     Dict:

					"""simple docstring"""

					return (abs(source - target	) / target) < 0.01







@pytest.mark.integration
def    __UpperCAmelCase (	__a      :			Any	)			->     List[Any]:

					"""simple docstring"""

					_a	:    Any         =		_TestCommandArgs(dataset=__a		,all_configs=__a		,save_infos=__a	)
					_a	:    Union[str, Any]         =		TestCommand(*__a	)
					test_command.run()
					_a	:    str         =		os.path.join(__a		,'''README.md'''	)
					assert os.path.exists(__a	)
					_a	:    Any         =		DatasetInfosDict.from_directory(__a	)
					_a	:    int         =		DatasetInfosDict(
					    {
					        '''default''': DatasetInfo(
					            features=Features(
					                {
					                    '''tokens''': Sequence(Value('''string'''	)	),
					                    '''ner_tags''': Sequence(
					                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']	)	),
					                    '''langs''': Sequence(Value('''string'''	)	),
					                    '''spans''': Sequence(Value('''string'''	)	),
					                }	)		,splits=[
					                {
					                    '''name''': '''train''',
					                    '''num_bytes''': 2_351_563,
					                    '''num_examples''': 10_000,
					                },
					                {
					                    '''name''': '''validation''',
					                    '''num_bytes''': 238_418,
					                    '''num_examples''': 1_000,
					                },
					            ]		,download_size=3_940_680		,dataset_size=2_589_981		,)
					    }	)
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										_a      ,					_a	:    Dict         =		getattr(dataset_infos['''default''']		,__a	), getattr(expected_dataset_infos['''default''']		,__a	)
										if key == "num_bytes":
															assert is_apercent_close(__a		,__a	)
										elif key == "splits":
															assert list(__a	) == list(__a	)
															for split in result:
																				assert result[split].name == expected[split].name
																				assert result[split].num_examples == expected[split].num_examples
																				assert is_apercent_close(result[split].num_bytes		,expected[split].num_bytes	)
										else:
															result == expected




"""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 				snake_case       :




											"""simple docstring"""

											@staticmethod
											def 		__lowerCAmelCase						(				*lowerCamelCase__   :	List[Any]    ,**lowerCamelCase__   :	Union[str, Any]			):
															pass



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							hashlib.mda(image.tobytes()       )
				return m.hexdigest()[:1_0]



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							np.array(lowerCamelCase       )
				UpperCAmelCase__             =							npimg.shape
				return {"hash": hashimage(lowerCamelCase       ), "shape": shape}







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




					"""simple docstring"""

					snake_case__				    = dict(
					    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
					snake_case__				    = dict(
					    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							MaskGenerationPipeline(model=lowerCamelCase__    ,image_processor=lowerCamelCase__			)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	str			):
									pass







					@require_tf
					@unittest.skip('Image segmentation not implemented in TF'			)
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									pass







					@slow
					@require_torch
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							pipeline('mask-generation'    ,model='facebook/sam-vit-huge'			)

									UpperCAmelCase__             =							image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,points_per_batch=256			)

									# Shortening by hashing
									UpperCAmelCase__             =							[]
									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': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
									    ]    ,)
									# fmt: on







					@require_torch
					@slow
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							'facebook/sam-vit-huge'
									UpperCAmelCase__             =							pipeline('mask-generation'    ,model=lowerCamelCase__			)

									UpperCAmelCase__             =							image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'    ,pred_iou_thresh=1    ,points_per_batch=256			)

									# Shortening by hashing
									UpperCAmelCase__             =							[]
									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': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									    ]    ,)



