



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  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()[:10]
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					Dict:
   UpperCamelCase   :  Union[str, Any]						     =     np.array(snake_case__     )
   UpperCamelCase   :  Dict						     =     npimg.shape
   return {"hash": hashimage(snake_case__     ), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Dict       					=  dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
       UpperCAmelCase__    : Union[str, Any]       					=  dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          UpperCamelCase   :  Dict						     =     MaskGenerationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          pass
       @require_tf
       @unittest.skip('Image segmentation not implemented in TF'	)
       def  snake_case_	(      self	)					->		Dict:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Any						     =     pipeline('mask-generation',      model='facebook/sam-vit-huge'	)

          UpperCamelCase   :  List[Any]						     =     image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg',      points_per_batch=256	)

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

          # fmt: off
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_,      decimals=4	),      [
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_21},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.00_53},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.99_67},
                  {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_93},
                  {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.99_09},
                  {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.98_79},
                  {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.98_34},
                  {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.97_16},
                  {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.96_12},
                  {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.95_99},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.95_52},
                  {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.95_32},
                  {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.95_16},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.94_99},
                  {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.94_83},
                  {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.94_64},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_43},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_43},
                  {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.94_08},
                  {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.93_35},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.93_26},
                  {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.92_62},
                  {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.89_99},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.89_86},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.89_84},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.88_73},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.88_71}
              ],      )
          # fmt: on






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

          UpperCamelCase   :  List[str]						     =     image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg',      pred_iou_thresh=1,      points_per_batch=256	)

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

          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_,      decimals=4	),      [
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.02_10},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.00_53},
              ],      )








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = "arrow",      **SCREAMING_SNAKE_CASE_,      )					->		int:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      keep_in_memory=SCREAMING_SNAKE_CASE_,      streaming=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  Any						     =     load_from_cache_file
          UpperCamelCase   :  Any						     =     file_format
          UpperCamelCase   :  str						     =     Spark(
              df=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      working_dir=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )






       def  snake_case_	(      self	)					->		Tuple:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split	)
          UpperCamelCase   :  Dict						     =     None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,      file_format=self._file_format,      )
          return self.builder.as_dataset(split=self.split	)



