




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 		UpperCamelCase__	:

           '''simple docstring'''



           @staticmethod
           def     _lowercase     (    *UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Optional[Any]:
                  pass

def      A      (      _SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      Union[str, Any]			 =					hashlib.mda(image.tobytes()    )
       return m.hexdigest()[:10]

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Dict:
       lowerCamelCase   :      List[str]			 =					np.array(__lowerCAmelCase    )
       lowerCamelCase   :      Optional[Any]			 =					npimg.shape
       return {"hash": hashimage(__lowerCAmelCase    ), "shape": shape}




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

    '''simple docstring'''



    lowerCamelCase_      :	Tuple	     =      dict(
        (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])     )
    lowerCamelCase_      :	str	     =      dict(
        (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[int]:
           lowerCamelCase   :      List[Any]			 =					MaskGenerationPipeline(model=UpperCamelCase__     ,  image_processor=UpperCamelCase__     )
           return image_segmenter, [
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
           ]







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Tuple:
           pass







    @require_tf
    @unittest.skip("Image segmentation not implemented in TF"     )
    def     _lowercase     (    self     )     ->      str:
           pass







    @slow
    @require_torch
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      Optional[Any]			 =					pipeline("mask-generation"     ,  model="facebook/sam-vit-huge"     )

           lowerCamelCase   :      Optional[Any]			 =					image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"     ,  points_per_batch=256     )

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

           # fmt: off
           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=4     )     ,  [
                   {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0444},
                   {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.021},
                   {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0167},
                   {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0132},
                   {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0053},
                   {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9967},
                   {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.993},
                   {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9909},
                   {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9879},
                   {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9834},
                   {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9716},
                   {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9612},
                   {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9599},
                   {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9552},
                   {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9532},
                   {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9516},
                   {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9499},
                   {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9483},
                   {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9464},
                   {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.943},
                   {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.943},
                   {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9408},
                   {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9335},
                   {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9326},
                   {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9262},
                   {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8999},
                   {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8986},
                   {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8984},
                   {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8873},
                   {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8871}
               ]     ,  )
           # fmt: on







    @require_torch
    @slow
    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      Optional[int]			 =					"facebook/sam-vit-huge"
           lowerCamelCase   :      Dict			 =					pipeline("mask-generation"     ,  model=UpperCamelCase__     )

           lowerCamelCase   :      Tuple			 =					image_segmenter(
               "http://images.cocodataset.org/val2017/000000039769.jpg"     ,  pred_iou_thresh=1     ,  points_per_batch=256     )

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

           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=4     )     ,  [
                   {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0444},
                   {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0210},
                   {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0167},
                   {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0132},
                   {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0053},
               ]     ,  )














import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
 import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class 			__a  (    unittest.TestCase       ,   A__       ):







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  load_tool("text-to-speech"						)
      self.tool.setup()







 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   Union[str, Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   Any       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   int       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)