





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 ( *snake_case					,			**snake_case      )				->  int:
        """simple docstring"""





        pass






def       _A  (      lowerCamelCase   ):
   a__	:						int      		=  hashlib.mda(image.tobytes()   )
   return m.hexdigest()[:10]






def       _A  (      lowerCamelCase   ):
   a__	:						int      		=  np.array(lowerCamelCase   )
   a__	:						List[str]      		=  npimg.shape
   return {"hash": hashimage(lowerCamelCase   ), "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 :						Dict     		=				dict(
       (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()  ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])  )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Dict      		=  MaskGenerationPipeline(model=snake_case					,			image_processor=snake_case      )
      return image_segmenter, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  List[str]:
      """simple docstring"""





      pass





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





      pass





   @slow
   @require_torch
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  pipeline("mask-generation"					,			model="facebook/sam-vit-huge"      )

      a__	:						Dict      		=  image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"					,			points_per_batch=256      )

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

      # fmt: off
      self.assertEqual(
          nested_simplify(snake_case					,			decimals=4      )					,			[
              {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_444},
              {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.021},
              {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_167},
              {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_132},
              {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_053},
              {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_967},
              {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.993},
              {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_909},
              {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_879},
              {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_834},
              {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_716},
              {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_612},
              {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_599},
              {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_552},
              {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_532},
              {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_516},
              {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_499},
              {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_483},
              {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_464},
              {"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": 0.9_408},
              {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_335},
              {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_326},
              {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_262},
              {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_999},
              {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_986},
              {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_984},
              {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_873},
              {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_871}
          ]					,			)
      # fmt: on





   @require_torch
   @slow
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Any      		=  "facebook/sam-vit-huge"
      a__	:						List[Any]      		=  pipeline("mask-generation"					,			model=snake_case      )

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

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

      self.assertEqual(
          nested_simplify(snake_case					,			decimals=4      )					,			[
              {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_444},
              {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_210},
              {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_167},
              {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_132},
              {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_053},
          ]					,			)








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       :			Dict			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[int]     		=				"""mobilenet_v2"""





   def __init__( self					,			snake_case=3					,			snake_case=224					,			snake_case=1.0					,			snake_case=8					,			snake_case=8					,			snake_case=6					,			snake_case=32					,			snake_case=True					,			snake_case=True					,			snake_case="relu6"					,			snake_case=True					,			snake_case=0.8					,			snake_case=0.02					,			snake_case=0.001					,			snake_case=255					,			**snake_case					,			)				->  int:
      """simple docstring"""





      super().__init__(**snake_case      )

      if depth_multiplier <= 0:
         raise ValueError("depth_multiplier must be greater than zero."      )

      a__	:						str      		=  num_channels
      a__	:						Dict      		=  image_size
      a__	:						Any      		=  depth_multiplier
      a__	:						str      		=  depth_divisible_by
      a__	:						Optional[int]      		=  min_depth
      a__	:						Dict      		=  expand_ratio
      a__	:						str      		=  output_stride
      a__	:						Optional[int]      		=  first_layer_is_expansion
      a__	:						Union[str, Any]      		=  finegrained_output
      a__	:						Union[str, Any]      		=  hidden_act
      a__	:						str      		=  tf_padding
      a__	:						List[Any]      		=  classifier_dropout_prob
      a__	:						List[Any]      		=  initializer_range
      a__	:						Optional[Any]      		=  layer_norm_eps
      a__	:						str      		=  semantic_loss_ignore_index







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Any     		=				version.parse("""1.11"""  )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      return OrderedDict([("pixel_values", {0: "batch"})]      )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      if self.task == "image-classification":
         return OrderedDict([("logits", {0: "batch"})]      )
      else:
         return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]      )





   @property
   def 	_snake_case ( self      )				->  float:
      """simple docstring"""





      return 1E-4

