






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 			__a  :







  @staticmethod
  def 					__lowercase					(		*SCREAMING_SNAKE_CASE					:   Optional[Any]							,   **SCREAMING_SNAKE_CASE					:   Any						):


       '''simple docstring'''






       pass







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Union[str, Any]       				=  hashlib.mda(image.tobytes()			)
     return m.hexdigest()[:10]







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





@is_pipeline_test
@require_vision
@require_torch
class 			__a  (    unittest.TestCase       ):
 _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					:   str							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  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 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      pass







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


      '''simple docstring'''






      pass







 @slow
 @require_torch
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






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

      UpperCamelCase__			:   Optional[Any]       				=  image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"							,   points_per_batch=2_56						)

      # Shortening by hashing
      UpperCamelCase__			:   str       				=  []
      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": (4_80, 6_40)}, "scores": 1.0_4_4_4},
              {"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0_2_1},
              {"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0_1_6_7},
              {"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0_1_3_2},
              {"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0_0_5_3},
              {"mask": {"hash": "e2d0b7a0b7", "shape": (4_80, 6_40)}, "scores": 0.9_9_6_7},
              {"mask": {"hash": "453c7844bd", "shape": (4_80, 6_40)}, "scores": 0.9_9_3},
              {"mask": {"hash": "3d44f2926d", "shape": (4_80, 6_40)}, "scores": 0.9_9_0_9},
              {"mask": {"hash": "64033ddc3f", "shape": (4_80, 6_40)}, "scores": 0.9_8_7_9},
              {"mask": {"hash": "801064ff79", "shape": (4_80, 6_40)}, "scores": 0.9_8_3_4},
              {"mask": {"hash": "6172f276ef", "shape": (4_80, 6_40)}, "scores": 0.9_7_1_6},
              {"mask": {"hash": "b49e60e084", "shape": (4_80, 6_40)}, "scores": 0.9_6_1_2},
              {"mask": {"hash": "a811e775fd", "shape": (4_80, 6_40)}, "scores": 0.9_5_9_9},
              {"mask": {"hash": "a6a8ebcf4b", "shape": (4_80, 6_40)}, "scores": 0.9_5_5_2},
              {"mask": {"hash": "9d8257e080", "shape": (4_80, 6_40)}, "scores": 0.9_5_3_2},
              {"mask": {"hash": "32de6454a8", "shape": (4_80, 6_40)}, "scores": 0.9_5_1_6},
              {"mask": {"hash": "af3d4af2c8", "shape": (4_80, 6_40)}, "scores": 0.9_4_9_9},
              {"mask": {"hash": "3c6db475fb", "shape": (4_80, 6_40)}, "scores": 0.9_4_8_3},
              {"mask": {"hash": "c290813fb9", "shape": (4_80, 6_40)}, "scores": 0.9_4_6_4},
              {"mask": {"hash": "b6f0b8f606", "shape": (4_80, 6_40)}, "scores": 0.9_4_3},
              {"mask": {"hash": "92ce16bfdf", "shape": (4_80, 6_40)}, "scores": 0.9_4_3},
              {"mask": {"hash": "c749b25868", "shape": (4_80, 6_40)}, "scores": 0.9_4_0_8},
              {"mask": {"hash": "efb6cab859", "shape": (4_80, 6_40)}, "scores": 0.9_3_3_5},
              {"mask": {"hash": "1ff2eafb30", "shape": (4_80, 6_40)}, "scores": 0.9_3_2_6},
              {"mask": {"hash": "788b798e24", "shape": (4_80, 6_40)}, "scores": 0.9_2_6_2},
              {"mask": {"hash": "abea804f0e", "shape": (4_80, 6_40)}, "scores": 0.8_9_9_9},
              {"mask": {"hash": "7b9e8ddb73", "shape": (4_80, 6_40)}, "scores": 0.8_9_8_6},
              {"mask": {"hash": "cd24047c8a", "shape": (4_80, 6_40)}, "scores": 0.8_9_8_4},
              {"mask": {"hash": "6943e6bcbd", "shape": (4_80, 6_40)}, "scores": 0.8_8_7_3},
              {"mask": {"hash": "b5f47c9191", "shape": (4_80, 6_40)}, "scores": 0.8_8_7_1}
          ]							,   )
      # fmt: on







 @require_torch
 @slow
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






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

      UpperCamelCase__			:   Tuple       				=  image_segmenter(
          "http://images.cocodataset.org/val2017/000000039769.jpg"							,   pred_iou_thresh=1							,   points_per_batch=2_56						)

      # Shortening by hashing
      UpperCamelCase__			:   str       				=  []
      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": (4_80, 6_40)}, "scores": 1.0_4_4_4},
              {"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0_2_1_0},
              {"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0_1_6_7},
              {"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0_1_3_2},
              {"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0_0_5_3},
          ]							,   )







from math import factorial





class 			__a  :







 def __init__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  real
      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Union[str, Any]       				=  [1] * rank
      else:
           UpperCamelCase__			:   int       				=  rank







 def __repr__(		self					:   Tuple						):


      '''simple docstring'''






      return (
          F'{self.real}+'
          F'{"+".join(str(SCREAMING_SNAKE_CASE						)+"E"+str(n+1						)for n,dual in enumerate(self.duals						)						)}'
      )







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.duals.copy()
      while cur[-1] == 0:
           cur.pop(-1						)
      return Dual(self.real							,   SCREAMING_SNAKE_CASE						)







 def __add__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           return Dual(self.real + other							,   self.duals						)
      UpperCamelCase__			:   Optional[int]       				=  self.duals.copy()
      UpperCamelCase__			:   Any       				=  other.duals.copy()
      if len(SCREAMING_SNAKE_CASE						) > len(SCREAMING_SNAKE_CASE						):
           o_dual.extend([1] * (len(SCREAMING_SNAKE_CASE						) - len(SCREAMING_SNAKE_CASE						))						)
      elif len(SCREAMING_SNAKE_CASE						) < len(SCREAMING_SNAKE_CASE						):
           s_dual.extend([1] * (len(SCREAMING_SNAKE_CASE						) - len(SCREAMING_SNAKE_CASE						))						)
      UpperCamelCase__			:   Optional[int]       				=  []
      for i in range(len(SCREAMING_SNAKE_CASE						)						):
           new_duals.append(s_dual[i] + o_dual[i]						)
      return Dual(self.real + other.real							,   SCREAMING_SNAKE_CASE						)

 _lowerCAmelCase       :       Dict        = __add__







 def __sub__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      return self + other * -1







 def __mul__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i * other						)
           return Dual(self.real * other							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  [0] * (len(self.duals						) + len(other.duals						) + 1)
      for i, item in enumerate(self.duals						):
           for j, jtem in enumerate(other.duals						):
                new_duals[i + j + 1] += item * jtem
      for k in range(len(self.duals						)						):
           new_duals[k] += self.duals[k] * other.real
      for index in range(len(other.duals						)						):
           new_duals[index] += other.duals[index] * self.real
      return Dual(self.real * other.real							,   SCREAMING_SNAKE_CASE						)

 _lowerCAmelCase       :       Union[str, Any]        = __mul__







 def __truediv__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i / other						)
           return Dual(self.real / other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __floordiv__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Dict       				=  []
           for i in self.duals:
                new_duals.append(i // other						)
           return Dual(self.real // other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __pow__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      if n < 0 or isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           raise ValueError("power must be a positive integer"						)
      if n == 0:
           return 1
      if n == 1:
           return self
      UpperCamelCase__			:   str       				=  self
      for _ in range(n - 1						):
           x *= self
      return x







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	str:
     if not callable(__lowerCAmelCase			):
          raise ValueError("differentiate() requires a function as input for func"			)
     if not isinstance(__lowerCAmelCase					,    (float, int)			):
          raise ValueError("differentiate() requires a float as input for position"			)
     if not isinstance(__lowerCAmelCase					,    __lowerCAmelCase			):
          raise ValueError("differentiate() requires an int as input for order"			)
     UpperCamelCase__			:   Optional[Any]       				=  Dual(__lowerCAmelCase					,    1			)
     UpperCamelCase__			:   Any       				=  func(__lowerCAmelCase			)
     if order == 0:
          return result.real
     return result.duals[order - 1] * factorial(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()







 def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[Any]:
      return y**2 * y**4




 print(differentiate(f, 9, 2))