






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       snake_case_   ( *_lowerCAmelCase     ,						**_lowerCAmelCase		):



           '''simple docstring'''
           pass


def 					snake_case__				(				UpperCAmelCase				:	Image							):
    lowerCAmelCase__ :Optional[Any]   				=					hashlib.mda(image.tobytes()							)
    return m.hexdigest()[:1_0]


def 					snake_case__				(				UpperCAmelCase				:	Image							):
    lowerCAmelCase__ :Dict   				=					np.array(lowercase__							)
    lowerCAmelCase__ :Dict   				=					npimg.shape
    return {"hash": hashimage(lowercase__							), "shape": shape}







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




      """simple docstring"""



      A    =     dict(
          (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if MODEL_FOR_MASK_GENERATION_MAPPING else [])		)
      A    =     dict(
          (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])		)
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__ :Optional[int]   				=					MaskGenerationPipeline(model=__lowercase     ,						image_processor=__lowercase		)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase		):



          '''simple docstring'''
          pass
      @require_tf
      @unittest.skip("Image segmentation not implemented in TF"		)
      def       snake_case_   ( self		):



          '''simple docstring'''
          pass
      @slow
      @require_torch
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :List[Any]   				=					pipeline("mask-generation"     ,						model="facebook/sam-vit-huge"		)

          lowerCAmelCase__ :List[str]   				=					image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"     ,						points_per_batch=256		)

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

          # fmt: off
          self.assertEqual(
              nested_simplify(__lowercase     ,						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       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :int   				=					"facebook/sam-vit-huge"
          lowerCAmelCase__ :List[str]   				=					pipeline("mask-generation"     ,						model=__lowercase		)

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

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

          self.assertEqual(
              nested_simplify(__lowercase     ,						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},
              ]     ,						)






'''simple docstring'''

def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





     if upper_limit < 0:
          raise ValueError('Limit for the Catalan sequence must be ≥ 0'			)

     __lowercase      		=[0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     __lowercase      		=1
     if upper_limit > 0:
          __lowercase      		=1

     # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
     for i in range(2,       upper_limit + 1			):
          for j in range(lowercase__			):
               catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

     return catalan_list


if __name__ == "__main__":
       print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
       print('''\n*** Enter -1 at any time to quit ***''')
       print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
       try:
              while True:
                     UpperCAmelCase					 =	int(input().strip())
                     if N < 0:
                            print('''\n********* Goodbye!! ************''')
                            break
                     else:
                            print(F'''The Catalan numbers from 0 through {N} are:''')
                            print(catalan_numbers(N))
                            print('''Try another upper limit for the sequence: ''', end='''''')
       except (NameError, ValueError):
              print('''\n********* Invalid input, goodbye! ************\n''')

       import doctest

       doctest.testmod()


