
"""simple docstring"""

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 						UpperCAmelCase (     *__UpperCAmelCase     ,					**__UpperCAmelCase				):

            '''simple docstring'''




            pass






def 							A						(    snake_case				:Image		)				->     str:
 __UpperCamelCase      =	hashlib.mda(image.tobytes()		)
 return m.hexdigest()[:1_0]






def 							A						(    snake_case				:Image		)				->     Dict:
 __UpperCamelCase      =	np.array(snake_case		)
 __UpperCamelCase      =	npimg.shape
 return {"hash": hashimage(snake_case		), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class __lowerCAmelCase		(				unittest.TestCase      ):
     lowercase						       =						dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
     lowercase						       =						dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	MaskGenerationPipeline(model=__UpperCAmelCase     ,					image_processor=__UpperCAmelCase				)
      return image_segmenter, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      pass





     @require_tf
     @unittest.skip('Image segmentation not implemented in TF'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @slow
     @require_torch
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	pipeline('mask-generation'     ,					model='facebook/sam-vit-huge'				)

      __UpperCamelCase      =	image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'     ,					points_per_batch=256				)

      # Shortening by hashing
      __UpperCamelCase      =	[]
      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': 1.0_4_4_4},
              {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
              {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
              {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
              {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
              {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
              {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
              {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
              {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
              {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
              {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
              {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
              {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
              {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
              {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
              {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
              {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
              {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
              {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
              {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
              {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
              {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
              {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
              {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
              {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
              {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
              {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
              {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
              {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
              {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
          ]     ,					)
      # fmt: on







     @require_torch
     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'facebook/sam-vit-huge'
      __UpperCamelCase      =	pipeline('mask-generation'     ,					model=__UpperCAmelCase				)

      __UpperCamelCase      =	image_segmenter(
          'http://images.cocodataset.org/val2017/000000039769.jpg'     ,					pred_iou_thresh=1     ,					points_per_batch=256				)

      # Shortening by hashing
      __UpperCamelCase      =	[]
      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': 1.0_4_4_4},
              {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
              {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
              {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
              {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
          ]     ,					)





"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


UpperCamelCase     :    Optional[int]       =			logging.get_logger(__name__)

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'     ,					__UpperCAmelCase     ,					)
      super().__init__(*__UpperCAmelCase     ,					**__UpperCAmelCase				)



