'''simple docstring'''

import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import SegformerImageProcessor





class 				lowercase    (    unittest.TestCase			):


   def __init__( self					:						List[str] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Optional[int]=7 ,		__lowerCAmelCase					:						Union[str, Any]=3 ,		__lowerCAmelCase					:						List[str]=30 ,		__lowerCAmelCase					:						Any=400 ,		__lowerCAmelCase					:						Dict=True ,		__lowerCAmelCase					:						List[str]=None ,		__lowerCAmelCase					:						Union[str, Any]=True ,		__lowerCAmelCase					:						Any=[0.5, 0.5, 0.5] ,		__lowerCAmelCase					:						List[Any]=[0.5, 0.5, 0.5] ,		__lowerCAmelCase					:						Optional[int]=False ,		)   ->       Tuple:
         lowercase_     			=   size if size is not None else {"height": 30, "width": 30}
         lowercase_     			=   parent
         lowercase_     			=   batch_size
         lowercase_     			=   num_channels
         lowercase_     			=   min_resolution
         lowercase_     			=   max_resolution
         lowercase_     			=   do_resize
         lowercase_     			=   size
         lowercase_     			=   do_normalize
         lowercase_     			=   image_mean
         lowercase_     			=   image_std
         lowercase_     			=   do_reduce_labels


   def __UpperCAmelCase							( self					:						Any)   ->       str:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_reduce_labels": self.do_reduce_labels,
         }




def __a			(						)						->				str:
      '''simple docstring'''


      lowercase_     			=   load_dataset("hf-internal-testing/fixtures_ade20k"			,		split="test"					)

      lowercase_     			=   Image.open(dataset[0]["file"]					)
      lowercase_     			=   Image.open(dataset[1]["file"]					)

      return image, map


def __a			(						)						->				Any:
      '''simple docstring'''


      lowercase_     			=   load_dataset("hf-internal-testing/fixtures_ade20k"			,		split="test"					)

      lowercase_     			=   Image.open(dataset[0]["file"]					)
      lowercase_     			=   Image.open(dataset[1]["file"]					)
      lowercase_     			=   Image.open(dataset[2]["file"]					)
      lowercase_     			=   Image.open(dataset[3]["file"]					)

      return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =SegformerImageProcessor if is_vision_available() else None


   def __UpperCAmelCase							( self					:						Any)   ->       Any:
         lowercase_     			=   SegformerImageProcessingTester(self)


   @property
   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       int:
         return self.image_processor_tester.prepare_image_processor_dict()


   def __UpperCAmelCase							( self					:						Any)   ->       Tuple:
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__lowerCAmelCase ,		"do_resize"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"size"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"do_normalize"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"image_mean"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"image_std"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"do_reduce_labels"))


   def __UpperCAmelCase							( self					:						Tuple)   ->       int:
         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size ,		{"height": 30, "width": 30})
         self.assertEqual(image_processor.do_reduce_labels ,		__lowerCAmelCase)

         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict ,		size=42 ,		reduce_labels=__lowerCAmelCase)
         self.assertEqual(image_processor.size ,		{"height": 42, "width": 42})
         self.assertEqual(image_processor.do_reduce_labels ,		__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Dict)   ->       Tuple:
         pass


   def __UpperCAmelCase							( self					:						Dict)   ->       Dict:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		Image.Image)

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)


   def __UpperCAmelCase							( self					:						List[str])   ->       Optional[int]:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase ,		numpify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		np.ndarray)

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)


   def __UpperCAmelCase							( self					:						str)   ->       Union[str, Any]:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase ,		torchify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		torch.Tensor)

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)


   def __UpperCAmelCase							( self					:						Dict)   ->       List[Any]:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase ,		torchify=__lowerCAmelCase)
         lowercase_     			=   []
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		torch.Tensor)
               maps.append(torch.zeros(image.shape[-2:]).long())

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		maps[0] ,		return_tensors="pt")
         self.assertEqual(
             encoding["pixel_values"].shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)
         self.assertEqual(
             encoding["labels"].shape ,		(
                 1,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)
         self.assertEqual(encoding["labels"].dtype ,		torch.long)
         self.assertTrue(encoding["labels"].min().item() >= 0)
         self.assertTrue(encoding["labels"].max().item() <= 255)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		__lowerCAmelCase ,		return_tensors="pt")
         self.assertEqual(
             encoding["pixel_values"].shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)
         self.assertEqual(
             encoding["labels"].shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)
         self.assertEqual(encoding["labels"].dtype ,		torch.long)
         self.assertTrue(encoding["labels"].min().item() >= 0)
         self.assertTrue(encoding["labels"].max().item() <= 255)

         # Test not batched input (PIL images)
         lowercase_			,				lowercase_     			=   prepare_semantic_single_inputs()

         lowercase_     			=   image_processing(__lowerCAmelCase ,		__lowerCAmelCase ,		return_tensors="pt")
         self.assertEqual(
             encoding["pixel_values"].shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)
         self.assertEqual(
             encoding["labels"].shape ,		(
                 1,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)
         self.assertEqual(encoding["labels"].dtype ,		torch.long)
         self.assertTrue(encoding["labels"].min().item() >= 0)
         self.assertTrue(encoding["labels"].max().item() <= 255)

         # Test batched input (PIL images)
         lowercase_			,				lowercase_     			=   prepare_semantic_batch_inputs()

         lowercase_     			=   image_processing(__lowerCAmelCase ,		__lowerCAmelCase ,		return_tensors="pt")
         self.assertEqual(
             encoding["pixel_values"].shape ,		(
                 2,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)
         self.assertEqual(
             encoding["labels"].shape ,		(
                 2,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             ) ,		)
         self.assertEqual(encoding["labels"].dtype ,		torch.long)
         self.assertTrue(encoding["labels"].min().item() >= 0)
         self.assertTrue(encoding["labels"].max().item() <= 255)


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Any:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)

         # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
         lowercase_			,				lowercase_     			=   prepare_semantic_single_inputs()
         lowercase_     			=   image_processing(__lowerCAmelCase ,		__lowerCAmelCase ,		return_tensors="pt")
         self.assertTrue(encoding["labels"].min().item() >= 0)
         self.assertTrue(encoding["labels"].max().item() <= 150)

         lowercase_     			=   True
         lowercase_     			=   image_processing(__lowerCAmelCase ,		__lowerCAmelCase ,		return_tensors="pt")
         self.assertTrue(encoding["labels"].min().item() >= 0)
         self.assertTrue(encoding["labels"].max().item() <= 255)







'''simple docstring'''

from hashlib import mda, shaaaa

lowerCAmelCase_			:			Union[str, Any]    	=   (shaaaa, mda)





class 				lowercase    :


   def __init__( self					:						Optional[Any] ,		__lowerCAmelCase					:						int = 8)   ->       None:
         lowercase_     			=   0B0
         lowercase_     			=   size


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						str)   ->       None:
         lowercase_     			=   self.hash_(__lowerCAmelCase)
         self.bitarray |= h


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						str)   ->       bool:
         lowercase_     			=   self.hash_(__lowerCAmelCase)
         return (h & self.bitarray) == h


   def __contains__( self					:						str ,		__lowerCAmelCase					:						str)   ->       bool:
         return self.exists(__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						int)   ->       str:
         lowercase_     			=   bin(__lowerCAmelCase)[2:]
         return res.zfill(self.size)


   @property
   def __UpperCAmelCase							( self					:						Dict)   ->       str:
         return self.format_bin(self.bitarray)


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						str)   ->       int:
         lowercase_     			=   0B0
         for func in HASH_FUNCTIONS:
               lowercase_     			=   (
                   int.from_bytes(func(value.encode()).digest() ,		"little") % self.size
               )
               res |= 2**position
         return res


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						str)   ->       str:
         return self.format_bin(self.hash_(__lowerCAmelCase))


   @property
   def __UpperCAmelCase							( self					:						str)   ->       float:
         lowercase_     			=   bin(self.bitarray).count("1")
         return (n_ones / self.size) ** len(__lowerCAmelCase)






