






"""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 __a ( unittest.TestCase					):



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



  def     A_					(self	:		Optional[int] )		->				int:
   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			(  )	->		int:
 __A						:							Optional[int]      =						load_dataset('hf-internal-testing/fixtures_ade20k'  ,						split='test'		)

 __A						:							Union[str, Any]      =						Image.open(dataset[0]['file']		)
 __A						:							Dict      =						Image.open(dataset[1]['file']		)

 return image, map




def 					a			(  )	->		Union[str, Any]:
 __A						:							Optional[int]      =						load_dataset('hf-internal-testing/fixtures_ade20k'  ,						split='test'		)

 __A						:							Union[str, Any]      =						Image.open(dataset[0]['file']		)
 __A						:							List[str]      =						Image.open(dataset[1]['file']		)
 __A						:							Union[str, Any]      =						Image.open(dataset[2]['file']		)
 __A						:							Optional[int]      =						Image.open(dataset[3]['file']		)

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






@require_torch
@require_vision
class __a ( snake_case_			,unittest.TestCase					):
  __lowercase     :     List[Any]       						=       SegformerImageProcessor if is_vision_available() else None



  def     A_					(self	:		str )		->				Union[str, Any]:
   __A						:							Dict      =						SegformerImageProcessingTester(self )



  @property
  def     A_					(self	:		List[Any] )		->				Optional[Any]:
   return self.image_processor_tester.prepare_image_processor_dict()



  def     A_					(self	:		Any )		->				List[Any]:
   __A						:							Dict      =						self.image_processing_class(**self.image_processor_dict )
   self.assertTrue(hasattr(lowercase_					,      'do_resize' ) )
   self.assertTrue(hasattr(lowercase_					,      'size' ) )
   self.assertTrue(hasattr(lowercase_					,      'do_normalize' ) )
   self.assertTrue(hasattr(lowercase_					,      'image_mean' ) )
   self.assertTrue(hasattr(lowercase_					,      'image_std' ) )
   self.assertTrue(hasattr(lowercase_					,      'do_reduce_labels' ) )



  def     A_					(self	:		List[str] )		->				List[Any]:
   __A						:							str      =						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					,      lowercase_ )

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



  def     A_					(self	:		Union[str, Any] )		->				Tuple:
   pass



  def     A_					(self	:		Optional[int] )		->				str:
   # Initialize image_processing
   __A						:							Any      =						self.image_processing_class(**self.image_processor_dict )
   # create random PIL images
   __A						:							Optional[int]      =						prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_ )
   for image in image_inputs:
    self.assertIsInstance(lowercase_					,      Image.Image )

   # Test not batched input
   __A						:							Optional[int]      =						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
   __A						:							Any      =						image_processing(lowercase_					,      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     A_					(self	:		List[Any] )		->				Dict:
   # Initialize image_processing
   __A						:							Union[str, Any]      =						self.image_processing_class(**self.image_processor_dict )
   # create random numpy tensors
   __A						:							List[Any]      =						prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      numpify=lowercase_ )
   for image in image_inputs:
    self.assertIsInstance(lowercase_					,      np.ndarray )

   # Test not batched input
   __A						:							Optional[int]      =						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
   __A						:							Optional[Any]      =						image_processing(lowercase_					,      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     A_					(self	:		int )		->				str:
   # Initialize image_processing
   __A						:							List[str]      =						self.image_processing_class(**self.image_processor_dict )
   # create random PyTorch tensors
   __A						:							Optional[int]      =						prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_ )
   for image in image_inputs:
    self.assertIsInstance(lowercase_					,      torch.Tensor )

   # Test not batched input
   __A						:							int      =						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
   __A						:							Optional[int]      =						image_processing(lowercase_					,      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     A_					(self	:		Any )		->				Any:
   # Initialize image_processing
   __A						:							Optional[int]      =						self.image_processing_class(**self.image_processor_dict )
   # create random PyTorch tensors
   __A						:							Dict      =						prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_ )
   __A						:							int      =						[]
   for image in image_inputs:
    self.assertIsInstance(lowercase_					,      torch.Tensor )
    maps.append(torch.zeros(image.shape[-2:] ).long() )

   # Test not batched input
   __A						:							str      =						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
   __A						:							List[str]      =						image_processing(lowercase_					,      lowercase_					,      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)
   __A     ,       __A						:							Dict      =						prepare_semantic_single_inputs()

   __A						:							Optional[int]      =						image_processing(lowercase_					,      lowercase_					,      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)
   __A     ,       __A						:							Dict      =						prepare_semantic_batch_inputs()

   __A						:							Optional[Any]      =						image_processing(lowercase_					,      lowercase_					,      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     A_					(self	:		Optional[int] )		->				Optional[Any]:
   # Initialize image_processing
   __A						:							int      =						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
   __A     ,       __A						:							Union[str, Any]      =						prepare_semantic_single_inputs()
   __A						:							Tuple      =						image_processing(lowercase_					,      lowercase_					,      return_tensors='pt' )
   self.assertTrue(encoding['labels'].min().item() >= 0 )
   self.assertTrue(encoding['labels'].max().item() <= 150 )

   __A						:							Tuple      =						True
   __A						:							Dict      =						image_processing(lowercase_					,      lowercase_					,      return_tensors='pt' )
   self.assertTrue(encoding['labels'].min().item() >= 0 )
   self.assertTrue(encoding['labels'].max().item() <= 255 )













"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(inputs > threshold).type(inputs.type() )
   if mask.sum() < nb_min:
    # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							List[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Tuple      =						1
   return mask





