





"""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       snake_case		(       unittest.TestCase       ):

      def __init__(      self       :Tuple						,							_lowerCamelCase       :Any						,							_lowerCamelCase       :List[Any]=7						,							_lowerCamelCase       :Optional[int]=3						,							_lowerCamelCase       :Any=3_0						,							_lowerCamelCase       :List[str]=4_0_0						,							_lowerCamelCase       :List[str]=True						,							_lowerCamelCase       :str=None						,							_lowerCamelCase       :Optional[int]=True						,							_lowerCamelCase       :List[str]=[0.5, 0.5, 0.5]						,							_lowerCamelCase       :Dict=[0.5, 0.5, 0.5]						,							_lowerCamelCase       :Any=False						,							):
       __SCREAMING_SNAKE_CASE			:      int 							=	size if size is not None else {'''height''': 3_0, '''width''': 3_0}
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	parent
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	batch_size
       __SCREAMING_SNAKE_CASE			:      Dict 							=	num_channels
       __SCREAMING_SNAKE_CASE			:      Any 							=	min_resolution
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	max_resolution
       __SCREAMING_SNAKE_CASE			:      Any 							=	do_resize
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	size
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	do_normalize
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_mean
       __SCREAMING_SNAKE_CASE			:      Any 							=	image_std
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	do_reduce_labels



      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       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 		lowerCAmelCase_      (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Dict 							=	load_dataset('''hf-internal-testing/fixtures_ade20k'''			,				split='''test'''		)

 __SCREAMING_SNAKE_CASE			:      Any 							=	Image.open(dataset[0]['''file''']		)
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	Image.open(dataset[1]['''file''']		)

 return image, map




def 		lowerCAmelCase_      (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	load_dataset('''hf-internal-testing/fixtures_ade20k'''			,				split='''test'''		)

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

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






@require_torch
@require_vision
class       snake_case		(       lowercase__			,					unittest.TestCase       ):
      lowerCamelCase__      =			SegformerImageProcessor if is_vision_available() else None

      def 						SCREAMING_SNAKE_CASE_      (      self       :str						):
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	SegformerImageProcessingTester(self						)

      @property
      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       return self.image_processor_tester.prepare_image_processor_dict()

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	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 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       __SCREAMING_SNAKE_CASE			:      str 							=	self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size						,							{'''height''': 3_0, '''width''': 3_0}						)
       self.assertEqual(image_processor.do_reduce_labels						,							lowerCAmelCase__						)

       __SCREAMING_SNAKE_CASE			:      str 							=	self.image_processing_class.from_dict(self.image_processor_dict						,							size=4_2						,							reduce_labels=lowerCAmelCase__						)
       self.assertEqual(image_processor.size						,							{'''height''': 4_2, '''width''': 4_2}						)
       self.assertEqual(image_processor.do_reduce_labels						,							lowerCAmelCase__						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       pass

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       __SCREAMING_SNAKE_CASE			:      str 							=	prepare_image_inputs(self.image_processor_tester						,							equal_resolution=lowerCAmelCase__						)
       for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase__						,							Image.Image						)

       # Test not batched input
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	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
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	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 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       __SCREAMING_SNAKE_CASE			:      Any 							=	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
       __SCREAMING_SNAKE_CASE			:      Dict 							=	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
       __SCREAMING_SNAKE_CASE			:      Any 							=	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 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	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
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	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
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	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 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE			:      Any 							=	prepare_image_inputs(self.image_processor_tester						,							equal_resolution=lowerCAmelCase__						,							torchify=lowerCAmelCase__						)
       __SCREAMING_SNAKE_CASE			:      str 							=	[]
       for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase__						,							torch.Tensor						)
        maps.append(torch.zeros(image.shape[-2:]						).long()						)

       # Test not batched input
       __SCREAMING_SNAKE_CASE			:      int 							=	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() <= 2_5_5						)

       # Test batched
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	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() <= 2_5_5						)

       # Test not batched input (PIL images)
       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[str] 							=	prepare_semantic_single_inputs()

       __SCREAMING_SNAKE_CASE			:      str 							=	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() <= 2_5_5						)

       # Test batched input (PIL images)
       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	prepare_semantic_batch_inputs()

       __SCREAMING_SNAKE_CASE			:      str 							=	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() <= 2_5_5						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Dict						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	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
       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	prepare_semantic_single_inputs()
       __SCREAMING_SNAKE_CASE			:      Any 							=	image_processing(lowerCAmelCase__						,							lowerCAmelCase__						,							return_tensors='''pt'''						)
       self.assertTrue(encoding['''labels'''].min().item() >= 0						)
       self.assertTrue(encoding['''labels'''].max().item() <= 1_5_0						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	True
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	image_processing(lowerCAmelCase__						,							lowerCAmelCase__						,							return_tensors='''pt'''						)
       self.assertTrue(encoding['''labels'''].min().item() >= 0						)
       self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5						)








"""simple docstring"""





def 		lowerCAmelCase_     (  lowercase_       :							int = 30		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      str 							=	exponent + 2
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	(1 + 5**0.5) / 2
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

 return int(lowercase_		)


if __name__ == "__main__":
    print(f'{solution() = }')

