




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		):
     """simple docstring"""

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




     def 			__snake_case (     self	: Tuple      )					->							List[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_      (	)       ->      Dict:


      '''simple docstring'''




      lowerCamelCase__   	=					load_dataset('hf-internal-testing/fixtures_ade20k'  ,					split='test'    )

      lowerCamelCase__   	=					Image.open(dataset[0]['file']    )
      lowerCamelCase__   	=					Image.open(dataset[1]['file']    )

      return image, map

def       lowerCAmelCase_      (	)       ->      Dict:


      '''simple docstring'''




      lowerCamelCase__   	=					load_dataset('hf-internal-testing/fixtures_ade20k'  ,					split='test'    )

      lowerCamelCase__   	=					Image.open(dataset[0]['file']    )
      lowerCamelCase__   	=					Image.open(dataset[1]['file']    )
      lowerCamelCase__   	=					Image.open(dataset[2]['file']    )
      lowerCamelCase__   	=					Image.open(dataset[3]['file']    )

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






@require_torch
@require_vision
class       A      (		_lowerCAmelCase      ,    unittest.TestCase		):
     """simple docstring"""

     _snake_case:						Any  =SegformerImageProcessor if is_vision_available() else None




     def 			__snake_case (     self	: Optional[int]      )					->							Any:
           lowerCamelCase__   	=					SegformerImageProcessingTester(self      )




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




     def 			__snake_case (     self	: str      )					->							Tuple:
           lowerCamelCase__   	=					self.image_processing_class(**self.image_processor_dict      )
           self.assertTrue(hasattr(A__						,	'do_resize'      )      )
           self.assertTrue(hasattr(A__						,	'size'      )      )
           self.assertTrue(hasattr(A__						,	'do_normalize'      )      )
           self.assertTrue(hasattr(A__						,	'image_mean'      )      )
           self.assertTrue(hasattr(A__						,	'image_std'      )      )
           self.assertTrue(hasattr(A__						,	'do_reduce_labels'      )      )




     def 			__snake_case (     self	: Optional[Any]      )					->							Dict:
           lowerCamelCase__   	=					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						,	A__      )

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




     def 			__snake_case (     self	: str      )					->							Tuple:
           pass




     def 			__snake_case (     self	: Union[str, Any]      )					->							List[Any]:
           # Initialize image_processing
           lowerCamelCase__   	=					self.image_processing_class(**self.image_processor_dict      )
           # create random PIL images
           lowerCamelCase__   	=					prepare_image_inputs(self.image_processor_tester						,	equal_resolution=A__      )
           for image in image_inputs:
                 self.assertIsInstance(A__						,	Image.Image      )

           # Test not batched input
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					image_processing(A__						,	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 			__snake_case (     self	: str      )					->							Any:
           # Initialize image_processing
           lowerCamelCase__   	=					self.image_processing_class(**self.image_processor_dict      )
           # create random numpy tensors
           lowerCamelCase__   	=					prepare_image_inputs(self.image_processor_tester						,	equal_resolution=A__						,	numpify=A__      )
           for image in image_inputs:
                 self.assertIsInstance(A__						,	np.ndarray      )

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

           # Test not batched input
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					image_processing(A__						,	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 			__snake_case (     self	: List[str]      )					->							List[str]:
           # Initialize image_processing
           lowerCamelCase__   	=					self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           lowerCamelCase__   	=					prepare_image_inputs(self.image_processor_tester						,	equal_resolution=A__						,	torchify=A__      )
           lowerCamelCase__   	=					[]
           for image in image_inputs:
                 self.assertIsInstance(A__						,	torch.Tensor      )
                 maps.append(torch.zeros(image.shape[-2:]      ).long()      )

           # Test not batched input
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					image_processing(A__						,	A__						,	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)
           lowerCamelCase__	,		lowerCamelCase__   	=					prepare_semantic_single_inputs()

           lowerCamelCase__   	=					image_processing(A__						,	A__						,	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)
           lowerCamelCase__	,		lowerCamelCase__   	=					prepare_semantic_batch_inputs()

           lowerCamelCase__   	=					image_processing(A__						,	A__						,	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 			__snake_case (     self	: Union[str, Any]      )					->							int:
           # Initialize image_processing
           lowerCamelCase__   	=					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
           lowerCamelCase__	,		lowerCamelCase__   	=					prepare_semantic_single_inputs()
           lowerCamelCase__   	=					image_processing(A__						,	A__						,	return_tensors='pt'      )
           self.assertTrue(encoding['labels'].min().item() >= 0      )
           self.assertTrue(encoding['labels'].max().item() <= 1_5_0      )

           lowerCamelCase__   	=					True
           lowerCamelCase__   	=					image_processing(A__						,	A__						,	return_tensors='pt'      )
           self.assertTrue(encoding['labels'].min().item() >= 0      )
           self.assertTrue(encoding['labels'].max().item() <= 2_5_5      )





import math

def       lowerCAmelCase_      (	lowerCamelCase:							int    )       ->      str:


      '''simple docstring'''




      if number == 0:
            lowerCamelCase__   	=					'Zero'
            return words
      else:
            lowerCamelCase__   	=					math.logaa(lowerCamelCase    )
            lowerCamelCase__   	=					digits + 1
            lowerCamelCase__   	=					{}
            lowerCamelCase__   	=					''
            lowerCamelCase__   	=					'One'
            lowerCamelCase__   	=					'Two'
            lowerCamelCase__   	=					'Three'
            lowerCamelCase__   	=					'Four'
            lowerCamelCase__   	=					'Five'
            lowerCamelCase__   	=					'Six'
            lowerCamelCase__   	=					'Seven'
            lowerCamelCase__   	=					'Eight'
            lowerCamelCase__   	=					'Nine'

            lowerCamelCase__   	=					{}
            lowerCamelCase__   	=					''
            lowerCamelCase__   	=					'Twenty'
            lowerCamelCase__   	=					'Thirty'
            lowerCamelCase__   	=					'Forty'
            lowerCamelCase__   	=					'Fifty'
            lowerCamelCase__   	=					'Sixty'
            lowerCamelCase__   	=					'Seventy'
            lowerCamelCase__   	=					'Eighty'
            lowerCamelCase__   	=					'Ninety'

            lowerCamelCase__   	=					{}
            lowerCamelCase__   	=					'Ten'
            lowerCamelCase__   	=					'Eleven'
            lowerCamelCase__   	=					'Twelve'
            lowerCamelCase__   	=					'Thirteen'
            lowerCamelCase__   	=					'Fourteen'
            lowerCamelCase__   	=					'Fifteen'
            lowerCamelCase__   	=					'Sixteen'
            lowerCamelCase__   	=					'Seventeen'
            lowerCamelCase__   	=					'Eighteen'
            lowerCamelCase__   	=					'Nineteen'

            lowerCamelCase__   	=					{}
            lowerCamelCase__   	=					'Hundred,'
            lowerCamelCase__   	=					'Thousand,'
            lowerCamelCase__   	=					'Lakh,'
            lowerCamelCase__   	=					'Crore,'

            lowerCamelCase__   	=					number
            lowerCamelCase__   	=					''
            lowerCamelCase__   	=					0
            lowerCamelCase__   	=					int(lowerCamelCase    )
            while counter < digits:
                  lowerCamelCase__   	=					temp_num % 10
                  if counter % 2 == 0:
                        lowerCamelCase__   	=					''
                        if counter in placevalue and current != 0:
                              lowerCamelCase__   	=					placevalue[counter]
                        if counter == 2:
                              lowerCamelCase__   	=					singles[current] + addition + words
                        elif counter == 0:
                              if ((temp_num % 1_00) // 10) == 1:
                                    lowerCamelCase__   	=					teens[current] + addition + words
                                    lowerCamelCase__   	=					temp_num // 10
                                    counter += 1
                              else:
                                    lowerCamelCase__   	=					singles[current] + addition + words

                        else:
                              lowerCamelCase__   	=					doubles[current] + addition + words

                  else:
                        if counter == 1:
                              if current == 1:
                                    lowerCamelCase__   	=					teens[number % 10] + words
                              else:
                                    lowerCamelCase__   	=					''
                                    if counter in placevalue:
                                          lowerCamelCase__   	=					placevalue[counter]
                                    lowerCamelCase__   	=					doubles[current] + addition + words
                        else:
                              lowerCamelCase__   	=					''
                              if counter in placevalue:
                                    if current == 0 and ((temp_num % 1_00) // 10) == 0:
                                          lowerCamelCase__   	=					''
                                    else:
                                          lowerCamelCase__   	=					placevalue[counter]
                              if ((temp_num % 1_00) // 10) == 1:
                                    lowerCamelCase__   	=					teens[current] + addition + words
                                    lowerCamelCase__   	=					temp_num // 10
                                    counter += 1
                              else:
                                    lowerCamelCase__   	=					singles[current] + addition + words
                  counter += 1
                  lowerCamelCase__   	=					temp_num // 10
      return words


if __name__ == "__main__":
      import doctest

      doctest.testmod()