


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		:  Tuple    ,     lowercase_		:  Dict    ,     lowercase_		:  Dict=7    ,     lowercase_		:  Optional[int]=3    ,     lowercase_		:  int=30    ,     lowercase_		:  Optional[Any]=400    ,     lowercase_		:  List[str]=True    ,     lowercase_		:  int=None    ,     lowercase_		:  Union[str, Any]=True    ,     lowercase_		:  Optional[Any]=[0.5, 0.5, 0.5]    ,     lowercase_		:  Optional[int]=[0.5, 0.5, 0.5]    ,     lowercase_		:  List[Any]=False    ,     )      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=size if size is not None else {'height': 30, 'width': 30}
             _lowerCamelCase :	List[str]						=parent
             _lowerCamelCase :	int						=batch_size
             _lowerCamelCase :	Optional[int]						=num_channels
             _lowerCamelCase :	List[Any]						=min_resolution
             _lowerCamelCase :	Optional[int]						=max_resolution
             _lowerCamelCase :	str						=do_resize
             _lowerCamelCase :	Optional[Any]						=size
             _lowerCamelCase :	List[str]						=do_normalize
             _lowerCamelCase :	Optional[int]						=image_mean
             _lowerCamelCase :	Union[str, Any]						=image_std
             _lowerCamelCase :	str						=do_reduce_labels






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		Union[str, Any]:
             """simple docstring"""




             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_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Tuple						=load_dataset('hf-internal-testing/fixtures_ade20k'	,    split='test'  )

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

       return image, map




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Optional[Any]						=load_dataset('hf-internal-testing/fixtures_ade20k'	,    split='test'  )

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

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




@require_torch
@require_vision
class 			A						(     UpperCamelCase_					,							unittest.TestCase  ):
      UpperCamelCase__							:						Any   =SegformerImageProcessor if is_vision_available() else None






      def  lowerCamelCase (      self		:  List[str]				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=SegformerImageProcessingTester(self				)






      @property
      def  lowerCamelCase (      self		:  List[str]				)      ->		Tuple:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def  lowerCamelCase (      self		:  int				)      ->		Dict:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=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  lowerCamelCase (      self		:  Union[str, Any]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	int						=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_				)

             _lowerCamelCase :	Union[str, Any]						=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  lowerCamelCase (      self		:  Optional[int]				)      ->		Any:
             """simple docstring"""




             pass






      def  lowerCamelCase (      self		:  str				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=self.image_processing_class(**self.image_processor_dict				)
             # create random PIL images
             _lowerCamelCase :	Dict						=prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowercase_				)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_    ,     Image.Image				)

             # Test not batched input
             _lowerCamelCase :	List[str]						=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 :	str						=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  lowerCamelCase (      self		:  str				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	Any						=self.image_processing_class(**self.image_processor_dict				)
             # create random numpy tensors
             _lowerCamelCase :	int						=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
             _lowerCamelCase :	List[str]						=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 :	List[str]						=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  lowerCamelCase (      self		:  Any				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	str						=self.image_processing_class(**self.image_processor_dict				)
             # create random PyTorch tensors
             _lowerCamelCase :	Union[str, Any]						=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
             _lowerCamelCase :	Optional[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
             _lowerCamelCase :	Tuple						=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  lowerCamelCase (      self		:  List[str]				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=self.image_processing_class(**self.image_processor_dict				)
             # create random PyTorch tensors
             _lowerCamelCase :	List[str]						=prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowercase_    ,     torchify=lowercase_				)
             _lowerCamelCase :	int						=[]
             for image in image_inputs:
                    self.assertIsInstance(lowercase_    ,     torch.Tensor				)
                    maps.append(torch.zeros(image.shape[-2:]				).long()				)

             # Test not batched input
             _lowerCamelCase :	Dict						=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
             _lowerCamelCase :	Tuple						=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)
             _lowerCamelCase :	str						=prepare_semantic_single_inputs()

             _lowerCamelCase :	Union[str, Any]						=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)
             _lowerCamelCase :	List[str]						=prepare_semantic_batch_inputs()

             _lowerCamelCase :	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  lowerCamelCase (      self		:  List[str]				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	List[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
             _lowerCamelCase :	Optional[Any]						=prepare_semantic_single_inputs()
             _lowerCamelCase :	str						=image_processing(lowercase_    ,     lowercase_    ,     return_tensors='pt'				)
             self.assertTrue(encoding['labels'].min().item() >= 0				)
             self.assertTrue(encoding['labels'].max().item() <= 150				)

             _lowerCamelCase :	int						=True
             _lowerCamelCase :	Union[str, Any]						=image_processing(lowercase_    ,     lowercase_    ,     return_tensors='pt'				)
             self.assertTrue(encoding['labels'].min().item() >= 0				)
             self.assertTrue(encoding['labels'].max().item() <= 255				)






from __future__ import annotations

from collections.abc import Generator




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) or n < 1:
              raise Exception('Sequence only defined for positive integers'  )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _lowerCamelCase :	Optional[int]						=3 * n + 1
              yield n




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=43
       _lowerCamelCase :	Dict						=tuple(collatz_sequence(SCREAMING_SNAKE_CASE__  )  )
       print(SCREAMING_SNAKE_CASE__  )
       print(F'''Collatz sequence from {n} took {len(SCREAMING_SNAKE_CASE__  )} steps.'''  )


if __name__ == "__main__":
 main()


