"""simple docstring"""






import unittest

import numpy as np

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 PoolFormerImageProcessor


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=30    ,			_UpperCAmelCase=400    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=0.9    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=[0.5, 0.5, 0.5]    ,			_UpperCAmelCase=[0.5, 0.5, 0.5]    ,			):
       lowercase__:   List[str]					=      size if size is not None else {'''shortest_edge''': 30}
       lowercase__:   Optional[Any]					=      crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
       lowercase__:   Any					=      parent
       lowercase__:   List[Any]					=      batch_size
       lowercase__:   List[str]					=      num_channels
       lowercase__:   List[Any]					=      min_resolution
       lowercase__:   Optional[int]					=      max_resolution
       lowercase__:   Optional[Any]					=      do_resize_and_center_crop
       lowercase__:   str					=      size
       lowercase__:   int					=      crop_pct
       lowercase__:   Union[str, Any]					=      crop_size
       lowercase__:   Union[str, Any]					=      do_normalize
       lowercase__:   str					=      image_mean
       lowercase__:   List[str]					=      image_std






    def _snake_case			(   self							):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }



@require_torch
@require_vision
class     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = PoolFormerImageProcessor if is_vision_available() else None






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      PoolFormerImageProcessingTester(self							)






    @property
    def _snake_case			(   self							):
       return self.image_processor_tester.prepare_image_processor_dict()






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(_UpperCAmelCase    ,			'''do_resize_and_center_crop'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase    ,			'''size'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase    ,			'''crop_pct'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase    ,			'''do_normalize'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase    ,			'''image_mean'''							)							)
       self.assertTrue(hasattr(_UpperCAmelCase    ,			'''image_std'''							)							)






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size    ,			{'''shortest_edge''': 30}							)
       self.assertEqual(image_processor.crop_size    ,			{'''height''': 30, '''width''': 30}							)

       lowercase__:   List[str]					=      self.image_processing_class.from_dict(self.image_processor_dict    ,			size=42    ,			crop_size=84							)
       self.assertEqual(image_processor.size    ,			{'''shortest_edge''': 42}							)
       self.assertEqual(image_processor.crop_size    ,			{'''height''': 84, '''width''': 84}							)






    def _snake_case			(   self							):
       pass






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

       # Test not batched input
       lowercase__:   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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,			)

       # Test batched
       lowercase__:   int					=      image_processing(_UpperCAmelCase    ,			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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,			)






    def _snake_case			(   self							):
       # Initialize image_processing
       lowercase__:   Optional[Any]					=      self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       lowercase__:   Dict					=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_UpperCAmelCase    ,			numpify=_UpperCAmelCase							)
       for image in image_inputs:
          self.assertIsInstance(_UpperCAmelCase    ,			np.ndarray							)

       # Test not batched input
       lowercase__:   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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,			)

       # Test batched
       lowercase__:   List[str]					=      image_processing(_UpperCAmelCase    ,			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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,			)






    def _snake_case			(   self							):
       # Initialize image_processing
       lowercase__:   Any					=      self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       lowercase__:   Tuple					=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_UpperCAmelCase    ,			torchify=_UpperCAmelCase							)
       for image in image_inputs:
          self.assertIsInstance(_UpperCAmelCase    ,			torch.Tensor							)

       # Test not batched input
       lowercase__:   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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,			)

       # Test batched
       lowercase__:   Tuple					=      image_processing(_UpperCAmelCase    ,			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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )    ,			)

"""simple docstring"""






from collections.abc import Sequence



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase = False )  ->  float:
   if not arr:
      return 0

   lowercase__:   Any					=      0 if allow_empty_subarrays else float('''-inf''' )
   lowercase__:   Union[str, Any]					=      0.0
   for num in arr:
      lowercase__:   List[str]					=      max(0 if allow_empty_subarrays else num       ,    curr_sum + num )
      lowercase__:   int					=      max(__UpperCAmelCase       ,    __UpperCAmelCase )

   return max_sum


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       __A			   = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
       print(f'''{max_subarray_sum(nums) = }''')
