'''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 MobileNetVaImageProcessor





class 							lowercase						(						unittest.TestCase ):


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Any=7   ,		__lowerCAmelCase				:	List[str]=3   ,		__lowerCAmelCase				:	Optional[Any]=18   ,		__lowerCAmelCase				:	List[Any]=30   ,		__lowerCAmelCase				:	Any=400   ,		__lowerCAmelCase				:	int=True   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	int=None   ,		)      ->					Tuple:
         lowercase_     =      size if size is not None else {"shortest_edge": 20}
         lowercase_     =      crop_size if crop_size is not None else {"height": 18, "width": 18}
         lowercase_     =      parent
         lowercase_     =      batch_size
         lowercase_     =      num_channels
         lowercase_     =      image_size
         lowercase_     =      min_resolution
         lowercase_     =      max_resolution
         lowercase_     =      do_resize
         lowercase_     =      size
         lowercase_     =      do_center_crop
         lowercase_     =      crop_size


   def __UpperCAmelCase       (   self				:	str)      ->					str:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }





@require_torch
@require_vision
class 							lowercase						(						__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=MobileNetVaImageProcessor if is_vision_available() else None


   def __UpperCAmelCase       (   self				:	List[str])      ->					str:
         lowercase_     =      MobileNetVaImageProcessingTester(self)


   @property
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Dict:
         return self.image_processor_tester.prepare_image_processor_dict()


   def __UpperCAmelCase       (   self				:	Dict)      ->					Any:
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_resize"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"size"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_center_crop"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"crop_size"))


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					List[str]:
         lowercase_     =      self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size   ,		{"shortest_edge": 20})
         self.assertEqual(image_processor.crop_size   ,		{"height": 18, "width": 18})

         lowercase_     =      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 __UpperCAmelCase       (   self				:	int)      ->					str:
         pass


   def __UpperCAmelCase       (   self				:	Any)      ->					Dict:
         # Initialize image_processing
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase_     =      prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase   ,		Image.Image)

         # Test not batched input
         lowercase_     =      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_     =      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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,		)


   def __UpperCAmelCase       (   self				:	int)      ->					List[Any]:
         # Initialize image_processing
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase_     =      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
         lowercase_     =      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_     =      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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,		)


   def __UpperCAmelCase       (   self				:	int)      ->					Dict:
         # Initialize image_processing
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase_     =      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
         lowercase_     =      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_     =      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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,		)







'''simple docstring'''

import functools
from typing import Any


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						list[str]				)	->							bool:
      '''simple docstring'''


      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or len(__lowerCamelCase				) == 0:
            raise ValueError("the string should be not empty string"				)

      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or not all(
          isinstance(__lowerCamelCase ,		__lowerCamelCase				) and len(__lowerCamelCase				) > 0 for item in words				):
            raise ValueError("the words should be a list of non-empty strings"				)

      # Build trie
      lowercase_     =      {}
      lowercase_     =      "WORD_KEEPER"

      for word in words:
            lowercase_     =      trie
            for c in word:
                  if c not in trie_node:
                        lowercase_     =      {}

                  lowercase_     =      trie_node[c]

            lowercase_     =      True

      lowercase_     =      len(__lowerCamelCase				)

      # Dynamic programming method
      @functools.cache
      def is_breakable(__lowerCamelCase			:						int				) -> bool:
            if index == len_string:
                  return True

            lowercase_     =      trie
            for i in range(__lowerCamelCase ,		__lowerCamelCase				):
                  lowercase_     =      trie_node.get(string[i] ,		__lowerCamelCase				)

                  if trie_node is None:
                        return False

                  if trie_node.get(__lowerCamelCase ,		__lowerCamelCase				) and is_breakable(i + 1				):
                        return True

            return False

      return is_breakable(0				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






