
"""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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ChineseCLIPImageProcessor
class 			UpperCamelCase      (							unittest.TestCase  ):
     def __init__(     self  ,     snake_case__  ,     snake_case__=7  ,     snake_case__=3  ,     snake_case__=18  ,     snake_case__=30  ,     snake_case__=400  ,     snake_case__=True  ,     snake_case__=None  ,     snake_case__=True  ,     snake_case__=None  ,     snake_case__=True  ,     snake_case__=[0.48_145_466, 0.4_578_275, 0.40_821_073]  ,     snake_case__=[0.26_862_954, 0.26_130_258, 0.27_577_711]  ,     snake_case__=True  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = size if size is not None else {"height": 224, "width": 224}
       _SCREAMING_SNAKE_CASE   :				str							    = crop_size if crop_size is not None else {"height": 18, "width": 18}
       _SCREAMING_SNAKE_CASE   :				int							    = parent
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = batch_size
       _SCREAMING_SNAKE_CASE   :				str							    = num_channels
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = image_size
       _SCREAMING_SNAKE_CASE   :				Any							    = min_resolution
       _SCREAMING_SNAKE_CASE   :				int							    = max_resolution
       _SCREAMING_SNAKE_CASE   :				int							    = do_resize
       _SCREAMING_SNAKE_CASE   :				int							    = size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = do_center_crop
       _SCREAMING_SNAKE_CASE   :				Dict							    = crop_size
       _SCREAMING_SNAKE_CASE   :				Any							    = do_normalize
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = image_mean
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = image_std
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = do_convert_rgb
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
       }




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=False  ,     snake_case__=False  ,     snake_case__=False				):




       """simple docstring"""


       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
         _SCREAMING_SNAKE_CASE   :				Any							    = []
         for i in range(self.batch_size				):
           image_inputs.append(
               np.random.randint(
                   255  ,     size=(self.num_channels, self.max_resolution, self.max_resolution)  ,     dtype=np.uinta				)				)
       else:
         _SCREAMING_SNAKE_CASE   :				Any							    = []
         for i in range(self.batch_size				):
           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = np.random.choice(np.arange(self.min_resolution  ,     self.max_resolution				)  ,     2				)
           image_inputs.append(np.random.randint(255  ,     size=(self.num_channels, width, height)  ,     dtype=np.uinta				)				)

       if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_  ,     0  ,     -1				)				) for x in image_inputs]

       if torchify:
         _SCREAMING_SNAKE_CASE   :				int							    = [torch.from_numpy(SCREAMING_SNAKE_CASE_				) for x in image_inputs]

       return image_inputs
@require_torch
@require_vision
class 			UpperCamelCase      (							__lowerCamelCase      ,       unittest.TestCase  ):
     A__      =			ChineseCLIPImageProcessor if is_vision_available() else None
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = ChineseCLIPImageProcessingTester(self  ,     do_center_crop=SCREAMING_SNAKE_CASE_				)
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "do_resize"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "size"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "do_center_crop"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "center_crop"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "do_normalize"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "image_mean"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "image_std"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "do_convert_rgb"				)				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.image_processing_class.from_dict(self.image_processor_dict				)
       self.assertEqual(image_processor.size  ,     {"height": 224, "width": 224}				)
       self.assertEqual(image_processor.crop_size  ,     {"height": 18, "width": 18}				)

       _SCREAMING_SNAKE_CASE   :				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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       pass
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_				)
       for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,     Image.Image				)

       # Test not batched input
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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
       _SCREAMING_SNAKE_CASE   :				Tuple							    = image_processing(SCREAMING_SNAKE_CASE_  ,     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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       _SCREAMING_SNAKE_CASE   :				int							    = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_  ,     numpify=SCREAMING_SNAKE_CASE_				)
       for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,     np.ndarray				)

       # 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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )

       # Test batched
       _SCREAMING_SNAKE_CASE   :				Dict							    = image_processing(SCREAMING_SNAKE_CASE_  ,     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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_  ,     torchify=SCREAMING_SNAKE_CASE_				)
       for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,     torch.Tensor				)

       # 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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )

       # Test batched
       _SCREAMING_SNAKE_CASE   :				int							    = image_processing(SCREAMING_SNAKE_CASE_  ,     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"],
           )  ,     )
@require_torch
@require_vision
class 			UpperCamelCase      (							__lowerCamelCase      ,       unittest.TestCase  ):
     A__      =			ChineseCLIPImageProcessor if is_vision_available() else None
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = ChineseCLIPImageProcessingTester(self  ,     num_channels=4  ,     do_center_crop=SCREAMING_SNAKE_CASE_				)
       _SCREAMING_SNAKE_CASE   :				str							    = 3
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "do_resize"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "size"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "do_center_crop"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "center_crop"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "do_normalize"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "image_mean"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "image_std"				)				)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,     "do_convert_rgb"				)				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       pass




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       _SCREAMING_SNAKE_CASE   :				int							    = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_				)
       for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,     Image.Image				)

       # Test not batched input
       _SCREAMING_SNAKE_CASE   :				int							    = image_processing(image_inputs[0]  ,     return_tensors="pt"				).pixel_values
       self.assertEqual(
           encoded_images.shape  ,     (
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )

       # Test batched
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = image_processing(SCREAMING_SNAKE_CASE_  ,     return_tensors="pt"				).pixel_values
       self.assertEqual(
           encoded_images.shape  ,     (
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )  ,     )





"""simple docstring"""
from __future__ import annotations

lowercase_       :						List[str]     					=	'''#'''
class 			UpperCamelCase      :
     def __init__(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				dict							    = {}
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = self._trie
       for char in text:
         if char not in trie:
           _SCREAMING_SNAKE_CASE   :				List[str]							    = {}
         _SCREAMING_SNAKE_CASE   :				List[str]							    = trie[char]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = True
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self._trie
       for char in prefix:
         if char in trie:
           _SCREAMING_SNAKE_CASE   :				str							    = trie[char]
         else:
           return []
       return self._elements(snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = []
       for c, v in d.items():
         _SCREAMING_SNAKE_CASE   :				int							    = [" "] if c == END else [(c + s) for s in self._elements(snake_case__				)]
         result.extend(snake_case__				)
       return tuple(snake_case__				)
lowercase_       :						Union[str, Any]     					=	Trie()
lowercase_       :						Optional[Any]     					=	('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''')
for word in words:
      trie.insert_word(word)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = trie.find_word(lowerCamelCase__				)
  return tuple(string + word for word in suffixes				)





def    _lowerCAmelCase    (     )  ->    None:
  print(autocomplete_using_trie("de"				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



