

"""simple docstring"""


from __future__ import annotations

__A						       =      "#"





class    snake_case     :

				def __init__(     self							:    Any)->       None:



						'''simple docstring'''


						__lowerCAmelCase:	dict   				=      {}

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    str)->       None:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self._trie
						for char in text:
								if char not in trie:
										__lowerCAmelCase:	str   				=      {}
								__lowerCAmelCase:	Tuple   				=      trie[char]
						__lowerCAmelCase:	Dict   				=      True

				def 							lowercase_					(     self							:    List[str]					,  UpperCamelCase__							:    str)->       tuple | list:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self._trie
						for char in prefix:
								if char in trie:
										__lowerCAmelCase:	List[Any]   				=      trie[char]
								else:
										return []
						return self._elements(UpperCamelCase__)




				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    dict)->       tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      []
						for c, v in d.items():
								__lowerCAmelCase:	Optional[int]   				=      [" "] if c == END else [(c + s) for s in self._elements(UpperCamelCase__)]
								result.extend(UpperCamelCase__)
						return tuple(UpperCamelCase__)


__A						       =      Trie()
__A						       =      ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
			trie.insert_word(word)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     tuple:
		__lowerCAmelCase:	List[Any]   				=      trie.find_word(__SCREAMING_SNAKE_CASE			)
		return tuple(string + word for word in suffixes			)






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


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()





"""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    snake_case     (    unittest.TestCase  ):

				def __init__(     self							:    Any					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Any=7					,  UpperCamelCase__							:    List[str]=3					,  UpperCamelCase__							:    List[Any]=1_8					,  UpperCamelCase__							:    List[Any]=3_0					,  UpperCamelCase__							:    List[str]=4_0_0					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    Optional[Any]=None					,  UpperCamelCase__							:    List[str]=True					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    int=[0.48145466, 0.4578275, 0.40821073]					,  UpperCamelCase__							:    str=[0.26862954, 0.26130258, 0.27577711]					,  UpperCamelCase__							:    List[str]=True					,  )->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      size if size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase:	Union[str, Any]   				=      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
						__lowerCAmelCase:	Optional[int]   				=      parent
						__lowerCAmelCase:	List[str]   				=      batch_size
						__lowerCAmelCase:	Union[str, Any]   				=      num_channels
						__lowerCAmelCase:	Optional[Any]   				=      image_size
						__lowerCAmelCase:	Tuple   				=      min_resolution
						__lowerCAmelCase:	List[str]   				=      max_resolution
						__lowerCAmelCase:	List[Any]   				=      do_resize
						__lowerCAmelCase:	Union[str, Any]   				=      size
						__lowerCAmelCase:	List[Any]   				=      do_center_crop
						__lowerCAmelCase:	Optional[int]   				=      crop_size
						__lowerCAmelCase:	Dict   				=      do_normalize
						__lowerCAmelCase:	List[str]   				=      image_mean
						__lowerCAmelCase:	Optional[int]   				=      image_std
						__lowerCAmelCase:	str   				=      do_convert_rgb

				def 							lowercase_					(     self							:    Tuple)->       str:



						'''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 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Tuple=False					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    Dict=False)->       List[str]:



						'''simple docstring'''


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

						if equal_resolution:
								__lowerCAmelCase:	Optional[int]   				=      []
								for i in range(self.batch_size):
										image_inputs.append(
										    np.random.randint(
										        2_5_5					,  size=(self.num_channels, self.max_resolution, self.max_resolution)					,  dtype=np.uinta))
						else:
								__lowerCAmelCase:	List[str]   				=      []
								for i in range(self.batch_size):
										__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      np.random.choice(np.arange(self.min_resolution					,  self.max_resolution)					,  2)
										image_inputs.append(np.random.randint(2_5_5					,  size=(self.num_channels, width, height)					,  dtype=np.uinta))

						if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								__lowerCAmelCase:	Union[str, Any]   				=      [Image.fromarray(np.moveaxis(UpperCamelCase__					,  0					,  -1)) for x in image_inputs]

						if torchify:
								__lowerCAmelCase:	str   				=      [torch.from_numpy(UpperCamelCase__) for x in image_inputs]

						return image_inputs





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				str       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      ChineseCLIPImageProcessingTester(self					,  do_center_crop=UpperCamelCase__)

				@property
				def 							lowercase_					(     self							:    Any)->       Optional[Any]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    Union[str, Any])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    List[Any])->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size					,  {"height": 2_2_4, "width": 2_2_4})
						self.assertEqual(image_processor.crop_size					,  {"height": 1_8, "width": 1_8})

						__lowerCAmelCase:	List[str]   				=      self.image_processing_class.from_dict(self.image_processor_dict					,  size=4_2					,  crop_size=8_4)
						self.assertEqual(image_processor.size					,  {"shortest_edge": 4_2})
						self.assertEqual(image_processor.crop_size					,  {"height": 8_4, "width": 8_4})

				def 							lowercase_					(     self							:    List[str])->       Optional[int]:



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Any)->       Optional[int]:



						'''simple docstring'''


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

						# Test not batched input
						__lowerCAmelCase:	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
						__lowerCAmelCase:	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 							lowercase_					(     self							:    int)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	List[Any]   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  numpify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  np.ndarray)

						# Test not batched input
						__lowerCAmelCase:	List[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
						__lowerCAmelCase:	Any   				=      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 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	Dict   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:	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
						__lowerCAmelCase:	Optional[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"],
						    )					,  )





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      ChineseCLIPImageProcessingTester(self					,  num_channels=4					,  do_center_crop=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      3

				@property
				def 							lowercase_					(     self							:    Union[str, Any])->       Any:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						pass




				def 							lowercase_					(     self							:    Tuple)->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	int   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      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
						__lowerCAmelCase:	Optional[int]   				=      image_processing(UpperCamelCase__					,  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"],
						    )					,  )


