




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 ViTImageProcessor





class 		A__				(   unittest.TestCase ):






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase=7				,     lowerCamelCase=3				,     lowerCamelCase=18				,     lowerCamelCase=30				,     lowerCamelCase=400				,     lowerCamelCase=True				,     lowerCamelCase=None				,     lowerCamelCase=True				,     lowerCamelCase=[0.5, 0.5, 0.5]				,     lowerCamelCase=[0.5, 0.5, 0.5]				,     )      ->   str:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							size if size is not None else {"height": 18, "width": 18}
						__magic_name__					:							str		   =							parent
						__magic_name__					:							Any		   =							batch_size
						__magic_name__					:							str		   =							num_channels
						__magic_name__					:							Union[str, Any]		   =							image_size
						__magic_name__					:							Union[str, Any]		   =							min_resolution
						__magic_name__					:							Tuple		   =							max_resolution
						__magic_name__					:							Union[str, Any]		   =							do_resize
						__magic_name__					:							Optional[int]		   =							size
						__magic_name__					:							Optional[int]		   =							do_normalize
						__magic_name__					:							Optional[int]		   =							image_mean
						__magic_name__					:							str		   =							image_std






	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






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





@require_torch
@require_vision
class 		A__				(   SCREAMING_SNAKE_CASE__		,     unittest.TestCase ):
	lowerCamelCase__		: Optional[Any]				    =ViTImageProcessor if is_vision_available() else None






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




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							ViTImageProcessingTester(self						)






	@property
	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()






	def  lowercase      (	self						)      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(snake_case__				,     '''image_mean'''						)						)
						self.assertTrue(hasattr(snake_case__				,     '''image_std'''						)						)
						self.assertTrue(hasattr(snake_case__				,     '''do_normalize'''						)						)
						self.assertTrue(hasattr(snake_case__				,     '''do_resize'''						)						)
						self.assertTrue(hasattr(snake_case__				,     '''size'''						)						)






	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size				,     {'''height''': 18, '''width''': 18}						)

						__magic_name__					:							Optional[int]		   =							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42						)
						self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}						)






	def  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						pass






	def  lowercase      (	self						)      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						__magic_name__					:							Optional[Any]		   =							prepare_image_inputs(self.image_processor_tester				,     equal_resolution=snake_case__						)
						for image in image_inputs:
											self.assertIsInstance(snake_case__				,     Image.Image						)

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

						# Test batched
						__magic_name__					:							int		   =							image_processing(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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )				,     )






	def  lowercase      (	self						)      ->   Optional[Any]:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						__magic_name__					:							Optional[int]		   =							prepare_image_inputs(self.image_processor_tester				,     equal_resolution=snake_case__				,     numpify=snake_case__						)
						for image in image_inputs:
											self.assertIsInstance(snake_case__				,     np.ndarray						)

						# Test not batched input
						__magic_name__					:							Union[str, 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
						__magic_name__					:							List[str]		   =							image_processing(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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )				,     )






	def  lowercase      (	self						)      ->   List[str]:




						"""simple docstring"""






						__magic_name__					:							str		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						__magic_name__					:							Tuple		   =							prepare_image_inputs(self.image_processor_tester				,     equal_resolution=snake_case__				,     torchify=snake_case__						)
						for image in image_inputs:
											self.assertIsInstance(snake_case__				,     torch.Tensor						)

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

						# Test batched
						__magic_name__					:							int		   =							image_processing(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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )				,     )











"""simple docstring"""







from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       word
												lowercase__				:      Any=       count




						def __eq__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count == other.count




						def __lt__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count < other.count





def 						lowercase__(A					,		A						)				->list[str]:




						"""simple docstring"""



						lowercase__				:      Heap[WordCount]=       Heap()
						lowercase__				:      str=       Counter(A						)
						heap.build_max_heap(
						    [WordCount(A					,		A						) for word, count in count_by_word.items()]						)
						return [heap.extract_max().word for _ in range(min(A					,		len(A						)						)						)]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






