code: 
  
  
  
  
  '''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 OwlViTImageProcessor
  
  
  
  
  
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase,						__UpperCamelCase=7,						__UpperCamelCase=3,						__UpperCamelCase=18,						__UpperCamelCase=30,						__UpperCamelCase=400,						__UpperCamelCase=True,						__UpperCamelCase=None,						__UpperCamelCase=True,						__UpperCamelCase=None,						__UpperCamelCase=True,						__UpperCamelCase=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],						__UpperCamelCase=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],						__UpperCamelCase=True,						):
  							_snake_case			 = parent
  							_snake_case			 = batch_size
  							_snake_case			 = num_channels
  							_snake_case			 = image_size
  							_snake_case			 = min_resolution
  							_snake_case			 = max_resolution
  							_snake_case			 = do_resize
  							_snake_case			 = size if size is not None else {"height": 18, "width": 18}
  							_snake_case			 = do_center_crop
  							_snake_case			 = crop_size if crop_size is not None else {"height": 18, "width": 18}
  							_snake_case			 = do_normalize
  							_snake_case			 = image_mean
  							_snake_case			 = image_std
  							_snake_case			 = do_convert_rgb
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							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,
  							}
  
  
  
  
  
  @require_torch
  @require_vision
  class   lowerCamelCase_							( UpperCAmelCase_, unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				OwlViTImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = OwlViTImageProcessingTester(self				)
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.image_processing_class.from_dict(self.image_processor_dict				)
  							self.assertEqual(image_processor.size,						{"height": 18, "width": 18}				)
  							self.assertEqual(image_processor.crop_size,						{"height": 18, "width": 18}				)
  
  							_snake_case			 = self.image_processing_class.from_dict(self.image_processor_dict,						size=42,						crop_size=84				)
  							self.assertEqual(image_processor.size,						{"height": 42, "width": 42}				)
  							self.assertEqual(image_processor.crop_size,						{"height": 84, "width": 84}				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# Initialize image_processing
  							_snake_case			 = self.image_processing_class(**self.image_processor_dict				)
  							# create random PIL images
  							_snake_case			 = prepare_image_inputs(self.image_processor_tester,						equal_resolution=__UpperCamelCase				)
  
  							for image in image_inputs:
  									self.assertIsInstance(__UpperCamelCase,						Image.Image				)
  
  							# Test not batched input
  							_snake_case			 = 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
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self				):
  							# Initialize image_processing
  							_snake_case			 = self.image_processing_class(**self.image_processor_dict				)
  							# create random numpy tensors
  							_snake_case			 = 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
  							_snake_case			 = 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
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self				):
  							# Initialize image_processing
  							_snake_case			 = self.image_processing_class(**self.image_processor_dict				)
  							# create random PyTorch tensors
  							_snake_case			 = 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
  							_snake_case			 = 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
  							_snake_case			 = 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"],
  							    ),						)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import unittest
  import warnings
  from dataclasses import dataclass
  
  from transformers.convert_slow_tokenizer import SpmConverter
  from transformers.testing_utils import get_tests_dir
  
  
  
  
  
  @dataclass
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  
  
  
  
  
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece.model"				)
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = SpmConverter(__UpperCamelCase				)
  							self.assertEqual(len(__UpperCamelCase				),						0				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = SpmConverter(__UpperCamelCase				)
  							self.assertEqual(len(__UpperCamelCase				),						1				)
  
  							self.assertIn(
  							    "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
  							    " which is not implemented in the fast tokenizers.",						str(w[0].message				),						)
  
  
  
  
  
  