



import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: Tuple=2_56						):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     min(img.size						)

		if s < target_image_size:
				raise ValueError(f'min dim for image {s} < {target_image_size}'						)

		__UpperCAmelCase   :					Optional[int]													=     target_image_size / s
		__UpperCAmelCase   :					Union[str, Any]													=     (round(r * img.size[1]						), round(r * img.size[0]						))
		__UpperCAmelCase   :					Optional[int]													=     TF.resize(_UpperCAmelCase			,       _UpperCAmelCase			,       interpolation=PIL.Image.LANCZOS						)
		__UpperCAmelCase   :					List[Any]													=     TF.center_crop(_UpperCAmelCase			,       output_size=2 * [target_image_size]						)
		__UpperCAmelCase   :					Union[str, Any]													=     torch.unsqueeze(T.ToTensor()(_UpperCAmelCase						)			,       0						)
		return img

def 	a						(				_UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					str													=     2.0 * x - 1.0
		return x

def 	a						(				_UpperCAmelCase							: List[Any]			,       _UpperCAmelCase							: Any=True			,       _UpperCAmelCase							: int="RGB"						):







		'''simple docstring'''





		__UpperCAmelCase   :					Dict													=     x.detach().cpu()
		if process:
				__UpperCAmelCase   :					Union[str, Any]													=     post_process_tensor(_UpperCAmelCase						)
		__UpperCAmelCase   :					List[str]													=     x.numpy()
		if process:
				__UpperCAmelCase   :					Any													=     (2_55 * x).astype(np.uinta						)
		__UpperCAmelCase   :					Optional[int]													=     Image.fromarray(_UpperCAmelCase						)
		if not x.mode == mode:
				__UpperCAmelCase   :					List[Any]													=     x.convert(_UpperCAmelCase						)
		return x

def 	a						(				_UpperCAmelCase							: Tuple						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[int]													=     torch.clamp(_UpperCAmelCase			,       -1.0			,       1.0						)
		__UpperCAmelCase   :					str													=     (x + 1.0) / 2.0
		__UpperCAmelCase   :					List[Any]													=     x.permute(1			,       2			,       0						)
		return x

def 	a						(				_UpperCAmelCase							: Any						):







		'''simple docstring'''





		__UpperCAmelCase   :					List[str]													=     post_process_tensor(x.squeeze()						)
		return x.permute(2			,       0			,       1						).unsqueeze(0						)










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 		UpperCAmelCase__      :




			'''simple docstring'''






			UpperCamelCase      						=	42

class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     get_tests_dir('''fixtures/test_sentencepiece.model'''			)
					__UpperCAmelCase   :					Union[str, Any]													=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''			)

					__UpperCAmelCase   :					List[str]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Optional[int]													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	0			)

					__UpperCAmelCase   :					Optional[int]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Dict													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	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			)	,	)





