


import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 							lowercase__(						snake_case__			,							unittest.TestCase			):






      '''simple docstring'''







      snake_case__       =					MPNetTokenizer
      snake_case__       =					MPNetTokenizerFast
      snake_case__       =					True
      snake_case__       =					True




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







         """simple docstring"""







         super().setUp()

         UpperCamelCase__ :			Union[str, Any] 						=[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCamelCase__ :			Union[str, Any] 						=os.path.join(self.tmpdirname   ,			VOCAB_FILES_NAMES["vocab_file"])
         with open(self.vocab_file   ,			"w"   ,			encoding="utf-8") as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						="UNwant\u00E9d,running"
         UpperCamelCase__ :			Tuple 						="unwanted, running"
         return input_text, output_text




      def    UpperCAmelCase							(		self)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=self.tokenizer_class(self.vocab_file)

         UpperCamelCase__ :			Optional[int] 						=tokenizer.tokenize("UNwant\u00E9d,running")
         self.assertListEqual(__SCREAMING_SNAKE_CASE   ,			["un", "##want", "##ed", ",", "runn", "##ing"])
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)   ,			[9, 6, 7, 12, 10, 11])




      @slow
      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=self.tokenizer_class.from_pretrained("microsoft/mpnet-base")

         UpperCamelCase__ :			Tuple 						=tokenizer.encode("sequence builders"   ,			add_special_tokens=__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			int 						=tokenizer.encode("multi-sequence build"   ,			add_special_tokens=__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Dict 						=tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Any 						=tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)

         assert encoded_sentence == [0] + text + [2]
         assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]





import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 	_lowerCamelCase  (		A_  :	Optional[int]							,  A_  :	List[Any]=2_5_6      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=min(img.size      )

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

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

   '''simple docstring'''






   UpperCamelCase__ :			List[Any] 						=2.0 * x - 1.0
   return x
def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Any=True							,  A_  :	int="RGB"      )			->			int:

   '''simple docstring'''






   UpperCamelCase__ :			Dict 						=x.detach().cpu()
   if process:
      UpperCamelCase__ :			Optional[int] 						=post_process_tensor(A_      )
   UpperCamelCase__ :			Optional[int] 						=x.numpy()
   if process:
      UpperCamelCase__ :			Dict 						=(2_5_5 * x).astype(np.uinta      )
   UpperCamelCase__ :			int 						=Image.fromarray(A_      )
   if not x.mode == mode:
      UpperCamelCase__ :			Tuple 						=x.convert(A_      )
   return x
def 	_lowerCamelCase  (		A_  :	Dict      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=torch.clamp(A_							,  -1.0							,  1.0      )
   UpperCamelCase__ :			Union[str, Any] 						=(x + 1.0) / 2.0
   UpperCamelCase__ :			Union[str, Any] 						=x.permute(1							,  2							,  0      )
   return x
def 	_lowerCamelCase  (		A_  :	Tuple      )			->			List[str]:

   '''simple docstring'''






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

