code: 
  
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
   import PIL
  
  
  lowerCamelCase			:							Union[str, Any]       =logging.get_logger(__name__)
  
  
  
  
  
  class     __a						(					A__ ):
   _lowerCAmelCase : str 				=				['''pixel_values''']
  
  
  
  
  
  
  
   def __init__(	self      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     bool = True							,     SCREAMING_SNAKE_CASE      :     Dict[str, int] = None							,     SCREAMING_SNAKE_CASE      :     PILImageResampling = PIL.Image.NEAREST							,     SCREAMING_SNAKE_CASE      :     bool = False							,     SCREAMING_SNAKE_CASE      :     Dict[str, int] = None							,     SCREAMING_SNAKE_CASE      :     Union[int, float] = 1 / 2_55							,     SCREAMING_SNAKE_CASE      :     bool = False							,     SCREAMING_SNAKE_CASE      :     bool = True							,     SCREAMING_SNAKE_CASE      :     bool = True							,     SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None							,     SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None							,     SCREAMING_SNAKE_CASE      :     bool = True							,     **SCREAMING_SNAKE_CASE      :     List[Any]							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().__init__(**SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     List[str]        =						size if size is not None else {"height": 3_46, "width": 3_46}
        UpperCamelCase__    :     Dict        =						get_size_dict(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
        UpperCamelCase__    :     Union[str, Any]        =						get_size_dict(SCREAMING_SNAKE_CASE							,     param_name="crop_size"		)
  
        UpperCamelCase__    :     Tuple        =						do_resize
        UpperCamelCase__    :     Any        =						size
        UpperCamelCase__    :     str        =						resample
        UpperCamelCase__    :     Union[str, Any]        =						do_center_crop
        UpperCamelCase__    :     List[str]        =						crop_size
        UpperCamelCase__    :     List[Any]        =						do_rescale
        UpperCamelCase__    :     Dict        =						rescale_factor
        UpperCamelCase__    :     Optional[Any]        =						rescale_offset
        UpperCamelCase__    :     Dict        =						do_normalize
        UpperCamelCase__    :     List[Any]        =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCamelCase__    :     Optional[Any]        =						image_std if image_std is not None else IMAGENET_STANDARD_STD
        UpperCamelCase__    :     List[Any]        =						include_top
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     str							,     SCREAMING_SNAKE_CASE      :     np.ndarray							,     SCREAMING_SNAKE_CASE      :     Dict[str, int]							,     SCREAMING_SNAKE_CASE      :     PILImageResampling = PIL.Image.NEAREST							,     SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None							,     **SCREAMING_SNAKE_CASE      :     Optional[int]							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[int]        =						get_size_dict(SCREAMING_SNAKE_CASE		)
        if "height" not in size or "width" not in size:
             raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'		)
        return resize(
            SCREAMING_SNAKE_CASE							,     size=(size["height"], size["width"])							,     resample=SCREAMING_SNAKE_CASE							,     data_format=SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     np.ndarray							,     SCREAMING_SNAKE_CASE      :     Dict[str, int]							,     SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None							,     **SCREAMING_SNAKE_CASE      :     Dict							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						get_size_dict(SCREAMING_SNAKE_CASE		)
        if "height" not in size or "width" not in size:
             raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'		)
        return center_crop(SCREAMING_SNAKE_CASE							,     size=(size["height"], size["width"])							,     data_format=SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     np.ndarray							,     SCREAMING_SNAKE_CASE      :     Union[int, float]							,     SCREAMING_SNAKE_CASE      :     bool = True							,     SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None							,     **SCREAMING_SNAKE_CASE      :     List[Any]							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if offset:
             UpperCamelCase__    :     Optional[Any]        =						(image - 1_27.5) * scale
             if data_format is not None:
                  UpperCamelCase__    :     int        =						to_channel_dimension_format(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
             UpperCamelCase__    :     Any        =						rescaled_image.astype(np.floataa		)
        else:
             UpperCamelCase__    :     str        =						rescale(SCREAMING_SNAKE_CASE							,     scale=SCREAMING_SNAKE_CASE							,     data_format=SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE		)
        return rescaled_image
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Dict							,     SCREAMING_SNAKE_CASE      :     np.ndarray							,     SCREAMING_SNAKE_CASE      :     Union[float, List[float]]							,     SCREAMING_SNAKE_CASE      :     Union[float, List[float]]							,     SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None							,     **SCREAMING_SNAKE_CASE      :     str							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return normalize(SCREAMING_SNAKE_CASE							,     mean=SCREAMING_SNAKE_CASE							,     std=SCREAMING_SNAKE_CASE							,     data_format=SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     ImageInput							,     SCREAMING_SNAKE_CASE      :     bool = None							,     SCREAMING_SNAKE_CASE      :     Dict[str, int] = None							,     SCREAMING_SNAKE_CASE      :     Any=None							,     SCREAMING_SNAKE_CASE      :     bool = None							,     SCREAMING_SNAKE_CASE      :     Dict[str, int] = None							,     SCREAMING_SNAKE_CASE      :     bool = None							,     SCREAMING_SNAKE_CASE      :     float = None							,     SCREAMING_SNAKE_CASE      :     bool = None							,     SCREAMING_SNAKE_CASE      :     bool = None							,     SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None							,     SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None							,     SCREAMING_SNAKE_CASE      :     bool = None							,     SCREAMING_SNAKE_CASE      :     Optional[Union[str, TensorType]] = None							,     SCREAMING_SNAKE_CASE      :     ChannelDimension = ChannelDimension.FIRST							,     **SCREAMING_SNAKE_CASE      :     Tuple							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						do_resize if do_resize is not None else self.do_resize
        UpperCamelCase__    :     List[str]        =						resample if resample is not None else self.resample
        UpperCamelCase__    :     str        =						do_center_crop if do_center_crop is not None else self.do_center_crop
        UpperCamelCase__    :     Any        =						do_rescale if do_rescale is not None else self.do_rescale
        UpperCamelCase__    :     int        =						rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCamelCase__    :     Tuple        =						rescale_offset if rescale_offset is not None else self.rescale_offset
        UpperCamelCase__    :     Any        =						do_normalize if do_normalize is not None else self.do_normalize
        UpperCamelCase__    :     Any        =						image_mean if image_mean is not None else self.image_mean
        UpperCamelCase__    :     List[Any]        =						image_std if image_std is not None else self.image_std
        UpperCamelCase__    :     int        =						include_top if include_top is not None else self.include_top
  
        UpperCamelCase__    :     Optional[int]        =						size if size is not None else self.size
        UpperCamelCase__    :     int        =						get_size_dict(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						crop_size if crop_size is not None else self.crop_size
        UpperCamelCase__    :     Dict        =						get_size_dict(SCREAMING_SNAKE_CASE							,     param_name="crop_size"		)
  
        UpperCamelCase__    :     int        =						make_list_of_images(SCREAMING_SNAKE_CASE		)
  
        if not valid_images(SCREAMING_SNAKE_CASE		):
             raise ValueError(
                 "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                 "torch.Tensor, tf.Tensor or jax.ndarray."		)
  
        if do_resize and size is None or resample is None:
             raise ValueError("Size and resample must be specified if do_resize is True."		)
  
        if do_center_crop and crop_size is None:
             raise ValueError("Crop size must be specified if do_center_crop is True."		)
  
        if do_rescale and rescale_factor is None:
             raise ValueError("Rescale factor must be specified if do_rescale is True."		)
  
        if do_normalize and (image_mean is None or image_std is None):
             raise ValueError("Image mean and std must be specified if do_normalize is True."		)
  
        # All transformations expect numpy arrays.
        UpperCamelCase__    :     List[str]        =						[to_numpy_array(SCREAMING_SNAKE_CASE		) for image in images]
  
        if do_resize:
             UpperCamelCase__    :     int        =						[self.resize(image=SCREAMING_SNAKE_CASE							,     size=SCREAMING_SNAKE_CASE							,     resample=SCREAMING_SNAKE_CASE		) for image in images]
  
        if do_center_crop:
             UpperCamelCase__    :     List[Any]        =						[self.center_crop(image=SCREAMING_SNAKE_CASE							,     size=SCREAMING_SNAKE_CASE		) for image in images]
  
        if do_rescale:
             UpperCamelCase__    :     str        =						[self.rescale(image=SCREAMING_SNAKE_CASE							,     scale=SCREAMING_SNAKE_CASE							,     offset=SCREAMING_SNAKE_CASE		) for image in images]
  
        if do_normalize:
             UpperCamelCase__    :     Any        =						[self.normalize(image=SCREAMING_SNAKE_CASE							,     mean=SCREAMING_SNAKE_CASE							,     std=SCREAMING_SNAKE_CASE		) for image in images]
  
        if include_top:
             UpperCamelCase__    :     str        =						[self.normalize(image=SCREAMING_SNAKE_CASE							,     mean=[0, 0, 0]							,     std=SCREAMING_SNAKE_CASE		) for image in images]
  
        UpperCamelCase__    :     List[Any]        =						[to_channel_dimension_format(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		) for image in images]
  
        UpperCamelCase__    :     List[Any]        =						{"pixel_values": images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE							,     tensor_type=SCREAMING_SNAKE_CASE		)
  
style_context: 
  
  
  
  
  
  
  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     __a						(					A__ ,     unittest.TestCase ):
   _lowerCAmelCase : str 				=				MPNetTokenizer
   _lowerCAmelCase : List[str] 				=				MPNetTokenizerFast
   _lowerCAmelCase : List[Any] 				=				True
   _lowerCAmelCase : Union[str, Any] 				=				True
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().setUp()
  
        UpperCamelCase__    :     List[Any]        =						[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        UpperCamelCase__    :     int        =						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       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						"UNwant\u00E9d,running"
        UpperCamelCase__    :     List[str]        =						"unwanted, running"
        return input_text, output_text
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						self.tokenizer_class(self.vocab_file		)
  
        UpperCamelCase__    :     Any        =						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       __lowercase      (	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Union[str, Any]        =						self.tokenizer_class.from_pretrained("microsoft/mpnet-base"		)
  
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("sequence builders"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("multi-sequence build"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Tuple        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						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]