






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		)








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]