

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    __snake_case	(     lowerCAmelCase__  ,			unittest.TestCase							):
  __lowerCAmelCase  :						List[Any]    			=	MPNetTokenizer
  __lowerCAmelCase  :						Union[str, Any]    			=	MPNetTokenizerFast
  __lowerCAmelCase  :						int    			=	True
  __lowerCAmelCase  :						List[Any]    			=	True

  def     lowerCAmelCase__  (       self):
      super().setUp()

      SCREAMING_SNAKE_CASE_						=    [
          '[UNK]',
          '[CLS]',
          '[SEP]',
          '[PAD]',
          '[MASK]',
          'want',
          '##want',
          '##ed',
          'wa',
          'un',
          'runn',
          '##ing',
          ',',
          'low',
          'lowest',
      ]
      SCREAMING_SNAKE_CASE_						=    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     lowerCAmelCase__  (       self    ,		_A):
      SCREAMING_SNAKE_CASE_						=    'UNwant\u00E9d,running'
      SCREAMING_SNAKE_CASE_						=    'unwanted, running'
      return input_text, output_text

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.tokenizer_class(self.vocab_file)

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize('UNwant\u00E9d,running')
      self.assertListEqual(_A    ,		['un', '##want', '##ed', ',', 'runn', '##ing'])
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)    ,		[9, 6, 7, 12, 10, 11])


  @slow
  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.tokenizer_class.from_pretrained('microsoft/mpnet-base')

      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('sequence builders'    ,		add_special_tokens=_A)
      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('multi-sequence build'    ,		add_special_tokens=_A)

      SCREAMING_SNAKE_CASE_						=    tokenizer.build_inputs_with_special_tokens(_A)
      SCREAMING_SNAKE_CASE_						=    tokenizer.build_inputs_with_special_tokens(_A    ,		_A)

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





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 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, logging


UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						Tuple    			=	['pixel_values']

  def __init__(       self    ,		_A = True    ,		_A = None    ,		_A = PILImageResampling.BILINEAR    ,		_A = True    ,		_A = 1 / 255    ,		_A = True    ,		_A = None    ,		_A = None    ,		**_A    ,		):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_						=    size if size is not None else {'height': 224, 'width': 224}
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)
      SCREAMING_SNAKE_CASE_						=    do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize
      SCREAMING_SNAKE_CASE_						=    size
      SCREAMING_SNAKE_CASE_						=    resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else IMAGENET_STANDARD_STD

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = PILImageResampling.BILINEAR    ,		_A = None    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)
      if "height" not in size or "width" not in size:
          raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""")
      return resize(
          _A    ,		size=(size['height'], size['width'])    ,		resample=_A    ,		data_format=_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = None    ,		**_A):
      return rescale(_A    ,		scale=_A    ,		data_format=_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A    ,		_A = None    ,		**_A    ,		):
      return normalize(_A    ,		mean=_A    ,		std=_A    ,		data_format=_A    ,		**_A)


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = ChannelDimension.FIRST    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    do_resize if do_resize is not None else self.do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale if do_rescale is not None else self.do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize if do_normalize is not None else self.do_normalize
      SCREAMING_SNAKE_CASE_						=    resample if resample is not None else self.resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor if rescale_factor is not None else self.rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else self.image_mean
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else self.image_std

      SCREAMING_SNAKE_CASE_						=    size if size is not None else self.size
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)

      SCREAMING_SNAKE_CASE_						=    make_list_of_images(_A)

      if not valid_images(_A):
          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:
          raise ValueError('Size must be specified if do_resize is True.')

      if do_rescale and rescale_factor is None:
          raise ValueError('Rescale factor must be specified if do_rescale is True.')

      # All transformations expect numpy arrays.
      SCREAMING_SNAKE_CASE_						=    [to_numpy_array(_A) for image in images]

      if do_resize:
          SCREAMING_SNAKE_CASE_						=    [self.resize(image=_A    ,		size=_A    ,		resample=_A) for image in images]

      if do_rescale:
          SCREAMING_SNAKE_CASE_						=    [self.rescale(image=_A    ,		scale=_A) for image in images]

      if do_normalize:
          SCREAMING_SNAKE_CASE_						=    [self.normalize(image=_A    ,		mean=_A    ,		std=_A) for image in images]

      SCREAMING_SNAKE_CASE_						=    [to_channel_dimension_format(_A    ,		_A) for image in images]

      SCREAMING_SNAKE_CASE_						=    {'pixel_values': images}
      return BatchFeature(data=_A    ,		tensor_type=_A)


