






"""simple docstring"""



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      ):
    UpperCAmelCase_					:	List[Any]      	=MPNetTokenizer
    UpperCAmelCase_					:	str      	=MPNetTokenizerFast
    UpperCAmelCase_					:	Union[str, Any]      	=True
    UpperCAmelCase_					:	int      	=True







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

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.tokenizer_class(self.vocab_file			)

      lowercase				       =			tokenizer.tokenize('UNwant\u00E9d,running'			)
      self.assertListEqual(_lowerCamelCase				,						['un', '##want', '##ed', ',', 'runn', '##ing']			)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase			)				,						[9, 6, 7, 1_2, 1_0, 1_1]			)







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

      lowercase				       =			tokenizer.encode('sequence builders'				,						add_special_tokens=_lowerCamelCase			)
      lowercase				       =			tokenizer.encode('multi-sequence build'				,						add_special_tokens=_lowerCamelCase			)

      lowercase				       =			tokenizer.build_inputs_with_special_tokens(_lowerCamelCase			)
      lowercase				       =			tokenizer.build_inputs_with_special_tokens(_lowerCamelCase				,						_lowerCamelCase			)

      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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_UpperCAmelCase    			=					logging.get_logger(__name__)


if is_vision_available():
  import PIL

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = ["pixel_values"]


  def __init__(					self	,		a__ = True	,		a__ = None	,		a__ = PILImageResampling.BILINEAR	,		a__ = True	,		a__ = False	,		a__ = True	,		a__ = True	,		a__ = 1 / 2_55	,		a__ = True	,		a__ = None	,		a__ = None	,		**a__	,		):
    super().__init__(**a__		)

    _lowerCamelCase      =						size if size is not None else {'''height''': 25_60, '''width''': 19_20}
    if isinstance(a__	,		(tuple, list)		):
      # The previous feature extractor size parameter was in (width, height) format
      _lowerCamelCase      =						size[::-1]
    _lowerCamelCase      =						get_size_dict(a__		)

    _lowerCamelCase      =						do_resize
    _lowerCamelCase      =						size
    _lowerCamelCase      =						resample
    _lowerCamelCase      =						do_thumbnail
    _lowerCamelCase      =						do_align_long_axis
    _lowerCamelCase      =						do_pad
    _lowerCamelCase      =						do_rescale
    _lowerCamelCase      =						rescale_factor
    _lowerCamelCase      =						do_normalize
    _lowerCamelCase      =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    _lowerCamelCase      =						image_std if image_std is not None else IMAGENET_STANDARD_STD


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = None		):
    _lowerCamelCase    ,    _lowerCamelCase      =						get_image_size(a__		)
    _lowerCamelCase    ,    _lowerCamelCase      =						size['''height'''], size['''width''']

    if (output_width < output_height and input_width > input_height) or (
        output_width > output_height and input_width < input_height
    ):
      _lowerCamelCase      =						np.rotaa(a__	,		3		)

    if data_format is not None:
      _lowerCamelCase      =						to_channel_dimension_format(a__	,		a__		)

    return image


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = False	,		a__ = None	,		):
    _lowerCamelCase    ,    _lowerCamelCase      =						size['''height'''], size['''width''']
    _lowerCamelCase    ,    _lowerCamelCase      =						get_image_size(a__		)

    _lowerCamelCase      =						output_width - input_width
    _lowerCamelCase      =						output_height - input_height

    if random_padding:
      _lowerCamelCase      =						np.random.randint(low=0	,		high=delta_height + 1		)
      _lowerCamelCase      =						np.random.randint(low=0	,		high=delta_width + 1		)
    else:
      _lowerCamelCase      =						delta_height // 2
      _lowerCamelCase      =						delta_width // 2

    _lowerCamelCase      =						delta_height - pad_top
    _lowerCamelCase      =						delta_width - pad_left

    _lowerCamelCase      =						((pad_top, pad_bottom), (pad_left, pad_right))
    return pad(a__	,		a__	,		data_format=a__		)


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''		)
    return self.pad_image(*a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = PILImageResampling.BICUBIC	,		a__ = None	,		**a__	,		):
    _lowerCamelCase    ,    _lowerCamelCase      =						get_image_size(a__		)
    _lowerCamelCase    ,    _lowerCamelCase      =						size['''height'''], size['''width''']

    # We always resize to the smallest of either the input or output size.
    _lowerCamelCase      =						min(a__	,		a__		)
    _lowerCamelCase      =						min(a__	,		a__		)

    if height == input_height and width == input_width:
      return image

    if input_height > input_width:
      _lowerCamelCase      =						int(input_width * height / input_height		)
    elif input_width > input_height:
      _lowerCamelCase      =						int(input_height * width / input_width		)

    return resize(
        a__	,		size=(height, width)	,		resample=a__	,		reducing_gap=2.0	,		data_format=a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = PILImageResampling.BICUBIC	,		a__ = None	,		**a__	,		):
    _lowerCamelCase      =						get_size_dict(a__		)
    _lowerCamelCase      =						min(size['''height''']	,		size['''width''']		)
    _lowerCamelCase      =						get_resize_output_image_size(a__	,		size=a__	,		default_to_square=a__		)
    _lowerCamelCase      =						resize(a__	,		size=a__	,		resample=a__	,		data_format=a__	,		**a__		)
    return resized_image


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = None	,		**a__	,		):
    return rescale(a__	,		scale=a__	,		data_format=a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		a__ = None	,		**a__	,		):
    return normalize(a__	,		mean=a__	,		std=a__	,		data_format=a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = False	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = ChannelDimension.FIRST	,		**a__	,		):
    _lowerCamelCase      =						do_resize if do_resize is not None else self.do_resize
    _lowerCamelCase      =						size if size is not None else self.size
    if isinstance(a__	,		(tuple, list)		):
      # Previous feature extractor had size in (width, height) format
      _lowerCamelCase      =						size[::-1]
    _lowerCamelCase      =						get_size_dict(a__		)
    _lowerCamelCase      =						resample if resample is not None else self.resample
    _lowerCamelCase      =						do_thumbnail if do_thumbnail is not None else self.do_thumbnail
    _lowerCamelCase      =						do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
    _lowerCamelCase      =						do_pad if do_pad is not None else self.do_pad
    _lowerCamelCase      =						do_rescale if do_rescale is not None else self.do_rescale
    _lowerCamelCase      =						rescale_factor if rescale_factor is not None else self.rescale_factor
    _lowerCamelCase      =						do_normalize if do_normalize is not None else self.do_normalize
    _lowerCamelCase      =						image_mean if image_mean is not None else self.image_mean
    _lowerCamelCase      =						image_std if image_std is not None else self.image_std

    _lowerCamelCase      =						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.'''		)

    if do_pad and size is None:
      raise ValueError('''Size must be specified if do_pad 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.
    _lowerCamelCase      =						[to_numpy_array(a__		) for image in images]

    if do_align_long_axis:
      _lowerCamelCase      =						[self.align_long_axis(a__	,		size=a__		) for image in images]

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

    if do_thumbnail:
      _lowerCamelCase      =						[self.thumbnail(image=a__	,		size=a__		) for image in images]

    if do_pad:
      _lowerCamelCase      =						[self.pad_image(image=a__	,		size=a__	,		random_padding=a__		) for image in images]

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

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

    _lowerCamelCase      =						[to_channel_dimension_format(a__	,		a__		) for image in images]

    _lowerCamelCase      =						{'''pixel_values''': images}
    return BatchFeature(data=a__	,		tensor_type=a__		)




