





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


snake_case_           =      logging.get_logger(__name__)

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   ['pixel_values']


      def __init__(self	:				Dict    ,      a__	:				bool = True    ,      a__	:				Union[int, float] = 1 / 255    ,      a__	:				bool = True    ,      a__	:				int = 8    ,      **a__	:				Optional[Any]    ,      ):





         """simple docstring"""



         super().__init__(**a__				)

         __snake_case								=       do_rescale
         __snake_case								=       rescale_factor
         __snake_case								=       do_pad
         __snake_case								=       pad_size


      def        a			(self	:				str    ,      a__	:				np.ndarray    ,      a__	:				float    ,      a__	:				Optional[Union[str, ChannelDimension]] = None    ,      **a__	:				Dict				):





         """simple docstring"""



         return rescale(a__    ,      scale=a__    ,      data_format=a__    ,      **a__				)


      def        a			(self	:				List[str]    ,      a__	:				np.ndarray    ,      a__	:				int    ,      a__	:				Optional[Union[str, ChannelDimension]] = None				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       get_image_size(a__				)
         __snake_case								=       (old_height // size + 1) * size - old_height
         __snake_case								=       (old_width // size + 1) * size - old_width

         return pad(a__    ,      ((0, pad_height), (0, pad_width))    ,      mode='''symmetric'''    ,      data_format=a__				)


      def        a			(self	:				Optional[Any]    ,      a__	:				ImageInput    ,      a__	:				Optional[bool] = None    ,      a__	:				Optional[float] = None    ,      a__	:				Optional[bool] = None    ,      a__	:				Optional[int] = None    ,      a__	:				Optional[Union[str, TensorType]] = None    ,      a__	:				Union[str, ChannelDimension] = ChannelDimension.FIRST    ,      **a__	:				int    ,      ):





         """simple docstring"""



         __snake_case								=       do_rescale if do_rescale is not None else self.do_rescale
         __snake_case								=       rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case								=       do_pad if do_pad is not None else self.do_pad
         __snake_case								=       pad_size if pad_size is not None else self.pad_size

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

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

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

         if do_pad:
            __snake_case								=       [self.pad(a__    ,      size=a__				) for image in images]

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

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








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    int							=   XLMTokenizer
      A_						:    Optional[Any]							=   False


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __snake_case								=       [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''w</w>''',
             '''r</w>''',
             '''t</w>''',
             '''lo''',
             '''low''',
             '''er</w>''',
             '''low</w>''',
             '''lowest</w>''',
             '''newer</w>''',
             '''wider</w>''',
             '''<unk>''',
         ]
         __snake_case								=       dict(zip(a__    ,      range(len(a__				)				)				)				)
         __snake_case								=       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']				)
         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''merges_file''']				)
         with open(self.vocab_file    ,      '''w'''				) as fp:
            fp.write(json.dumps(a__				)				)
         with open(self.merges_file    ,      '''w'''				) as fp:
            fp.write('''\n'''.join(a__				)				)


      def        a			(self	:				int    ,      a__	:				Any				):





         """simple docstring"""



         __snake_case								=       '''lower newer'''
         __snake_case								=       '''lower newer'''
         return input_text, output_text


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer(self.vocab_file    ,      self.merges_file				)

         __snake_case								=       '''lower'''
         __snake_case								=       ['''low''', '''er</w>''']
         __snake_case								=       tokenizer.tokenize(a__				)
         self.assertListEqual(a__    ,      a__				)

         __snake_case								=       tokens + ['''<unk>''']
         __snake_case								=       [14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__				)    ,      a__				)


      @slow
      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''				)

         __snake_case								=       tokenizer.encode('''sequence builders'''    ,      add_special_tokens=a__				)
         __snake_case								=       tokenizer.encode('''multi-sequence build'''    ,      add_special_tokens=a__				)

         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__				)
         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__    ,      a__				)

         assert encoded_sentence == [0] + text + [1]
         assert encoded_pair == [0] + text + [1] + text_a + [1]

