
import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class        UpperCAmelCase_			( __lowerCamelCase  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		DebertaTokenizer
  a__           =		True
  a__           =		DebertaTokenizerFast
  def 							_lowercase							( self			: str    )			->	List[str]:







    """simple docstring"""






    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __magic_name__				     =       [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """[UNK]""",
    ]
    __magic_name__				     =       dict(zip(UpperCamelCase_			,							range(len(UpperCamelCase_    )    )    )    )
    __magic_name__				     =       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    __magic_name__				     =       {"""unk_token""": """[UNK]"""}

    __magic_name__				     =       os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["""vocab_file"""]    )
    __magic_name__				     =       os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["""merges_file"""]    )
    with open(self.vocab_file			,							"""w"""			,							encoding="""utf-8"""    ) as fp:
      fp.write(json.dumps(UpperCamelCase_    ) + """\n"""    )
    with open(self.merges_file			,							"""w"""			,							encoding="""utf-8"""    ) as fp:
      fp.write("""\n""".join(UpperCamelCase_    )    )
  def 							_lowercase							( self			: List[Any]			,							**UpperCamelCase__			: List[Any]    )			->	Union[str, Any]:







    """simple docstring"""






    kwargs.update(self.special_tokens_map    )
    return self.tokenizer_class.from_pretrained(self.tmpdirname			,							**UpperCamelCase_    )
  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: Dict    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """lower newer"""
    __magic_name__				     =       """lower newer"""
    return input_text, output_text
  def 							_lowercase							( self			: Optional[int]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       self.get_tokenizer()
    __magic_name__				     =       """lower newer"""
    __magic_name__				     =       ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
    __magic_name__				     =       tokenizer.tokenize(UpperCamelCase_    )
    self.assertListEqual(UpperCamelCase_			,							UpperCamelCase_    )

    __magic_name__				     =       tokens + [tokenizer.unk_token]
    __magic_name__				     =       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_    )			,							UpperCamelCase_    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       self.get_tokenizer()
    __magic_name__				     =       tokenizer("""Hello"""			,							"""World"""    )
    __magic_name__				     =       [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
    self.assertListEqual(tokd["""token_type_ids"""]			,							UpperCamelCase_    )
  @slow
  def 							_lowercase							( self			: Dict    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

    __magic_name__				     =       tokenizer.encode("""sequence builders"""			,							add_special_tokens=UpperCamelCase_    )
    __magic_name__				     =       tokenizer.encode("""multi-sequence build"""			,							add_special_tokens=UpperCamelCase_    )

    __magic_name__				     =       tokenizer.encode(
        """sequence builders"""			,							add_special_tokens=UpperCamelCase_			,							add_prefix_space=UpperCamelCase_    )
    __magic_name__				     =       tokenizer.encode(
        """sequence builders"""			,							"""multi-sequence build"""			,							add_special_tokens=UpperCamelCase_			,							add_prefix_space=UpperCamelCase_    )

    __magic_name__				     =       tokenizer.build_inputs_with_special_tokens(UpperCamelCase_    )
    __magic_name__				     =       tokenizer.build_inputs_with_special_tokens(UpperCamelCase_			,							UpperCamelCase_    )

    assert encoded_sentence == encoded_text_from_decode
    assert encoded_pair == encoded_pair_from_decode





  @slow
  def 							_lowercase							( self			: str    )			->	int:







    """simple docstring"""






    __magic_name__				     =       [self.tokenizer_class]
    if self.test_rust_tokenizer:
      tokenizer_classes.append(self.rust_tokenizer_class    )

    for tokenizer_class in tokenizer_classes:
      __magic_name__				     =       tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

      __magic_name__				     =       [
          """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
          """ALBERT incorporates two parameter reduction techniques""",
          """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
          """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
          """ vocabulary embedding.""",
      ]

      __magic_name__				     =       tokenizer(UpperCamelCase_			,							padding=UpperCamelCase_    )
      __magic_name__				     =       [tokenizer.decode(UpperCamelCase_			,							skip_special_tokens=UpperCamelCase_    ) for seq in encoding["""input_ids"""]]

      # fmt: off
      __magic_name__				     =       {
          """input_ids""": [
              [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
          ],
          """token_type_ids""": [
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
          ],
          """attention_mask""": [
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
          ]
      }
      # fmt: on

      __magic_name__				     =       [
          """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
          """ALBERT incorporates two parameter reduction techniques""",
          """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
          """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
          """ vocabulary embedding.""",
      ]

      self.assertDictEqual(encoding.data			,							UpperCamelCase_    )

      for expected, decoded in zip(UpperCamelCase_			,							UpperCamelCase_    ):
        self.assertEqual(UpperCamelCase_			,							UpperCamelCase_    )







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


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: int = 8    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						pad_size
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Tuple   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None   ):
             __lowerCamelCase,   __lowerCamelCase   =						get_image_size(UpperCamelCase_   )
             __lowerCamelCase   =						(old_height // size + 1) * size - old_height
             __lowerCamelCase   =						(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase_    ,       ((0, pad_height), (0, pad_width))    ,       mode="""symmetric"""    ,       data_format=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Any    ,       ):
             __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_pad if do_pad is not None else self.do_pad
             __lowerCamelCase   =						pad_size if pad_size is not None else self.pad_size

             __lowerCamelCase   =						make_list_of_images(UpperCamelCase_   )

             if not valid_images(UpperCamelCase_   ):
                    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.
             __lowerCamelCase   =						[to_numpy_array(UpperCamelCase_   ) for image in images]

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

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

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )

