


'''simple docstring'''

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      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  DebertaTokenizer
  _lowerCamelCase		    =  True
  _lowerCamelCase		    =  DebertaTokenizerFast






  def   snake_case		(    self     ):







   """simple docstring"""
   super().setUp()

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

   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]     )
   lowerCamelCase_      =  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(_lowerCAmelCase     ) + "\n"     )
   with open(self.merges_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write("\n".join(_lowerCAmelCase     )     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   kwargs.update(self.special_tokens_map     )
   return self.tokenizer_class.from_pretrained(self.tmpdirname			,  **_lowerCAmelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "lower newer"
   lowerCamelCase_      =  "lower newer"
   return input_text, output_text






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  "lower newer"
   lowerCamelCase_      =  ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
   lowerCamelCase_      =  tokenizer.tokenize(_lowerCAmelCase     )
   self.assertListEqual(_lowerCAmelCase			,  _lowerCAmelCase     )

   lowerCamelCase_      =  tokens + [tokenizer.unk_token]
   lowerCamelCase_      =  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase     )			,  _lowerCAmelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_tokenizer()
   lowerCamelCase_      =  tokenizer("Hello"			,  "World"     )
   lowerCamelCase_      =  [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
   self.assertListEqual(tokd["token_type_ids"]			,  _lowerCAmelCase     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer_class.from_pretrained("microsoft/deberta-base"     )

   lowerCamelCase_      =  tokenizer.encode("sequence builders"			,  add_special_tokens=_lowerCAmelCase     )
   lowerCamelCase_      =  tokenizer.encode("multi-sequence build"			,  add_special_tokens=_lowerCAmelCase     )

   lowerCamelCase_      =  tokenizer.encode(
       "sequence builders"			,  add_special_tokens=_lowerCAmelCase			,  add_prefix_space=_lowerCAmelCase     )
   lowerCamelCase_      =  tokenizer.encode(
       "sequence builders"			,  "multi-sequence build"			,  add_special_tokens=_lowerCAmelCase			,  add_prefix_space=_lowerCAmelCase     )

   lowerCamelCase_      =  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase     )
   lowerCamelCase_      =  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase			,  _lowerCAmelCase     )

   assert encoded_sentence == encoded_text_from_decode
   assert encoded_pair == encoded_pair_from_decode







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.tokenizer_class]
   if self.test_rust_tokenizer:
    tokenizer_classes.append(self.rust_tokenizer_class     )

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

    lowerCamelCase_      =  [
        "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.",
    ]

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

    # fmt: off
    lowerCamelCase_      =  {
        "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

    lowerCamelCase_      =  [
        "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			,  _lowerCAmelCase     )

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









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor




class       __lowerCAmelCase				(							unittest.TestCase):






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Optional[Any]=3				,    _lowerCAmelCase:    List[Any]=18				,    _lowerCAmelCase:    int=30				,    _lowerCAmelCase:    Union[str, Any]=4_00				,    _lowerCAmelCase:    Any=True				,    _lowerCAmelCase:    Union[str, Any]=32				,    _lowerCAmelCase:    int=True				,    ):
         lowercase			:Any							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:Any							=  num_channels
         lowercase			:Optional[Any]							=  image_size
         lowercase			:Optional[Any]							=  min_resolution
         lowercase			:str							=  max_resolution
         lowercase			:str							=  do_resize
         lowercase			:Optional[int]							=  size_divisor
         lowercase			:Tuple							=  do_rescale






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return {
             "do_resize": self.do_resize,
             "size_divisor": self.size_divisor,
             "do_rescale": self.do_rescale,
         }







@require_torch
@require_vision
class       __lowerCAmelCase				(							lowerCAmelCase					,       unittest.TestCase):
       _a           =  GLPNImageProcessor if is_vision_available() else None






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:int							=  GLPNImageProcessingTester(self )






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return self.image_processor_tester.prepare_image_processor_dict()






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:Optional[Any]							=  self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "do_resize" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "size_divisor" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "resample" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "do_rescale" ) )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         pass






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         lowercase			:List[str]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    Image.Image )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:str							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         lowercase			:Union[str, Any]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase				,    numpify=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    np.ndarray )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:Optional[int]							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         # Initialize image_processing
         lowercase			:Optional[Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         lowercase			:List[str]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase				,    torchify=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    torch.Tensor )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:Dict							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )



