






'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=CanineTokenizer
      __a  					=False




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         super().setUp()
         __a				     =    CanineTokenizer()
         tokenizer.save_pretrained(self.tmpdirname      )




      @cached_property
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         return CanineTokenizer.from_pretrained('google/canine-s'      )




      def  __UpperCamelCase							(  self ,   **lowerCamelCase      )							->CanineTokenizer:



         '''simple docstring'''
         __a				     =    self.tokenizer_class.from_pretrained(self.tmpdirname ,   **lowerCamelCase      )
         __a				     =    1024
         return tokenizer




      @require_torch
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.canine_tokenizer
         __a				     =    ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
         # fmt: off
         __a				     =    [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
         # fmt: on
         __a				     =    tokenizer(lowerCamelCase ,   padding=lowerCamelCase ,   return_tensors='pt'      )
         self.assertIsInstance(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    list(batch.input_ids.numpy()[0]      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         self.assertEqual((2, 39) ,   batch.input_ids.shape      )
         self.assertEqual((2, 39) ,   batch.attention_mask.shape      )




      @require_torch
      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    self.canine_tokenizer
         __a				     =    ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
         __a				     =    tokenizer(lowerCamelCase ,   padding=lowerCamelCase ,   return_tensors='pt'      )
         # check if input_ids, attention_mask and token_type_ids are returned
         self.assertIn('input_ids' ,   lowerCamelCase      )
         self.assertIn('attention_mask' ,   lowerCamelCase      )
         self.assertIn('token_type_ids' ,   lowerCamelCase      )




      @require_torch
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.canine_tokenizer
         __a				     =    [
             'What\'s the weater?',
             'It\'s about 25 degrees.',
         ]
         __a				     =    tokenizer(
             text_target=lowerCamelCase ,   max_length=32 ,   padding='max_length' ,   truncation=lowerCamelCase ,   return_tensors='pt'      )
         self.assertEqual(32 ,   targets['input_ids'].shape[1]      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         # safety check on max_len default value so we are sure the test works
         __a				     =    self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               self.assertNotEqual(tokenizer.model_max_length ,   42      )

        # Now let's start the test
         __a				     =    self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               # Isolate this from the other tests because we save additional tokens/etc
               __a				     =    tempfile.mkdtemp()

               __a				     =    ' He is very happy, UNwant\u00E9d,running'
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               tokenizer.save_pretrained(lowerCamelCase      )

               __a				     =    tokenizer.__class__.from_pretrained(lowerCamelCase      )
               __a				     =    after_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

               shutil.rmtree(lowerCamelCase      )

         __a				     =    self.get_tokenizers(model_max_length=42      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               # Isolate this from the other tests because we save additional tokens/etc
               __a				     =    tempfile.mkdtemp()

               __a				     =    ' He is very happy, UNwant\u00E9d,running'

               __a				     =    tokenizer.additional_special_tokens

               # We can add a new special token for Canine as follows:
               __a				     =    chr(0xe007      )
               additional_special_tokens.append(lowerCamelCase      )
               tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}      )
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               tokenizer.save_pretrained(lowerCamelCase      )

               __a				     =    tokenizer.__class__.from_pretrained(lowerCamelCase      )
               __a				     =    after_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
               self.assertIn(lowerCamelCase ,   after_tokenizer.additional_special_tokens      )
               self.assertEqual(after_tokenizer.model_max_length ,   42      )

               __a				     =    tokenizer.__class__.from_pretrained(lowerCamelCase ,   model_max_length=43      )
               self.assertEqual(tokenizer.model_max_length ,   43      )

               shutil.rmtree(lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=lowerCamelCase      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a     ,			__a				     =    self.get_clean_sequence(lowerCamelCase      )

               # a special token for Canine can be defined as follows:
               __a				     =    0xe005
               __a				     =    chr(lowerCamelCase      )

               tokenizer.add_special_tokens({'cls_token': special_token}      )
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               self.assertEqual(len(lowerCamelCase      ) ,   1      )

               __a				     =    tokenizer.decode(ids + encoded_special_token ,   clean_up_tokenization_spaces=lowerCamelCase      )
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )

               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               self.assertEqual(lowerCamelCase ,   input_encoded + special_token_id      )

               __a				     =    tokenizer.decode(lowerCamelCase ,   skip_special_tokens=lowerCamelCase      )
               self.assertTrue(special_token not in decoded      )




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=lowerCamelCase      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    chr(0xe005      )
               __a				     =    chr(0xe006      )

               # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
               tokenizer.add_tokens([SPECIAL_TOKEN_1] ,   special_tokens=lowerCamelCase      )
               # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
               # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
               tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]}      )

               __a				     =    tokenizer.tokenize(lowerCamelCase      )
               __a				     =    tokenizer.tokenize(lowerCamelCase      )

               self.assertEqual(len(lowerCamelCase      ) ,   1      )
               self.assertEqual(len(lowerCamelCase      ) ,   1      )
               self.assertEqual(token_a[0] ,   lowerCamelCase      )
               self.assertEqual(token_a[0] ,   lowerCamelCase      )




      @require_tokenizers
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=lowerCamelCase      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               # a special token for Canine can be defined as follows:
               __a				     =    0xe006
               __a				     =    chr(lowerCamelCase      )

               __a				     =    AddedToken(lowerCamelCase ,   lstrip=lowerCamelCase      )
               tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}      )

               with tempfile.TemporaryDirectory() as tmp_dir_name:
                  tokenizer.save_pretrained(lowerCamelCase      )
                  tokenizer.from_pretrained(lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    []
         if self.test_slow_tokenizer:
            tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())      )

         if self.test_rust_tokenizer:
            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())      )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
            with tempfile.TemporaryDirectory() as tmp_dir:
               tokenizer_utils.save_pretrained(lowerCamelCase      )

               with open(os.path.join(lowerCamelCase ,   'special_tokens_map.json'      ) ,   encoding='utf-8'      ) as json_file:
                  __a				     =    json.load(lowerCamelCase      )

               with open(os.path.join(lowerCamelCase ,   'tokenizer_config.json'      ) ,   encoding='utf-8'      ) as json_file:
                  __a				     =    json.load(lowerCamelCase      )

               # a special token for Canine can be defined as follows:
               __a				     =    0xe006
               __a				     =    chr(lowerCamelCase      )

               __a				     =    [new_token_a]
               __a				     =    [new_token_a]

               with open(os.path.join(lowerCamelCase ,   'special_tokens_map.json'      ) ,   'w' ,   encoding='utf-8'      ) as outfile:
                  json.dump(lowerCamelCase ,   lowerCamelCase      )
               with open(os.path.join(lowerCamelCase ,   'tokenizer_config.json'      ) ,   'w' ,   encoding='utf-8'      ) as outfile:
                  json.dump(lowerCamelCase ,   lowerCamelCase      )

               # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
               # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
               # "special_tokens_map.json" files
               __a				     =    tokenizer_class.from_pretrained(lowerCamelCase ,   extra_ids=0      )
               self.assertIn(lowerCamelCase ,   tokenizer_without_change_in_init.additional_special_tokens      )
               # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
               self.assertEqual(
                   [new_token_a] ,   tokenizer_without_change_in_init.convert_ids_to_tokens(
                       tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]      )      ) ,   )

               __a				     =    0xe007
               __a				     =    chr(lowerCamelCase      )
               # Now we test that we can change the value of additional_special_tokens in the from_pretrained
               __a				     =    [AddedToken(lowerCamelCase ,   lstrip=lowerCamelCase      )]
               __a				     =    tokenizer_class.from_pretrained(
                   lowerCamelCase ,   additional_special_tokens=lowerCamelCase ,   extra_ids=0      )

               self.assertIn(lowerCamelCase ,   tokenizer.additional_special_tokens      )
               # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
               self.assertEqual(
                   [new_token_a] ,   tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]      )      )      )




      @require_tokenizers
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=lowerCamelCase      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    'hello world'
               if self.space_between_special_tokens:
                  __a				     =    '[CLS] hello world [SEP]'
               else:
                  __a				     =    input
               __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
               __a				     =    tokenizer.decode(lowerCamelCase ,   spaces_between_special_tokens=self.space_between_special_tokens      )
               self.assertIn(lowerCamelCase ,   [output, output.lower()]      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    [
                   'bos_token',
                   'eos_token',
                   'unk_token',
                   'sep_token',
                   'pad_token',
                   'cls_token',
                   'mask_token',
               ]

         __a				     =    'a'
         __a				     =    ord(lowerCamelCase      )

         for attr in attributes_list:
            setattr(lowerCamelCase ,   attr + '_id' ,   lowerCamelCase      )
            self.assertEqual(getattr(lowerCamelCase ,   lowerCamelCase      ) ,   lowerCamelCase      )
            self.assertEqual(getattr(lowerCamelCase ,   attr + '_id'      ) ,   lowerCamelCase      )

            setattr(lowerCamelCase ,   attr + '_id' ,   lowerCamelCase      )
            self.assertEqual(getattr(lowerCamelCase ,   lowerCamelCase      ) ,   lowerCamelCase      )
            self.assertEqual(getattr(lowerCamelCase ,   attr + '_id'      ) ,   lowerCamelCase      )

         setattr(lowerCamelCase ,   'additional_special_tokens_ids' ,   []      )
         self.assertListEqual(getattr(lowerCamelCase ,   'additional_special_tokens'      ) ,   []      )
         self.assertListEqual(getattr(lowerCamelCase ,   'additional_special_tokens_ids'      ) ,   []      )

         __a				     =    0xe006
         __a				     =    chr(lowerCamelCase      )
         setattr(lowerCamelCase ,   'additional_special_tokens_ids' ,   [additional_special_token_id]      )
         self.assertListEqual(getattr(lowerCamelCase ,   'additional_special_tokens'      ) ,   [additional_special_token]      )
         self.assertListEqual(getattr(lowerCamelCase ,   'additional_special_tokens_ids'      ) ,   [additional_special_token_id]      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         pass






      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         pass







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Dict      			=      ["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      [
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __UpperCamelCase      :       List[Any]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)