



import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      A:     Tuple										=	"pt"
elif is_tf_available():
      A:     Optional[Any]										=	"tf"
else:
      A:     Tuple										=	"jax"



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     int		=		PerceiverTokenizer
   __lowerCAmelCase						:     List[str]		=		False

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    super().setUp()
    UpperCAmelCase			:      Dict			=			PerceiverTokenizer()
    tokenizer.save_pretrained(self.tmpdirname		)

   @cached_property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   PerceiverTokenizer:





    '''simple docstring'''




    return self.tokenizer_class.from_pretrained(self.tmpdirname   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=20   ,  _SCREAMING_SNAKE_CASE=5		)						->   Tuple[str, list]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			[]
    for i in range(len(_SCREAMING_SNAKE_CASE		)		):
     try:
      UpperCAmelCase			:      List[str]			=			tokenizer.decode([i]   ,  clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
     except UnicodeDecodeError:
      pass
     toks.append((i, tok)		)

    UpperCAmelCase			:      int			=			list(filter(lambda _SCREAMING_SNAKE_CASE		: re.match(r"""^[ a-zA-Z]+$"""   ,  t[1]		)   ,  _SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      List[Any]			=			list(filter(lambda _SCREAMING_SNAKE_CASE		: [t[0]] == tokenizer.encode(t[1]   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)		)
    if max_length is not None and len(_SCREAMING_SNAKE_CASE		) > max_length:
     UpperCAmelCase			:      List[str]			=			toks[:max_length]
    if min_length is not None and len(_SCREAMING_SNAKE_CASE		) < min_length and len(_SCREAMING_SNAKE_CASE		) > 0:
     while len(_SCREAMING_SNAKE_CASE		) < min_length:
      UpperCAmelCase			:      Optional[int]			=			toks + toks
        # toks_str = [t[1] for t in toks]
    UpperCAmelCase			:      Dict			=			[t[0] for t in toks]

    # Ensure consistency
    UpperCAmelCase			:      int			=			tokenizer.decode(_SCREAMING_SNAKE_CASE   ,  clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
    if " " not in output_txt and len(_SCREAMING_SNAKE_CASE		) > 1:
     UpperCAmelCase			:      Tuple			=			(
         tokenizer.decode([toks_ids[0]]   ,  clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
         + """ """
         + tokenizer.decode(toks_ids[1:]   ,  clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
     )
    if with_prefix_space:
     UpperCAmelCase			:      Union[str, Any]			=			""" """ + output_txt
    UpperCAmelCase			:      str			=			tokenizer.encode(_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)
    return output_txt, output_ids

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.perceiver_tokenizer
    UpperCAmelCase			:      List[Any]			=			"""Unicode €."""
    UpperCAmelCase			:      List[str]			=			tokenizer(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
    self.assertEqual(encoded["""input_ids"""]   ,  _SCREAMING_SNAKE_CASE		)

    # decoding
    UpperCAmelCase			:      Union[str, Any]			=			tokenizer.decode(_SCREAMING_SNAKE_CASE		)
    self.assertEqual(_SCREAMING_SNAKE_CASE   ,  """[CLS]Unicode €.[SEP]"""		)

    UpperCAmelCase			:      List[Any]			=			tokenizer("""e è é ê ë"""		)
    UpperCAmelCase			:      Any			=			[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
    self.assertEqual(encoded["""input_ids"""]   ,  _SCREAMING_SNAKE_CASE		)
    # decoding
    UpperCAmelCase			:      str			=			tokenizer.decode(_SCREAMING_SNAKE_CASE		)
    self.assertEqual(_SCREAMING_SNAKE_CASE   ,  """[CLS]e è é ê ë[SEP]"""		)

    # encode/decode, but with `encode` instead of `__call__`
    self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""		)		)   ,  """[CLS]e è é ê ë[SEP]"""		)

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





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.perceiver_tokenizer
    UpperCAmelCase			:      List[Any]			=			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    # fmt: off
    UpperCAmelCase			:      List[str]			=			[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
    # fmt: on
    UpperCAmelCase			:      Dict			=			tokenizer(_SCREAMING_SNAKE_CASE   ,  padding=_SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    if FRAMEWORK != "jax":
     UpperCAmelCase			:      Any			=			list(batch.input_ids.numpy()[0]		)
    else:
     UpperCAmelCase			:      int			=			list(batch.input_ids.tolist()[0]		)

    self.assertListEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    self.assertEqual((2, 38)   ,  batch.input_ids.shape		)
    self.assertEqual((2, 38)   ,  batch.attention_mask.shape		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.perceiver_tokenizer
    UpperCAmelCase			:      Dict			=			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    UpperCAmelCase			:      int			=			tokenizer(_SCREAMING_SNAKE_CASE   ,  padding=_SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE		)
    # check if input_ids are returned and no decoder_input_ids
    self.assertIn("""input_ids"""   ,  _SCREAMING_SNAKE_CASE		)
    self.assertIn("""attention_mask"""   ,  _SCREAMING_SNAKE_CASE		)
    self.assertNotIn("""decoder_input_ids"""   ,  _SCREAMING_SNAKE_CASE		)
    self.assertNotIn("""decoder_attention_mask"""   ,  _SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.perceiver_tokenizer
    UpperCAmelCase			:      str			=			[
        """Summary of the text.""",
        """Another summary.""",
    ]
    UpperCAmelCase			:      Union[str, Any]			=			tokenizer(
        text_target=_SCREAMING_SNAKE_CASE   ,  max_length=32   ,  padding="""max_length"""   ,  truncation=_SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE		)
    self.assertEqual(32   ,  targets["""input_ids"""].shape[1]		)

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





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			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
    UpperCAmelCase			:      List[str]			=			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
      UpperCAmelCase			:      Union[str, Any]			=			tempfile.mkdtemp()

      UpperCAmelCase			:      Any			=			""" He is very happy, UNwant\u00E9d,running"""
      UpperCAmelCase			:      Optional[int]			=			tokenizer.encode(_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)
      tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE		)

      UpperCAmelCase			:      Dict			=			tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      List[str]			=			after_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)
      self.assertListEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

      shutil.rmtree(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			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
      UpperCAmelCase			:      int			=			tempfile.mkdtemp()

      UpperCAmelCase			:      Any			=			""" He is very happy, UNwant\u00E9d,running"""
      tokenizer.add_tokens(["""bim""", """bambam"""]		)
      UpperCAmelCase			:      Dict			=			tokenizer.additional_special_tokens
      additional_special_tokens.append("""new_additional_special_token"""		)
      tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}		)
      UpperCAmelCase			:      Optional[int]			=			tokenizer.encode(_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)
      tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE		)

      UpperCAmelCase			:      Dict			=			tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      str			=			after_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)
      self.assertListEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
      self.assertIn("""new_additional_special_token"""   ,  after_tokenizer.additional_special_tokens		)
      self.assertEqual(after_tokenizer.model_max_length   ,  42		)

      UpperCAmelCase			:      Optional[int]			=			tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE   ,  model_max_length=43		)
      self.assertEqual(tokenizer.model_max_length   ,  43		)

      shutil.rmtree(_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			[]
    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(_SCREAMING_SNAKE_CASE		)

      with open(os.path.join(_SCREAMING_SNAKE_CASE   ,  """special_tokens_map.json"""		)   ,  encoding="""utf-8"""		) as json_file:
       UpperCAmelCase			:      int			=			json.load(_SCREAMING_SNAKE_CASE		)

      with open(os.path.join(_SCREAMING_SNAKE_CASE   ,  """tokenizer_config.json"""		)   ,  encoding="""utf-8"""		) as json_file:
       UpperCAmelCase			:      List[str]			=			json.load(_SCREAMING_SNAKE_CASE		)

      UpperCAmelCase			:      str			=			[F"<extra_id_{i}>" for i in range(125		)]

      UpperCAmelCase			:      Optional[int]			=			added_tokens_extra_ids + [
          """an_additional_special_token"""
      ]
      UpperCAmelCase			:      Tuple			=			added_tokens_extra_ids + [
          """an_additional_special_token"""
      ]

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

      # 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
      UpperCAmelCase			:      Optional[Any]			=			tokenizer_class.from_pretrained(
          _SCREAMING_SNAKE_CASE   ,  )
      self.assertIn(
          """an_additional_special_token"""   ,  tokenizer_without_change_in_init.additional_special_tokens		)
      self.assertEqual(
          ["""an_additional_special_token"""]   ,  tokenizer_without_change_in_init.convert_ids_to_tokens(
              tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]		)		)   ,  )

      # Now we test that we can change the value of additional_special_tokens in the from_pretrained
      UpperCAmelCase			:      Union[str, Any]			=			added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""   ,  lstrip=_SCREAMING_SNAKE_CASE		)]
      UpperCAmelCase			:      Optional[int]			=			tokenizer_class.from_pretrained(
          _SCREAMING_SNAKE_CASE   ,  additional_special_tokens=_SCREAMING_SNAKE_CASE   ,  )

      self.assertIn("""a_new_additional_special_token"""   ,  tokenizer.additional_special_tokens		)
      self.assertEqual(
          ["""a_new_additional_special_token"""]   ,  tokenizer.convert_ids_to_tokens(
              tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]		)		)   ,  )

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			self.perceiver_tokenizer
    self.assertEqual(tokenizer.decode([178]		)   ,  """�"""		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    pass

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    pass

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





    '''simple docstring'''




    pass

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    pass




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.get_tokenizers(fast=_SCREAMING_SNAKE_CASE   ,  do_lower_case=_SCREAMING_SNAKE_CASE		)
    for tokenizer in tokenizers:
     with self.subTest(F"{tokenizer.__class__.__name__}"		):
      UpperCAmelCase			:      Union[str, Any]			=			["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
      UpperCAmelCase			:      Optional[Any]			=			tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)

      self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


A:     Dict										=	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

      A:     int										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
