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_    					=       """pt"""
elif is_tf_available():
   a_    					=       """tf"""
else:
   a_    					=       """jax"""





class 		_lowercase						(					_lowerCamelCase						,      unittest.TestCase       ):
 lowercase				     =   PerceiverTokenizer
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Any:

  """simple docstring"""





  super().setUp()
  UpperCamelCase_					:						Tuple =					PerceiverTokenizer()
  tokenizer.save_pretrained(self.tmpdirname			)

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

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      **snake_case	:							List[Any]			)   ->						PerceiverTokenizer:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							str   ,      snake_case	:							Optional[Any]=False   ,      snake_case	:							List[Any]=2_0   ,      snake_case	:							Optional[int]=5			)   ->						Tuple[str, list]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					[]
  for i in range(len(a_			)			):
   try:
    UpperCamelCase_					:						int =					tokenizer.decode([i]   ,      clean_up_tokenization_spaces=a_			)
   except UnicodeDecodeError:
    pass
   toks.append((i, tok)			)

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

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.perceiver_tokenizer
  UpperCamelCase_					:						Union[str, Any] =					"""Unicode €."""
  UpperCamelCase_					:						Dict =					tokenizer(a_			)
  UpperCamelCase_					:						Tuple =					[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
  self.assertEqual(encoded['input_ids']   ,      a_			)

  # decoding
  UpperCamelCase_					:						Optional[Any] =					tokenizer.decode(a_			)
  self.assertEqual(a_   ,      '[CLS]Unicode €.[SEP]'			)

  UpperCamelCase_					:						Optional[Any] =					tokenizer('e è é ê ë'			)
  UpperCamelCase_					:						List[str] =					[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
  self.assertEqual(encoded['input_ids']   ,      a_			)
  # decoding
  UpperCamelCase_					:						List[Any] =					tokenizer.decode(a_			)
  self.assertEqual(a_   ,      '[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[str]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					self.perceiver_tokenizer
  UpperCamelCase_					:						str =					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  # fmt: off
  UpperCamelCase_					:						List[str] =					[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
  # fmt: on
  UpperCamelCase_					:						str =					tokenizer(a_   ,      padding=a_   ,      return_tensors=a_			)
  self.assertIsInstance(a_   ,      a_			)

  if FRAMEWORK != "jax":
   UpperCamelCase_					:						Union[str, Any] =					list(batch.input_ids.numpy()[0]			)
  else:
   UpperCamelCase_					:						Union[str, Any] =					list(batch.input_ids.tolist()[0]			)

  self.assertListEqual(a_   ,      a_			)

  self.assertEqual((2, 3_8)   ,      batch.input_ids.shape			)
  self.assertEqual((2, 3_8)   ,      batch.attention_mask.shape			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.perceiver_tokenizer
  UpperCamelCase_					:						List[str] =					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  UpperCamelCase_					:						str =					tokenizer(a_   ,      padding=a_   ,      return_tensors=a_			)
  # check if input_ids are returned and no decoder_input_ids
  self.assertIn('input_ids'   ,      a_			)
  self.assertIn('attention_mask'   ,      a_			)
  self.assertNotIn('decoder_input_ids'   ,      a_			)
  self.assertNotIn('decoder_attention_mask'   ,      a_			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.perceiver_tokenizer
  UpperCamelCase_					:						str =					[
      """Summary of the text.""",
      """Another summary.""",
  ]
  UpperCamelCase_					:						Tuple =					tokenizer(
      text_target=a_   ,      max_length=3_2   ,      padding='max_length'   ,      truncation=a_   ,      return_tensors=a_			)
  self.assertEqual(3_2   ,      targets['input_ids'].shape[1]			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.get_tokenizers()
  for tokenizer in tokenizers:
   with self.subTest(f"{tokenizer.__class__.__name__}"			):
    self.assertNotEqual(tokenizer.model_max_length   ,      4_2			)

        # Now let's start the test
  UpperCamelCase_					:						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_					:						str =					tempfile.mkdtemp()

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

    UpperCamelCase_					:						Union[str, Any] =					tokenizer.__class__.from_pretrained(a_			)
    UpperCamelCase_					:						Union[str, Any] =					after_tokenizer.encode(a_   ,      add_special_tokens=a_			)
    self.assertListEqual(a_   ,      a_			)

    shutil.rmtree(a_			)

  UpperCamelCase_					:						Optional[Any] =					self.get_tokenizers(model_max_length=4_2			)
  for tokenizer in tokenizers:
   with self.subTest(f"{tokenizer.__class__.__name__}"			):
    # Isolate this from the other tests because we save additional tokens/etc
    UpperCamelCase_					:						Optional[int] =					tempfile.mkdtemp()

    UpperCamelCase_					:						str =					""" He is very happy, UNwant\u00E9d,running"""
    tokenizer.add_tokens(['bim', 'bambam']			)
    UpperCamelCase_					:						int =					tokenizer.additional_special_tokens
    additional_special_tokens.append('new_additional_special_token'			)
    tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}			)
    UpperCamelCase_					:						List[str] =					tokenizer.encode(a_   ,      add_special_tokens=a_			)
    tokenizer.save_pretrained(a_			)

    UpperCamelCase_					:						Optional[int] =					tokenizer.__class__.from_pretrained(a_			)
    UpperCamelCase_					:						Dict =					after_tokenizer.encode(a_   ,      add_special_tokens=a_			)
    self.assertListEqual(a_   ,      a_			)
    self.assertIn('new_additional_special_token'   ,      after_tokenizer.additional_special_tokens			)
    self.assertEqual(after_tokenizer.model_max_length   ,      4_2			)

    UpperCamelCase_					:						Optional[Any] =					tokenizer.__class__.from_pretrained(a_   ,      model_max_length=4_3			)
    self.assertEqual(tokenizer.model_max_length   ,      4_3			)

    shutil.rmtree(a_			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					[]
  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(a_			)

    with open(os.path.join(a_   ,      'special_tokens_map.json'			)   ,      encoding='utf-8'			) as json_file:
     UpperCamelCase_					:						Union[str, Any] =					json.load(a_			)

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

    UpperCamelCase_					:						Dict =					[f"<extra_id_{i}>" for i in range(1_2_5			)]

    UpperCamelCase_					:						Optional[Any] =					added_tokens_extra_ids + [
        """an_additional_special_token"""
    ]
    UpperCamelCase_					:						List[str] =					added_tokens_extra_ids + [
        """an_additional_special_token"""
    ]

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

    # 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(
        a_   ,      )
    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_					:						int =					added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'   ,      lstrip=a_			)]
    UpperCamelCase_					:						List[str] =					tokenizer_class.from_pretrained(
        a_   ,      additional_special_tokens=a_   ,      )

    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			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.perceiver_tokenizer
  self.assertEqual(tokenizer.decode([1_7_8]			)   ,      '�'			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Optional[Any]:

  """simple docstring"""





  pass

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Any:

  """simple docstring"""





  pass

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Dict:

  """simple docstring"""





  pass

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Dict:

  """simple docstring"""





  pass


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.get_tokenizers(fast=a_   ,      do_lower_case=a_			)
  for tokenizer in tokenizers:
   with self.subTest(f"{tokenizer.__class__.__name__}"			):
    UpperCamelCase_					:						Optional[int] =					["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
    UpperCamelCase_					:						Dict =					tokenizer.convert_tokens_to_string(a_			)

    self.assertIsInstance(a_   ,      a_			)








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_							: Optional[int]						 =	{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	[
       """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """IBertForMaskedLM""",
       """IBertForMultipleChoice""",
       """IBertForQuestionAnswering""",
       """IBertForSequenceClassification""",
       """IBertForTokenClassification""",
       """IBertModel""",
       """IBertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   A_							: str						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



