




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():
 lowerCAmelCase_						=			'pt'
elif is_tf_available():
 lowerCAmelCase_						=			'tf'
else:
 lowerCAmelCase_						=			'jax'
class 						_A (  _lowerCamelCase      ,       unittest.TestCase				):
  _UpperCamelCase       :      Optional[int]			=      PerceiverTokenizer
  _UpperCamelCase       :      Any			=      False






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






       """simple docstring"""







       super().setUp()
       lowercase :       int       =			PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname				)






  @cached_property
  def  __a      (  self		:     Tuple				)		->   Union[str, Any]:






       """simple docstring"""







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






  def  __a      (  self		:     List[str]				,						**_A		:     List[str]				)		->   PerceiverTokenizer:






       """simple docstring"""







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






  def  __a      (  self		:     Optional[Any]				,						_A		:     Optional[Any]				,						_A		:     List[str]=False				,						_A		:     Tuple=20				,						_A		:     List[Any]=5				)		->   Tuple[str, list]:






       """simple docstring"""







       lowercase :       List[Any]       =			[]
       for i in range(len(_A				)				):
            try:
                 lowercase :       Dict       =			tokenizer.decode([i]				,						clean_up_tokenization_spaces=_A				)
            except UnicodeDecodeError:
                 pass
            toks.append((i, tok)				)

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

       # Ensure consistency
       lowercase :       int       =			tokenizer.decode(_A				,						clean_up_tokenization_spaces=_A				)
       if " " not in output_txt and len(_A				) > 1:
            lowercase :       Dict       =			(
                tokenizer.decode([toks_ids[0]]				,						clean_up_tokenization_spaces=_A				)
                + ''' '''
                + tokenizer.decode(toks_ids[1:]				,						clean_up_tokenization_spaces=_A				)
            )
       if with_prefix_space:
            lowercase :       Optional[Any]       =			''' ''' + output_txt
       lowercase :       List[str]       =			tokenizer.encode(_A				,						add_special_tokens=_A				)
       return output_txt, output_ids






  def  __a      (  self		:     str				)		->   Dict:






       """simple docstring"""







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

       # decoding
       lowercase :       List[Any]       =			tokenizer.decode(_A				)
       self.assertEqual(_A				,						'''[CLS]Unicode €.[SEP]'''				)

       lowercase :       Union[str, Any]       =			tokenizer('''e è é ê ë'''				)
       lowercase :       Optional[int]       =			[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
       self.assertEqual(encoded['''input_ids''']				,						_A				)
       # decoding
       lowercase :       Dict       =			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  __a      (  self		:     Dict				)		->   int:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self.perceiver_tokenizer
       lowercase :       Optional[Any]       =			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       # fmt: off
       lowercase :       Dict       =			[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
       lowercase :       int       =			tokenizer(_A				,						padding=_A				,						return_tensors=_A				)
       self.assertIsInstance(_A				,						_A				)

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

       self.assertListEqual(_A				,						_A				)

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






  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       str       =			self.perceiver_tokenizer
       lowercase :       str       =			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       lowercase :       Dict       =			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  __a      (  self		:     str				)		->   str:






       """simple docstring"""







       lowercase :       Tuple       =			self.perceiver_tokenizer
       lowercase :       str       =			[
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       lowercase :       Optional[Any]       =			tokenizer(
           text_target=_A				,						max_length=32				,						padding='''max_length'''				,						truncation=_A				,						return_tensors=_A				)
       self.assertEqual(32				,						targets['''input_ids'''].shape[1]				)






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






       """simple docstring"""







       lowercase :       str       =			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
       lowercase :       Dict       =			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
                 lowercase :       Any       =			tempfile.mkdtemp()

                 lowercase :       Optional[Any]       =			''' He is very happy, UNwant\u00E9d,running'''
                 lowercase :       Dict       =			tokenizer.encode(_A				,						add_special_tokens=_A				)
                 tokenizer.save_pretrained(_A				)

                 lowercase :       Optional[int]       =			tokenizer.__class__.from_pretrained(_A				)
                 lowercase :       Tuple       =			after_tokenizer.encode(_A				,						add_special_tokens=_A				)
                 self.assertListEqual(_A				,						_A				)

                 shutil.rmtree(_A				)

       lowercase :       List[str]       =			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
                 lowercase :       Optional[Any]       =			tempfile.mkdtemp()

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

                 lowercase :       int       =			tokenizer.__class__.from_pretrained(_A				)
                 lowercase :       Any       =			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				,						42				)

                 lowercase :       int       =			tokenizer.__class__.from_pretrained(_A				,						model_max_length=43				)
                 self.assertEqual(tokenizer.model_max_length				,						43				)

                 shutil.rmtree(_A				)






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






       """simple docstring"""







       lowercase :       Optional[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:
                      lowercase :       Any       =			json.load(_A				)

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

                 lowercase :       Any       =			[f"""<extra_id_{i}>""" for i in range(125				)]

                 lowercase :       int       =			added_tokens_extra_ids + [
                     '''an_additional_special_token'''
                 ]
                 lowercase :       Optional[Any]       =			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
                 lowercase :       Optional[int]       =			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
                 lowercase :       Any       =			added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''				,						lstrip=_A				)]
                 lowercase :       Optional[int]       =			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  __a      (  self		:     Any				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Optional[int]       =			self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([178]				)				,						'''�'''				)






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






       """simple docstring"""







       pass






  def  __a      (  self		:     int				)		->   Dict:






       """simple docstring"""







       pass






  def  __a      (  self		:     List[str]				)		->   Tuple:






       """simple docstring"""







       pass






  def  __a      (  self		:     Optional[Any]				)		->   List[Any]:






       """simple docstring"""







       pass






  def  __a      (  self		:     Union[str, Any]				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.get_tokenizers(fast=_A				,						do_lower_case=_A				)
       for tokenizer in tokenizers:
            with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
                 lowercase :       Tuple       =			['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                 lowercase :       str       =			tokenizer.convert_tokens_to_string(_A				)

                 self.assertIsInstance(_A				,						_A				)





class 						_A :  # Public class to implement a graph






  def __init__(  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     list[list[bool]]				)		->   None:






       """simple docstring"""







       lowercase :       Tuple       =			row
       lowercase :       Union[str, Any]       =			col
       lowercase :       int       =			graph






  def  __a      (  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     list[list[bool]]				)		->   bool:






       """simple docstring"""







       return (
           0 <= i < self.ROW
           and 0 <= j < self.COL
           and not visited[i][j]
           and self.graph[i][j]
       )






  def  __a      (  self		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     list[list[bool]]				)		->   None:






       """simple docstring"""







       lowercase :       List[str]       =			[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
       lowercase :       Dict       =			[-1, 0, 1, -1, 1, -1, 0, 1]
       lowercase :       Dict       =			True  # Make those cells visited
       for k in range(8				):
            if self.is_safe(i + row_nbr[k]				,						j + col_nbr[k]				,						_A				):
                 self.diffs(i + row_nbr[k]				,						j + col_nbr[k]				,						_A				)






  def  __a      (  self		:     List[str]				)		->   int:  # And finally, count all islands.






       """simple docstring"""







       lowercase :       List[str]       =			[[False for j in range(self.COL				)] for i in range(self.ROW				)]
       lowercase :       Optional[Any]       =			0
       for i in range(self.ROW				):
            for j in range(self.COL				):
                 if visited[i][j] is False and self.graph[i][j] == 1:
                      self.diffs(_A				,						_A				,						_A				)
                      count += 1
       return count