





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__      :     str         =		'pt'
elif is_tf_available():
      a__      :     Optional[int]         =		'tf'
else:
      a__      :     List[Any]         =		'jax'





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE     ,		unittest.TestCase ):
  __lowerCAmelCase   :						Dict							=   PerceiverTokenizer
  __lowerCAmelCase   :						str							=   False


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       super().setUp()
       A_          =       PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname )


  @cached_property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case=False ,__snake_case=2_0 ,__snake_case=5 ):



       """simple docstring"""





       A_          =       []
       for i in range(len(__snake_case ) ):
            try:
                 A_          =       tokenizer.decode([i] ,clean_up_tokenization_spaces=__snake_case )
            except UnicodeDecodeError:
                 pass
            toks.append((i, tok) )

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

       # Ensure consistency
       A_          =       tokenizer.decode(__snake_case ,clean_up_tokenization_spaces=__snake_case )
       if " " not in output_txt and len(__snake_case ) > 1:
            A_          =       (
                tokenizer.decode([toks_ids[0]] ,clean_up_tokenization_spaces=__snake_case )
                + ''' '''
                + tokenizer.decode(toks_ids[1:] ,clean_up_tokenization_spaces=__snake_case )
            )
       if with_prefix_space:
            A_          =       ''' ''' + output_txt
       A_          =       tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
       return output_txt, output_ids


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.perceiver_tokenizer
       A_          =       '''Unicode €.'''
       A_          =       tokenizer(__snake_case )
       A_          =       [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'''] ,__snake_case )

       # decoding
       A_          =       tokenizer.decode(__snake_case )
       self.assertEqual(__snake_case ,'''[CLS]Unicode €.[SEP]''' )

       A_          =       tokenizer('''e è é ê ë''' )
       A_          =       [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'''] ,__snake_case )
       # decoding
       A_          =       tokenizer.decode(__snake_case )
       self.assertEqual(__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 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.perceiver_tokenizer
       A_          =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       # fmt: off
       A_          =       [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
       A_          =       tokenizer(__snake_case ,padding=__snake_case ,return_tensors=__snake_case )
       self.assertIsInstance(__snake_case ,__snake_case )

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

       self.assertListEqual(__snake_case ,__snake_case )

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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.perceiver_tokenizer
       A_          =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       A_          =       tokenizer(__snake_case ,padding=__snake_case ,return_tensors=__snake_case )
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn('''input_ids''' ,__snake_case )
       self.assertIn('''attention_mask''' ,__snake_case )
       self.assertNotIn('''decoder_input_ids''' ,__snake_case )
       self.assertNotIn('''decoder_attention_mask''' ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.perceiver_tokenizer
       A_          =       [
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       A_          =       tokenizer(
           text_target=__snake_case ,max_length=3_2 ,padding='''max_length''' ,truncation=__snake_case ,return_tensors=__snake_case )
       self.assertEqual(3_2 ,targets['''input_ids'''].shape[1] )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       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
       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(__snake_case ,add_special_tokens=__snake_case )
                 tokenizer.save_pretrained(__snake_case )

                 A_          =       tokenizer.__class__.from_pretrained(__snake_case )
                 A_          =       after_tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
                 self.assertListEqual(__snake_case ,__snake_case )

                 shutil.rmtree(__snake_case )

       A_          =       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
                 A_          =       tempfile.mkdtemp()

                 A_          =       ''' He is very happy, UNwant\u00E9d,running'''
                 tokenizer.add_tokens(['''bim''', '''bambam'''] )
                 A_          =       tokenizer.additional_special_tokens
                 additional_special_tokens.append('''new_additional_special_token''' )
                 tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
                 A_          =       tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
                 tokenizer.save_pretrained(__snake_case )

                 A_          =       tokenizer.__class__.from_pretrained(__snake_case )
                 A_          =       after_tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
                 self.assertListEqual(__snake_case ,__snake_case )
                 self.assertIn('''new_additional_special_token''' ,after_tokenizer.additional_special_tokens )
                 self.assertEqual(after_tokenizer.model_max_length ,4_2 )

                 A_          =       tokenizer.__class__.from_pretrained(__snake_case ,model_max_length=4_3 )
                 self.assertEqual(tokenizer.model_max_length ,4_3 )

                 shutil.rmtree(__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """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(__snake_case )

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

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

                 A_          =       [f'<extra_id_{i}>' for i in range(1_2_5 )]

                 A_          =       added_tokens_extra_ids + [
                     '''an_additional_special_token'''
                 ]
                 A_          =       added_tokens_extra_ids + [
                     '''an_additional_special_token'''
                 ]

                 with open(os.path.join(__snake_case ,'''special_tokens_map.json''' ) ,'''w''' ,encoding='''utf-8''' ) as outfile:
                      json.dump(__snake_case ,__snake_case )
                 with open(os.path.join(__snake_case ,'''tokenizer_config.json''' ) ,'''w''' ,encoding='''utf-8''' ) as outfile:
                      json.dump(__snake_case ,__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
                 A_          =       tokenizer_class.from_pretrained(
                     __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
                 A_          =       added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' ,lstrip=__snake_case )]
                 A_          =       tokenizer_class.from_pretrained(
                     __snake_case ,additional_special_tokens=__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 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_tokenizers(fast=__snake_case ,do_lower_case=__snake_case )
       for tokenizer in tokenizers:
            with self.subTest(f'{tokenizer.__class__.__name__}' ):
                 A_          =       ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                 A_          =       tokenizer.convert_tokens_to_string(__snake_case )

                 self.assertIsInstance(__snake_case ,__snake_case )










from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def      UpperCAmelCase_    (    )	->				Optional[Any]:
     '''simple docstring'''


     A_          =       HfArgumentParser(_UpperCAmelCase				)
     A_          =       parser.parse_args_into_dataclasses()[0]
     A_          =       TensorFlowBenchmark(args=_UpperCAmelCase				)
     try:
          A_          =       parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_          =       '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_          =       ''' '''.join(str(_UpperCAmelCase				).split(''' '''				)[:-1]				)
          A_          =       ''''''
          A_          =       eval(str(_UpperCAmelCase				).split(''' '''				)[-1]				)
          A_          =       []
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]				)
               else:
                    wrong_args.append(_UpperCAmelCase				)
          if len(_UpperCAmelCase				) > 0:
               A_          =       full_error_msg + begin_error_msg + str(_UpperCAmelCase				)
          raise ValueError(_UpperCAmelCase				)
     benchmark.run()


if __name__ == "__main__":
      main()



