


"""simple docstring"""







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_       :       Optional[Any]      						=  """pt"""
elif is_tf_available():
       lowerCamelCase_       :       Optional[int]      						=  """tf"""
else:
       lowerCamelCase_       :       Any      						=  """jax"""






class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase							):






 """simple docstring"""




 __lowerCAmelCase			 =  PerceiverTokenizer
 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     super().setUp()
     a					=PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname						)


 @cached_property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					PerceiverTokenizer:
     return self.tokenizer_class.from_pretrained(self.tmpdirname			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=False			,   __A=20			,   __A=5						) ->					Tuple[str, list]:
     # XXX The default common tokenizer tests assume that every ID is decodable on its own.
     # This assumption is invalid for Perceiver because single bytes might not be
     # valid utf-8 (byte 128 for instance).
     # Here we're overriding the smallest possible method to provide
     # a clean sequence without making the same assumption.

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

     a					=list(filter(lambda __A						: re.match(r'''^[ a-zA-Z]+$'''			,   t[1]						)			,   __A						)						)
     a					=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:
         a					=toks[:max_length]
     if min_length is not None and len(__A						) < min_length and len(__A						) > 0:
         while len(__A						) < 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(__A			,   clean_up_tokenization_spaces=__A						)
     if " " not in output_txt and len(__A						) > 1:
         a					=(
             tokenizer.decode([toks_ids[0]]			,   clean_up_tokenization_spaces=__A						)
             + ''' '''
             + tokenizer.decode(toks_ids[1:]			,   clean_up_tokenization_spaces=__A						)
         )
     if with_prefix_space:
         a					=''' ''' + output_txt
     a					=tokenizer.encode(__A			,   add_special_tokens=__A						)
     return output_txt, output_ids


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=self.perceiver_tokenizer
     a					='''Unicode €.'''
     a					=tokenizer(__A						)
     a					=[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
     self.assertEqual(encoded['''input_ids''']			,   __A						)

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

     a					=tokenizer('''e è é ê ë'''						)
     a					=[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
     self.assertEqual(encoded['''input_ids''']			,   __A						)
     # decoding
     a					=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						) ->					Any:
     a					=self.perceiver_tokenizer
     a					=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     # fmt: off
     a					=[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
     a					=tokenizer(__A			,   padding=__A			,   return_tensors=__A						)
     self.assertIsInstance(__A			,   __A						)

     if FRAMEWORK != "jax":
         a					=list(batch.input_ids.numpy()[0]						)
     else:
         a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=self.perceiver_tokenizer
     a					=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     a					=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						) ->					List[Any]:
     a					=self.perceiver_tokenizer
     a					=[
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     a					=tokenizer(
         text_target=__A			,   max_length=32			,   padding='''max_length'''			,   truncation=__A			,   return_tensors=__A						)
     self.assertEqual(32			,   targets['''input_ids'''].shape[1]						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     # 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(__A			,   add_special_tokens=__A						)
             tokenizer.save_pretrained(__A						)

             a					=tokenizer.__class__.from_pretrained(__A						)
             a					=after_tokenizer.encode(__A			,   add_special_tokens=__A						)
             self.assertListEqual(__A			,   __A						)

             shutil.rmtree(__A						)

     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'''
             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(__A			,   add_special_tokens=__A						)
             tokenizer.save_pretrained(__A						)

             a					=tokenizer.__class__.from_pretrained(__A						)
             a					=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						)

             a					=tokenizer.__class__.from_pretrained(__A			,   model_max_length=43						)
             self.assertEqual(tokenizer.model_max_length			,   43						)

             shutil.rmtree(__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     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(__A						)

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

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

             a					=[f'''<extra_id_{i}>''' for i in range(125						)]

             a					=added_tokens_extra_ids + [
                 '''an_additional_special_token'''
             ]
             a					=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
             a					=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
             a					=added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''			,   lstrip=__A						)]
             a					=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						) ->					Optional[int]:
     a					=self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([178]						)			,   '''�'''						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     pass





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
     # strings and special added tokens as tokens
     a					=self.get_tokenizers(fast=__A			,   do_lower_case=__A						)
     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(__A						)

             self.assertIsInstance(__A			,   __A						)



"""simple docstring"""







def _A						(						lowercase = 2_00_00_00		):







    """simple docstring"""
    a					=[0 for i in range(n + 1		)]
    a					=1
    a					=1

    for i in range(2	,   int(n**0.5		) + 1		):
        if primality_list[i] == 0:
            for j in range(i * i	,   n + 1	,   lowercase		):
                a					=1
    a					=0
    for i in range(lowercase		):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


if __name__ == "__main__":
       print(F'{solution() = }')