

'''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():
    A_				  =							"pt"
elif is_tf_available():
    A_				  =							"tf"
else:
    A_				  =							"jax"





class 							_snake_case       (				A_		,     unittest.TestCase  ):
   _A				:   Dict			      =    PerceiverTokenizer
   _A				:   Any			      =    False
   def 			__UpperCamelCase  (       self	:					Union[str, Any]							):
     super().setUp()
     SCREAMING_SNAKE_CASE:List[str]  					=    PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname							)
   @cached_property
   def 			__UpperCamelCase  (       self	:					Dict							):
     return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"							)
   def 			__UpperCamelCase  (       self	:					int   ,**SCREAMING_SNAKE_CASE__	:					Optional[Any]							):
     return self.tokenizer_class.from_pretrained(self.tmpdirname   ,**_lowerCamelCase							)
   def 			__UpperCamelCase  (       self	:					int   ,SCREAMING_SNAKE_CASE__	:					Dict   ,SCREAMING_SNAKE_CASE__	:					List[str]=False   ,SCREAMING_SNAKE_CASE__	:					Optional[int]=20   ,SCREAMING_SNAKE_CASE__	:					List[str]=5							):
     # 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.

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

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

     # Ensure consistency
     SCREAMING_SNAKE_CASE:Any  					=    tokenizer.decode(_lowerCamelCase   ,clean_up_tokenization_spaces=_lowerCamelCase							)
     if " " not in output_txt and len(_lowerCamelCase							) > 1:
       SCREAMING_SNAKE_CASE:str  					=    (
           tokenizer.decode([toks_ids[0]]   ,clean_up_tokenization_spaces=_lowerCamelCase							)
           + " "
           + tokenizer.decode(toks_ids[1:]   ,clean_up_tokenization_spaces=_lowerCamelCase							)
       )
     if with_prefix_space:
       SCREAMING_SNAKE_CASE:Any  					=    " " + output_txt
     SCREAMING_SNAKE_CASE:List[str]  					=    tokenizer.encode(_lowerCamelCase   ,add_special_tokens=_lowerCamelCase							)
     return output_txt, output_ids
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     SCREAMING_SNAKE_CASE:Dict  					=    self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE:Any  					=    "Unicode €."
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    tokenizer(_lowerCamelCase							)
     SCREAMING_SNAKE_CASE:Any  					=    [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
     self.assertEqual(encoded["input_ids"]   ,_lowerCamelCase							)

     # decoding
     SCREAMING_SNAKE_CASE:List[str]  					=    tokenizer.decode(_lowerCamelCase							)
     self.assertEqual(_lowerCamelCase   ,"[CLS]Unicode €.[SEP]"							)

     SCREAMING_SNAKE_CASE:Any  					=    tokenizer("e è é ê ë"							)
     SCREAMING_SNAKE_CASE:Tuple  					=    [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
     self.assertEqual(encoded["input_ids"]   ,_lowerCamelCase							)
     # decoding
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    tokenizer.decode(_lowerCamelCase							)
     self.assertEqual(_lowerCamelCase   ,"[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	:					List[str]							):
     SCREAMING_SNAKE_CASE:Optional[int]  					=    self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    ["A long paragraph for summarization.", "Another paragraph for summarization."]
     # fmt: off
     SCREAMING_SNAKE_CASE:Tuple  					=    [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
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    tokenizer(_lowerCamelCase   ,padding=_lowerCamelCase   ,return_tensors=_lowerCamelCase							)
     self.assertIsInstance(_lowerCamelCase   ,_lowerCamelCase							)

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

     self.assertListEqual(_lowerCamelCase   ,_lowerCamelCase							)

     self.assertEqual((2, 38)   ,batch.input_ids.shape							)
     self.assertEqual((2, 38)   ,batch.attention_mask.shape							)
   def 			__UpperCamelCase  (       self	:					Optional[int]							):
     SCREAMING_SNAKE_CASE:List[str]  					=    self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE:Any  					=    ["A long paragraph for summarization.", "Another paragraph for summarization."]
     SCREAMING_SNAKE_CASE:str  					=    tokenizer(_lowerCamelCase   ,padding=_lowerCamelCase   ,return_tensors=_lowerCamelCase							)
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn("input_ids"   ,_lowerCamelCase							)
     self.assertIn("attention_mask"   ,_lowerCamelCase							)
     self.assertNotIn("decoder_input_ids"   ,_lowerCamelCase							)
     self.assertNotIn("decoder_attention_mask"   ,_lowerCamelCase							)
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     SCREAMING_SNAKE_CASE:List[str]  					=    self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    [
         "Summary of the text.",
         "Another summary.",
     ]
     SCREAMING_SNAKE_CASE:Any  					=    tokenizer(
         text_target=_lowerCamelCase   ,max_length=32   ,padding="max_length"   ,truncation=_lowerCamelCase   ,return_tensors=_lowerCamelCase							)
     self.assertEqual(32   ,targets["input_ids"].shape[1]							)
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     # safety check on max_len default value so we are sure the test works
     SCREAMING_SNAKE_CASE: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
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    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
         SCREAMING_SNAKE_CASE:Optional[int]  					=    tempfile.mkdtemp()

         SCREAMING_SNAKE_CASE:str  					=    " He is very happy, UNwant\u00E9d,running"
         SCREAMING_SNAKE_CASE:List[Any]  					=    tokenizer.encode(_lowerCamelCase   ,add_special_tokens=_lowerCamelCase							)
         tokenizer.save_pretrained(_lowerCamelCase							)

         SCREAMING_SNAKE_CASE:Any  					=    tokenizer.__class__.from_pretrained(_lowerCamelCase							)
         SCREAMING_SNAKE_CASE:Any  					=    after_tokenizer.encode(_lowerCamelCase   ,add_special_tokens=_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase   ,_lowerCamelCase							)

         shutil.rmtree(_lowerCamelCase							)

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

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

         SCREAMING_SNAKE_CASE:int  					=    tokenizer.__class__.from_pretrained(_lowerCamelCase							)
         SCREAMING_SNAKE_CASE:List[str]  					=    after_tokenizer.encode(_lowerCamelCase   ,add_special_tokens=_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase   ,_lowerCamelCase							)
         self.assertIn("new_additional_special_token"   ,after_tokenizer.additional_special_tokens							)
         self.assertEqual(after_tokenizer.model_max_length   ,42							)

         SCREAMING_SNAKE_CASE:Dict  					=    tokenizer.__class__.from_pretrained(_lowerCamelCase   ,model_max_length=43							)
         self.assertEqual(tokenizer.model_max_length   ,43							)

         shutil.rmtree(_lowerCamelCase							)
   def 			__UpperCamelCase  (       self	:					Optional[int]							):
     SCREAMING_SNAKE_CASE: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(_lowerCamelCase							)

         with open(os.path.join(_lowerCamelCase   ,"special_tokens_map.json"							)   ,encoding="utf-8"							) as json_file:
           SCREAMING_SNAKE_CASE:List[Any]  					=    json.load(_lowerCamelCase							)

         with open(os.path.join(_lowerCamelCase   ,"tokenizer_config.json"							)   ,encoding="utf-8"							) as json_file:
           SCREAMING_SNAKE_CASE:Dict  					=    json.load(_lowerCamelCase							)

         SCREAMING_SNAKE_CASE:Tuple  					=    [F'''<extra_id_{i}>''' for i in range(125							)]

         SCREAMING_SNAKE_CASE:Union[str, Any]  					=    added_tokens_extra_ids + [
             "an_additional_special_token"
         ]
         SCREAMING_SNAKE_CASE:Optional[int]  					=    added_tokens_extra_ids + [
             "an_additional_special_token"
         ]

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

         # 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
         SCREAMING_SNAKE_CASE:List[Any]  					=    tokenizer_class.from_pretrained(
             _lowerCamelCase   ,)
         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
         SCREAMING_SNAKE_CASE:Union[str, Any]  					=    added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"   ,lstrip=_lowerCamelCase							)]
         SCREAMING_SNAKE_CASE:List[str]  					=    tokenizer_class.from_pretrained(
             _lowerCamelCase   ,additional_special_tokens=_lowerCamelCase   ,)

         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	:					Any							):
     SCREAMING_SNAKE_CASE:Any  					=    self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([178]							)   ,"�"							)
   def 			__UpperCamelCase  (       self	:					List[str]							):
     pass
   def 			__UpperCamelCase  (       self	:					Any							):
     pass
   def 			__UpperCamelCase  (       self	:					str							):
     pass
   def 			__UpperCamelCase  (       self	:					int							):
     pass







   def 			__UpperCamelCase  (       self	:					Any							):
     # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
     # strings and special added tokens as tokens
     SCREAMING_SNAKE_CASE:int  					=    self.get_tokenizers(fast=_lowerCamelCase   ,do_lower_case=_lowerCamelCase							)
     for tokenizer in tokenizers:
       with self.subTest(F'''{tokenizer.__class__.__name__}'''							):
         SCREAMING_SNAKE_CASE:List[Any]  					=    ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
         SCREAMING_SNAKE_CASE:Any  					=    tokenizer.convert_tokens_to_string(_lowerCamelCase							)

         self.assertIsInstance(_lowerCamelCase   ,_lowerCamelCase							)








"""simple docstring"""



from math import sqrt
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 1_00_00_00		)     ->					int:
  _snake_case					     =       0
  _snake_case					     =       0
  _snake_case					     =       42

  while num_cuboids <= limit:
    max_cuboid_size += 1
    for sum_shortest_sides in range(2 ,					2 * max_cuboid_size + 1		):
      if sqrt(sum_shortest_sides**2 + max_cuboid_size**2		).is_integer():
        num_cuboids += (
            min(__lowerCamelCase ,					sum_shortest_sides // 2		)
            - max(1 ,					sum_shortest_sides - max_cuboid_size		)
            + 1
        )

  return max_cuboid_size


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



