


"""simple docstring"""



import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Optional[int]        =   LEDTokenizer
       __A			:      List[str]        =   LEDTokenizerFast
       __A			:      Union[str, Any]        =   True






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              super().setUp()
              lowercase		:Any       							=				[
                  '''l''',
                  '''o''',
                  '''w''',
                  '''e''',
                  '''r''',
                  '''s''',
                  '''t''',
                  '''i''',
                  '''d''',
                  '''n''',
                  '''\u0120''',
                  '''\u0120l''',
                  '''\u0120n''',
                  '''\u0120lo''',
                  '''\u0120low''',
                  '''er''',
                  '''\u0120lowest''',
                  '''\u0120newer''',
                  '''\u0120wider''',
                  '''<unk>''',
              ]
              lowercase		:List[str]       							=				dict(zip(snake_case__  ,     range(len(snake_case__				)				)				)				)
              lowercase		:Union[str, Any]       							=				['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
              lowercase		:Union[str, Any]       							=				{'''unk_token''': '''<unk>'''}

              lowercase		:List[str]       							=				os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES['''vocab_file''']				)
              lowercase		:int       							=				os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES['''merges_file''']				)
              with open(self.vocab_file  ,     '''w'''  ,     encoding='''utf-8'''				) as fp:
                     fp.write(json.dumps(snake_case__				) + '''\n'''				)
              with open(self.merges_file  ,     '''w'''  ,     encoding='''utf-8'''				) as fp:
                     fp.write('''\n'''.join(snake_case__				)				)






       def  __snake_case						(   self  :    List[str]  ,     **snake_case__  :    Optional[Any]				):

              '''simple docstring'''






              kwargs.update(self.special_tokens_map				)
              return self.tokenizer_class.from_pretrained(self.tmpdirname  ,     **snake_case__				)






       def  __snake_case						(   self  :    List[Any]  ,     **snake_case__  :    str				):

              '''simple docstring'''






              kwargs.update(self.special_tokens_map				)
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,     **snake_case__				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    List[str]				):

              '''simple docstring'''






              return "lower newer", "lower newer"






       @cached_property
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''				)






       @cached_property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''				)






       @require_torch
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              lowercase		:Dict       							=				[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowercase		:int       							=				tokenizer(snake_case__  ,     max_length=len(snake_case__				)  ,     padding=snake_case__  ,     return_tensors='''pt'''				)
                     self.assertIsInstance(snake_case__  ,     snake_case__				)

                     self.assertEqual((2, 9)  ,     batch.input_ids.shape				)
                     self.assertEqual((2, 9)  ,     batch.attention_mask.shape				)
                     lowercase		:Dict       							=				batch.input_ids.tolist()[0]
                     self.assertListEqual(snake_case__  ,     snake_case__				)






       @require_torch
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:str       							=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowercase		:Tuple       							=				tokenizer(snake_case__  ,     padding=snake_case__  ,     return_tensors='''pt'''				)
                     self.assertIn('''input_ids'''  ,     snake_case__				)
                     self.assertIn('''attention_mask'''  ,     snake_case__				)
                     self.assertNotIn('''labels'''  ,     snake_case__				)
                     self.assertNotIn('''decoder_attention_mask'''  ,     snake_case__				)






       @require_torch
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:str       							=				[
                  '''Summary of the text.''',
                  '''Another summary.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowercase		:Any       							=				tokenizer(text_target=snake_case__  ,     max_length=3_2  ,     padding='''max_length'''  ,     return_tensors='''pt'''				)
                     self.assertEqual(3_2  ,     targets['''input_ids'''].shape[1]				)






       @require_torch
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowercase		:List[str]       							=				tokenizer(
                         ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']  ,     padding=snake_case__  ,     truncation=snake_case__  ,     return_tensors='''pt'''				)
                     self.assertIsInstance(snake_case__  ,     snake_case__				)
                     self.assertEqual(batch.input_ids.shape  ,     (2, 5_1_2_2)				)






       @require_torch
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:str       							=				['''A long paragraph for summarization.''']
              lowercase		:Any       							=				[
                  '''Summary of the text.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowercase		:int       							=				tokenizer(snake_case__  ,     return_tensors='''pt'''				)
                     lowercase		:Optional[Any]       							=				tokenizer(text_target=snake_case__  ,     return_tensors='''pt'''				)
                     lowercase		:Union[str, Any]       							=				inputs['''input_ids''']
                     lowercase		:Dict       							=				targets['''input_ids''']
                     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
                     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
                     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
                     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)






       @require_torch
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowercase		:Dict       							=				['''Summary of the text.''', '''Another summary.''']
                     lowercase		:Union[str, Any]       							=				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                     lowercase		:Optional[Any]       							=				tokenizer(snake_case__  ,     padding=snake_case__				)
                     lowercase		:int       							=				[[0] * len(snake_case__				) for x in encoded_output['''input_ids''']]
                     lowercase		:Any       							=				tokenizer.pad(snake_case__				)
                     self.assertSequenceEqual(outputs['''global_attention_mask''']  ,     snake_case__				)






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              pass







       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                            lowercase		:Optional[Any]       							=				self.rust_tokenizer_class.from_pretrained(snake_case__  ,     **snake_case__				)
                            lowercase		:Any       							=				self.tokenizer_class.from_pretrained(snake_case__  ,     **snake_case__				)
                            lowercase		:Union[str, Any]       							=				'''A, <mask> AllenNLP sentence.'''
                            lowercase		:Optional[Any]       							=				tokenizer_r.encode_plus(snake_case__  ,     add_special_tokens=snake_case__  ,     return_token_type_ids=snake_case__				)
                            lowercase		:int       							=				tokenizer_p.encode_plus(snake_case__  ,     add_special_tokens=snake_case__  ,     return_token_type_ids=snake_case__				)
                            self.assertEqual(sum(tokens_r['''token_type_ids''']				)  ,     sum(tokens_p['''token_type_ids''']				)				)
                            self.assertEqual(
                                sum(tokens_r['''attention_mask''']				) / len(tokens_r['''attention_mask''']				)  ,     sum(tokens_p['''attention_mask''']				) / len(tokens_p['''attention_mask''']				)  ,     )

                            lowercase		:List[Any]       							=				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
                            lowercase		:Tuple       							=				tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']				)
                            self.assertSequenceEqual(tokens_p['''input_ids''']  ,     [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]				)
                            self.assertSequenceEqual(tokens_r['''input_ids''']  ,     [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]				)

                            self.assertSequenceEqual(
                                snake_case__  ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
                            self.assertSequenceEqual(
                                snake_case__  ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)





"""simple docstring"""



from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def       lowerCamelCase					()     ->							tuple[list[int], int]:
       lowercase		:Any       							=				[randint(-1000				,				1000) for i in range(10)]
       lowercase		:Any       							=				randint(-5000				,				5000)
       return (arr, r)


UpperCAmelCase      		=			make_dataset()






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, ...]:
       for triplet in permutations(a_				,				3):
              if sum(a_) == target:
                     return tuple(sorted(a_))
       return (0, 0, 0)






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, int, int]:
       arr.sort()
       lowercase		:Union[str, Any]       							=				len(a_)
       for i in range(n - 1):
              lowercase							,  lowercase		:Union[str, Any]       							=				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)






def       lowerCamelCase					()     ->							tuple[float, float]:
       lowercase		:int       							=				'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       lowercase		:Optional[Any]       							=				'''
triplet_sum1(*dataset)
'''
       lowercase		:Union[str, Any]       							=				'''
triplet_sum2(*dataset)
'''
       lowercase		:Dict       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       lowercase		:Optional[int]       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       return (min(a_), min(a_))


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase      		=			solution_times()
 print(F"""The time for naive implementation is {times[0]}.""")
 print(F"""The time for optimized implementation is {times[1]}.""")

