code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors
  
  
  
  
  
  
  @require_tokenizers
  class 			UpperCAmelCase_						(       a_ ,    unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				BartTokenizer
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				BartTokenizerFast
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				True
        __SCREAMING_SNAKE_CASE		:     Optional[Any]				  =				filter_roberta_detectors
  
  
  
        def 		snake_case_      (  self  :			int		):
             super().setUp()
             _UpperCAmelCase    :			Optional[Any]              =					[
                 '''l''',
                 '''o''',
                 '''w''',
                 '''e''',
                 '''r''',
                 '''s''',
                 '''t''',
                 '''i''',
                 '''d''',
                 '''n''',
                 '''\u0120''',
                 '''\u0120l''',
                 '''\u0120n''',
                 '''\u0120lo''',
                 '''\u0120low''',
                 '''er''',
                 '''\u0120lowest''',
                 '''\u0120newer''',
                 '''\u0120wider''',
                 '''<unk>''',
             ]
             _UpperCAmelCase    :			int              =					dict(zip(_snake_case  ,			range(len(_snake_case		)		)		)		)
             _UpperCAmelCase    :			Any              =					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
             _UpperCAmelCase    :			Dict              =					{'''unk_token''': '''<unk>'''}
  
             _UpperCAmelCase    :			Optional[int]              =					os.path.join(self.tmpdirname  ,			VOCAB_FILES_NAMES["vocab_file"]		)
             _UpperCAmelCase    :			Optional[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  :			str  ,			**A  :			Optional[int]		):
             kwargs.update(self.special_tokens_map		)
             return self.tokenizer_class.from_pretrained(self.tmpdirname  ,			**_snake_case		)
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]  ,			**A  :			int		):
             kwargs.update(self.special_tokens_map		)
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,			**_snake_case		)
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			int		):
             return "lower newer", "lower newer"
  
  
  
        @cached_property
        def 		snake_case_      (  self  :			Tuple		):
             return BartTokenizer.from_pretrained("facebook/bart-large"		)
  
  
  
        @cached_property
        def 		snake_case_      (  self  :			Any		):
             return BartTokenizerFast.from_pretrained("facebook/bart-large"		)
  
  
  
        @require_torch
        def 		snake_case_      (  self  :			Optional[int]		):
             _UpperCAmelCase    :			Union[str, Any]              =					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             _UpperCAmelCase    :			Optional[Any]              =					[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]:
                  _UpperCAmelCase    :			Dict              =					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		)
                  _UpperCAmelCase    :			List[Any]              =					batch.input_ids.tolist()[0]
                  self.assertListEqual(_snake_case  ,			_snake_case		)
                  # Test that special tokens are reset
  
  
  
        @require_torch
        def 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			Optional[int]              =					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  _UpperCAmelCase    :			int              =					tokenizer(_snake_case  ,			padding=_snake_case  ,			return_tensors="pt"		)
                  # check if input_ids are returned and no labels
                  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  :			Union[str, Any]		):
             _UpperCAmelCase    :			Tuple              =					[
                 '''Summary of the text.''',
                 '''Another summary.''',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  _UpperCAmelCase    :			Dict              =					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  :			str		):
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  _UpperCAmelCase    :			Union[str, Any]              =					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, 1_0_2_4)		)
  
  
  
        @require_torch
        def 		snake_case_      (  self  :			Union[str, Any]		):
             _UpperCAmelCase    :			List[str]              =					['''A long paragraph for summarization.''']
             _UpperCAmelCase    :			Optional[Any]              =					[
                 '''Summary of the text.''',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  _UpperCAmelCase    :			Any              =					tokenizer(_snake_case  ,			return_tensors="pt"		)
                  _UpperCAmelCase    :			List[Any]              =					tokenizer(text_target=_snake_case  ,			return_tensors="pt"		)
                  _UpperCAmelCase    :			List[str]              =					inputs['''input_ids''']
                  _UpperCAmelCase    :			str              =					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()		)
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             pass
  
  
  
        def 		snake_case_      (  self  :			Optional[int]		):
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
                       _UpperCAmelCase    :			Optional[int]              =					self.rust_tokenizer_class.from_pretrained(_snake_case  ,			**_snake_case		)
                       _UpperCAmelCase    :			List[str]              =					self.tokenizer_class.from_pretrained(_snake_case  ,			**_snake_case		)
                       _UpperCAmelCase    :			Optional[int]              =					'''A, <mask> AllenNLP sentence.'''
                       _UpperCAmelCase    :			str              =					tokenizer_r.encode_plus(_snake_case  ,			add_special_tokens=_snake_case  ,			return_token_type_ids=_snake_case		)
                       _UpperCAmelCase    :			Dict              =					tokenizer_p.encode_plus(_snake_case  ,			add_special_tokens=_snake_case  ,			return_token_type_ids=_snake_case		)
  
                       # token_type_ids should put 0 everywhere
                       self.assertEqual(sum(tokens_r["token_type_ids"]		)  ,			sum(tokens_p["token_type_ids"]		)		)
  
                       # attention_mask should put 1 everywhere, so sum over length should be 1
                       self.assertEqual(
                           sum(tokens_r["attention_mask"]		) / len(tokens_r["attention_mask"]		)  ,			sum(tokens_p["attention_mask"]		) / len(tokens_p["attention_mask"]		)  ,			)
  
                       _UpperCAmelCase    :			Any              =					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]		)
                       _UpperCAmelCase    :			Any              =					tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]		)
  
                       # Rust correctly handles the space before the mask while python doesnt
                       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>"]		)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     bool:
  
  
  
  
  
       '''simple docstring'''
  
       if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
            raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]"       )
       if len(SCREAMING_SNAKE_CASE__       ) == 0:
            raise ValueError("Input list must be a non empty list"       )
       if len(SCREAMING_SNAKE_CASE__       ) == 1 and series[0] != 0:
            return True
       _UpperCAmelCase    :			Tuple              =					[]
       _UpperCAmelCase    :			Any              =					len(SCREAMING_SNAKE_CASE__       )
       for i in range(0		,						SCREAMING_SNAKE_CASE__       ):
            if series[i] == 0:
                 raise ValueError("Input series cannot have 0 as an element"       )
            rec_series.append(1 / series[i]       )
       _UpperCAmelCase    :			Dict              =					rec_series[1] - rec_series[0]
       for index in range(2		,						SCREAMING_SNAKE_CASE__       ):
            if rec_series[index] - rec_series[index - 1] != common_diff:
                 return False
       return True
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     float:
  
  
  
  
  
       '''simple docstring'''
  
       if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
            raise ValueError("Input series is not valid, valid series - [2, 4, 6]"       )
       if len(SCREAMING_SNAKE_CASE__       ) == 0:
            raise ValueError("Input list must be a non empty list"       )
       _UpperCAmelCase    :			Any              =					0
       for val in series:
            answer += 1 / val
       return len(SCREAMING_SNAKE_CASE__       ) / answer
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  