code: 
  
  
  
  
  
  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 (    SCREAMING_SNAKE_CASE__ ,   unittest.TestCase    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      List[str]       =BartTokenizer
        UpperCAmelCase_						:      List[Any]       =BartTokenizerFast
        UpperCAmelCase_						:      Any       =True
        UpperCAmelCase_						:      Dict       =filter_roberta_detectors
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().setUp()
          __snake_case   :				str        =       [
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
          ]
          __snake_case   :				int        =       dict(zip(a_				,						range(len(a_   )   )   )   )
          __snake_case   :				List[str]        =       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          __snake_case   :				List[str]        =       {"unk_token": "<unk>"}
  
          __snake_case   :				Dict        =       os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES["vocab_file"]   )
          __snake_case   :				Any        =       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(a_   ) + "\n"   )
          with open(self.merges_file				,						"w"				,						encoding="utf-8"   ) as fp:
            fp.write("\n".join(a_   )   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						**UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          kwargs.update(self.special_tokens_map   )
          return self.tokenizer_class.from_pretrained(self.tmpdirname				,						**a_   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						**UpperCAmelCase   )       ->   List[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          kwargs.update(self.special_tokens_map   )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,						**a_   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return "lower newer", "lower newer"
  
  
  
  
  
  
  
        @cached_property
        def 						UpperCAmelCase		( self   )       ->   Optional[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return BartTokenizer.from_pretrained("facebook/bart-large"   )
  
  
  
  
  
  
  
        @cached_property
        def 						UpperCAmelCase		( self   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return BartTokenizerFast.from_pretrained("facebook/bart-large"   )
  
  
  
  
  
  
  
        @require_torch
        def 						UpperCAmelCase		( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Union[str, Any]        =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
          __snake_case   :				Tuple        =       [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]
  
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __snake_case   :				List[Any]        =       tokenizer(a_				,						max_length=len(a_   )				,						padding=a_				,						return_tensors="pt"   )
            self.assertIsInstance(a_				,						a_   )
  
            self.assertEqual((2, 9)				,						batch.input_ids.shape   )
            self.assertEqual((2, 9)				,						batch.attention_mask.shape   )
            __snake_case   :				Optional[int]        =       batch.input_ids.tolist()[0]
            self.assertListEqual(a_				,						a_   )
            # Test that special tokens are reset
  
  
  
  
  
  
  
        @require_torch
        def 						UpperCAmelCase		( self   )       ->   Optional[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __snake_case   :				Any        =       tokenizer(a_				,						padding=a_				,						return_tensors="pt"   )
            # check if input_ids are returned and no labels
            self.assertIn("input_ids"				,						a_   )
            self.assertIn("attention_mask"				,						a_   )
            self.assertNotIn("labels"				,						a_   )
            self.assertNotIn("decoder_attention_mask"				,						a_   )
  
  
  
  
  
  
  
        @require_torch
        def 						UpperCAmelCase		( self   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Union[str, Any]        =       [
              "Summary of the text.",
              "Another summary.",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __snake_case   :				Optional[Any]        =       tokenizer(text_target=a_				,						max_length=32				,						padding="max_length"				,						return_tensors="pt"   )
            self.assertEqual(32				,						targets["input_ids"].shape[1]   )
  
  
  
  
  
  
  
        @require_torch
        def 						UpperCAmelCase		( self   )       ->   List[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __snake_case   :				Dict        =       tokenizer(
                ["I am a small frog" * 1024, "I am a small frog"]				,						padding=a_				,						truncation=a_				,						return_tensors="pt"   )
            self.assertIsInstance(a_				,						a_   )
            self.assertEqual(batch.input_ids.shape				,						(2, 1024)   )
  
  
  
  
  
  
  
        @require_torch
        def 						UpperCAmelCase		( self   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       ["A long paragraph for summarization."]
          __snake_case   :				List[str]        =       [
              "Summary of the text.",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __snake_case   :				str        =       tokenizer(a_				,						return_tensors="pt"   )
            __snake_case   :				Dict        =       tokenizer(text_target=a_				,						return_tensors="pt"   )
            __snake_case   :				Union[str, Any]        =       inputs["input_ids"]
            __snake_case   :				List[Any]        =       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 						UpperCAmelCase		( self   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          pass
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   Tuple:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
              __snake_case   :				str        =       self.rust_tokenizer_class.from_pretrained(a_				,						**a_   )
              __snake_case   :				int        =       self.tokenizer_class.from_pretrained(a_				,						**a_   )
              __snake_case   :				int        =       "A, <mask> AllenNLP sentence."
              __snake_case   :				Union[str, Any]        =       tokenizer_r.encode_plus(a_				,						add_special_tokens=a_				,						return_token_type_ids=a_   )
              __snake_case   :				Dict        =       tokenizer_p.encode_plus(a_				,						add_special_tokens=a_				,						return_token_type_ids=a_   )
  
              # 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"]   )				,						)
  
              __snake_case   :				Tuple        =       tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]   )
              __snake_case   :				Tuple        =       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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )
              self.assertSequenceEqual(tokens_r["input_ids"]				,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )
  
              self.assertSequenceEqual(
                  a_				,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]   )
              self.assertSequenceEqual(
                  a_				,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]   )
  
  
style_context: 
  
  
  
  
  
  import torch
  from torch import autograd
  
  
  
  
  class 				_lowerCamelCase (    autograd.Function    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       inputs.numel()
          __snake_case   :				Optional[int]        =       int(0.005 * nb_elems   ) + 1
          if sigmoid:
            __snake_case   :				Any        =       (torch.sigmoid(UpperCAmelCase   ) > threshold).type(inputs.type()   )
          else:
            __snake_case   :				Optional[int]        =       (inputs > threshold).type(inputs.type()   )
          if mask.sum() < nb_min:
            # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
            __snake_case   :				List[str]        =       inputs.flatten().kthvalue(max(nb_elems - nb_min				,						1   )   ).values
            __snake_case   :				Union[str, Any]        =       (inputs > k_threshold).type(inputs.type()   )
          return mask
  
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return gradOutput, None, None
  
  
  
  
  class 				_lowerCamelCase (    autograd.Function    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[int]        =       inputs.clone()
          __snake_case    ,   __snake_case   :				int        =       inputs.flatten().sort(descending=UpperCAmelCase   )
          __snake_case   :				Tuple        =       int(threshold * inputs.numel()   )
  
          # flat_out and mask access the same memory.
          __snake_case   :				Optional[int]        =       mask.flatten()
          __snake_case   :				Union[str, Any]        =       0
          __snake_case   :				Any        =       1
          return mask
  
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return gradOutput, None
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Union[str, Any]        =       inputs.clone()
          __snake_case    ,   __snake_case   :				Optional[Any]        =       inputs.abs().flatten().sort(descending=UpperCAmelCase   )
          __snake_case   :				List[Any]        =       int(threshold * inputs.numel()   )
  
          # flat_out and mask access the same memory.
          __snake_case   :				Optional[Any]        =       mask.flatten()
          __snake_case   :				Tuple        =       0
          __snake_case   :				Any        =       1
          return mask
  
  