






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     _a							(	UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = LEDTokenizer
  A_						       = LEDTokenizerFast
  A_						       = True




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     super().setUp()
     UpperCamelCase_						=  [
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         '\u0120',
         '\u0120l',
         '\u0120n',
         '\u0120lo',
         '\u0120low',
         'er',
         '\u0120lowest',
         '\u0120newer',
         '\u0120wider',
         '<unk>',
     ]
     UpperCamelCase_						=  dict(zip(_UpperCAmelCase		,		range(len(_UpperCAmelCase			)			)			)			)
     UpperCamelCase_						=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
     UpperCamelCase_						=  {'unk_token': '<unk>'}

     UpperCamelCase_						=  os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['vocab_file']			)
     UpperCamelCase_						=  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(_UpperCAmelCase			) + '\n'			)
     with open(self.merges_file		,		'w'		,		encoding='utf-8'			) as fp:
        fp.write('\n'.join(_UpperCAmelCase			)			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			List[str]:
     kwargs.update(self.special_tokens_map			)
     return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			Union[str, Any]:
     kwargs.update(self.special_tokens_map			)
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Any:
     return "lower newer", "lower newer"




  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     return LEDTokenizer.from_pretrained('allenai/led-base-16384'			)




  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			Dict:
     return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'			)




  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
     UpperCamelCase_						=  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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

        self.assertEqual((2, 9)		,		batch.input_ids.shape			)
        self.assertEqual((2, 9)		,		batch.attention_mask.shape			)
        UpperCamelCase_						=  batch.input_ids.tolist()[0]
        self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase			)




  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase_						=  tokenizer(_UpperCAmelCase		,		padding=_UpperCAmelCase		,		return_tensors='pt'			)
        self.assertIn('input_ids'		,		_UpperCAmelCase			)
        self.assertIn('attention_mask'		,		_UpperCAmelCase			)
        self.assertNotIn('labels'		,		_UpperCAmelCase			)
        self.assertNotIn('decoder_attention_mask'		,		_UpperCAmelCase			)




  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_						=  [
         'Summary of the text.',
         'Another summary.',
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase_						=  tokenizer(text_target=_UpperCAmelCase		,		max_length=32		,		padding='max_length'		,		return_tensors='pt'			)
        self.assertEqual(32		,		targets['input_ids'].shape[1]			)




  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			str:
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase_						=  tokenizer(
            ['I am a small frog' * 1024, 'I am a small frog']		,		padding=_UpperCAmelCase		,		truncation=_UpperCAmelCase		,		return_tensors='pt'			)
        self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase			)
        self.assertEqual(batch.input_ids.shape		,		(2, 5122)			)




  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  ['A long paragraph for summarization.']
     UpperCamelCase_						=  [
         'Summary of the text.',
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase_						=  tokenizer(_UpperCAmelCase		,		return_tensors='pt'			)
        UpperCamelCase_						=  tokenizer(text_target=_UpperCAmelCase		,		return_tensors='pt'			)
        UpperCamelCase_						=  inputs['input_ids']
        UpperCamelCase_						=  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  _UpperCAmelCase  (  self			)      ->			Any:
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase_						=  ['Summary of the text.', 'Another summary.']
        UpperCamelCase_						=  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

        UpperCamelCase_						=  tokenizer(_UpperCAmelCase		,		padding=_UpperCAmelCase			)
        UpperCamelCase_						=  [[0] * len(_UpperCAmelCase			) for x in encoded_output['input_ids']]
        UpperCamelCase_						=  tokenizer.pad(_UpperCAmelCase			)
        self.assertSequenceEqual(outputs['global_attention_mask']		,		_UpperCAmelCase			)




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




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
           UpperCamelCase_						=  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase		,		**_UpperCAmelCase			)
           UpperCamelCase_						=  self.tokenizer_class.from_pretrained(_UpperCAmelCase		,		**_UpperCAmelCase			)
           UpperCamelCase_						=  'A, <mask> AllenNLP sentence.'
           UpperCamelCase_						=  tokenizer_r.encode_plus(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase		,		return_token_type_ids=_UpperCAmelCase			)
           UpperCamelCase_						=  tokenizer_p.encode_plus(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase		,		return_token_type_ids=_UpperCAmelCase			)
           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']			)		,		)

           UpperCamelCase_						=  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
           UpperCamelCase_						=  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']			)
           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(
               _UpperCAmelCase		,		['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
           self.assertSequenceEqual(
               _UpperCAmelCase		,		['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)













from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

snake_case__   :						Dict													=   TypeVar("""T""")


class     _a							(	Generic[T]     ):






  """simple docstring"""





  A_						       = 42  # Cache store of keys
  A_						       = 42  # References of the keys in cache
  A_						       = 10  # Maximum capacity of cache




  def __init__(  self		,		_UpperCAmelCase			)      ->			None:
     UpperCamelCase_						=  deque()
     UpperCamelCase_						=  set()
     if not n:
        UpperCamelCase_						=  sys.maxsize
     elif n < 0:
        raise ValueError('n should be an integer greater than 0.'			)
     else:
        UpperCamelCase_						=  n




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     if x not in self.key_reference:
        if len(self.dq_store			) == LRUCache._MAX_CAPACITY:
           UpperCamelCase_						=  self.dq_store.pop()
           self.key_reference.remove(_UpperCAmelCase			)
     else:
        self.dq_store.remove(_UpperCAmelCase			)

     self.dq_store.appendleft(_UpperCAmelCase			)
     self.key_reference.add(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			None:
     for k in self.dq_store:
        print(_UpperCAmelCase			)




  def __repr__(  self			)      ->			str:
     return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store			)}"""


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 snake_case__   :						LRUCache[str | int]													=   LRUCache(4)
 lru_cache.refer("""A""")
 lru_cache.refer(2)
 lru_cache.refer(3)
 lru_cache.refer("""A""")
 lru_cache.refer(4)
 lru_cache.refer(5)
 lru_cache.display()

 print(lru_cache)
 assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





