






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

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					int
	UpperCamelCase_  :					int





class     lowerCamelCase      :







	def __init__(     self      :Dict   ,     lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [[] for _ in range(lowercase			)]
				SCREAMING_SNAKE_CASE     						= size







	def __getitem__(     self      :Union[str, Any]   ,     lowercase      :int			)		->      Iterator[Edge]:







				"""simple docstring"""






				return iter(self._graph[vertex]			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      str:







				"""simple docstring"""






				return self._size







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :int   ,     lowercase      :int			)		->      List[Any]:







				"""simple docstring"""






				if weight not in (0, 1):
							raise ValueError('''Edge weight must be either 0 or 1.'''			)

				if to_vertex < 0 or to_vertex >= self.size:
							raise ValueError('''Vertex indexes must be in [0; size).'''			)

				self._graph[from_vertex].append(Edge(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int | None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= deque([start_vertex]			)
				SCREAMING_SNAKE_CASE     						= [None] * self.size
				SCREAMING_SNAKE_CASE     						= 0

				while queue:
							SCREAMING_SNAKE_CASE     						= queue.popleft()
							SCREAMING_SNAKE_CASE     						= distances[current_vertex]
							if current_distance is None:
										continue

							for edge in self[current_vertex]:
										SCREAMING_SNAKE_CASE     						= current_distance + edge.weight
										SCREAMING_SNAKE_CASE     						= distances[edge.destination_vertex]
										if (
										    isinstance(lowercase   ,     lowercase			)
										    and new_distance >= dest_vertex_distance
										):
													continue
										SCREAMING_SNAKE_CASE     						= new_distance
										if edge.weight == 0:
													queue.appendleft(edge.destination_vertex			)
										else:
													queue.append(edge.destination_vertex			)

				if distances[finish_vertex] is None:
							raise ValueError('''No path from start_vertex to finish_vertex.'''			)

				return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()