





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        _lowerCAmelCase    (			_lowercase							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       List[Any]      					=LEDTokenizer
  a_							:       Optional[int]      					=LEDTokenizerFast
  a_							:       Union[str, Any]      					=True







  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





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

    _snake_case   :				str														=	os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES['vocab_file']							)
    _snake_case   :				List[str]														=	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      :					Dict      ,				**UpperCamelCase      :					int							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				**UpperCamelCase      :					Any							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    return "lower newer", "lower newer"







  @cached_property
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





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







  @cached_property
  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





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







  @require_torch
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
    _snake_case   :				Union[str, Any]														=	[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      _snake_case   :				str														=	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   :				Tuple														=	batch.input_ids.tolist()[0]
      self.assertListEqual(A_      ,				A_							)







  @require_torch
  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      _snake_case   :				List[str]														=	tokenizer(A_      ,				padding=A_      ,				return_tensors='pt'							)
      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   :				int														=	[
        'Summary of the text.',
        'Another summary.',
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      _snake_case   :				Dict														=	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      :					Optional[Any]							):


    '''simple docstring'''





    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      _snake_case   :				str														=	tokenizer(
          ['I am a small frog' * 10_24, 'I am a small frog']      ,				padding=A_      ,				truncation=A_      ,				return_tensors='pt'							)
      self.assertIsInstance(A_      ,				A_							)
      self.assertEqual(batch.input_ids.shape      ,				(2, 51_22)							)







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


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	['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   :				Union[str, Any]														=	tokenizer(A_      ,				return_tensors='pt'							)
      _snake_case   :				int														=	tokenizer(text_target=A_      ,				return_tensors='pt'							)
      _snake_case   :				str														=	inputs['input_ids']
      _snake_case   :				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()							)







  @require_torch
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
      _snake_case   :				Optional[Any]														=	['Summary of the text.', 'Another summary.']
      _snake_case   :				Optional[int]														=	[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

      _snake_case   :				List[str]														=	tokenizer(A_      ,				padding=A_							)
      _snake_case   :				Any														=	[[0] * len(A_							) for x in encoded_output['input_ids']]
      _snake_case   :				Optional[Any]														=	tokenizer.pad(A_							)
      self.assertSequenceEqual(outputs['global_attention_mask']      ,				A_							)







  def UpperCamelCase_ (      self      :					List[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   :				Any														=	self.rust_tokenizer_class.from_pretrained(A_      ,				**A_							)
        _snake_case   :				int														=	self.tokenizer_class.from_pretrained(A_      ,				**A_							)
        _snake_case   :				List[Any]														=	'A, <mask> AllenNLP sentence.'
        _snake_case   :				Dict														=	tokenizer_r.encode_plus(A_      ,				add_special_tokens=A_      ,				return_token_type_ids=A_							)
        _snake_case   :				List[str]														=	tokenizer_p.encode_plus(A_      ,				add_special_tokens=A_      ,				return_token_type_ids=A_							)
        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']							)      ,				)

        _snake_case   :				Tuple														=	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']							)
        _snake_case   :				Dict														=	tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']							)
        self.assertSequenceEqual(tokens_p['input_ids']      ,				[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
        self.assertSequenceEqual(tokens_r['input_ids']      ,				[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)

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











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Union[str, Any]      					=["""image_processor""", """tokenizer"""]
  a_							:       Optional[int]      					="""CLIPImageProcessor"""
  a_							:       Optional[Any]      					=("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")







  def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				int														=	None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'      ,				UpperCamelCase      ,				)
      _snake_case   :				Optional[Any]														=	kwargs.pop('feature_extractor'							)

    _snake_case   :				Dict														=	image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'							)
    if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'							)

    super().__init__(UpperCamelCase      ,				UpperCamelCase							)







  def __call__(      self      :					Dict      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    if text is None and images is None:
      raise ValueError('You have to specify either text or images. Both cannot be none.'							)

    if text is not None:
      _snake_case   :				Optional[int]														=	self.tokenizer(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if images is not None:
      _snake_case   :				Optional[int]														=	self.image_processor(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if text is not None and images is not None:
      _snake_case   :				Optional[int]														=	image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**UpperCamelCase							)      ,				tensor_type=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Any      ,				**UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    return self.tokenizer.batch_decode(*UpperCamelCase      ,				**UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Union[str, Any]      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    return self.tokenizer.decode(*UpperCamelCase      ,				**UpperCamelCase							)







  @property
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.tokenizer.model_input_names
    _snake_case   :				List[Any]														=	self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




