





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       List[Any]      					=["""image_processor""", """tokenizer"""]
  a_							:       Optional[int]      					="""BridgeTowerImageProcessor"""
  a_							:       Tuple      					=("""RobertaTokenizer""", """RobertaTokenizerFast""")







  def __init__(      self      :					Tuple      ,				UpperCamelCase      :					Tuple      ,				UpperCamelCase      :					Dict							):


    '''simple docstring'''





    super().__init__(UpperCamelCase      ,				UpperCamelCase							)







  def __call__(      self      :					str      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,				UpperCamelCase      :					bool = True      ,				UpperCamelCase      :					Union[bool, str, PaddingStrategy] = False      ,				UpperCamelCase      :					Union[bool, str, TruncationStrategy] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					int = 0      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[bool] = None      ,				UpperCamelCase      :					Optional[bool] = None      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					bool = False      ,				UpperCamelCase      :					bool = True      ,				UpperCamelCase      :					Optional[Union[str, TensorType]] = None      ,				**UpperCamelCase      :					Any      ,				):


    '''simple docstring'''





    _snake_case   :				Dict														=	self.tokenizer(
        text=UpperCamelCase      ,				add_special_tokens=UpperCamelCase      ,				padding=UpperCamelCase      ,				truncation=UpperCamelCase      ,				max_length=UpperCamelCase      ,				stride=UpperCamelCase      ,				pad_to_multiple_of=UpperCamelCase      ,				return_token_type_ids=UpperCamelCase      ,				return_attention_mask=UpperCamelCase      ,				return_overflowing_tokens=UpperCamelCase      ,				return_special_tokens_mask=UpperCamelCase      ,				return_offsets_mapping=UpperCamelCase      ,				return_length=UpperCamelCase      ,				verbose=UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase      ,				)
    # add pixel_values + pixel_mask
    _snake_case   :				Union[str, Any]														=	self.image_processor(
        UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				do_normalize=UpperCamelCase      ,				do_center_crop=UpperCamelCase      ,				**UpperCamelCase							)
    encoding.update(UpperCamelCase							)

    return encoding







  def UpperCamelCase_ (      self      :					int      ,				*UpperCamelCase      :					Tuple      ,				**UpperCamelCase      :					List[Any]							):


    '''simple docstring'''





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







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


    '''simple docstring'''





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







  @property
  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





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











from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def     lowerCamelCase_	(      )->		Any:
  _snake_case   :				List[str]														=	{
      'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
      'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
      'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
  }
  _snake_case   :				Optional[Any]														=	Dataset.from_dict(lowerCAmelCase				)
  return dataset







class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	get_dataset()
    _snake_case   :				Tuple														=	make_duplicate_clusters(UpperCamelCase      ,				0.85							)
    self.assertEqual(len(duplicate_clusters[0]							)      ,				2							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	get_dataset()
    _snake_case							,					_snake_case   :				str														=	deduplicate_dataset(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				2							)
    print(UpperCamelCase							)
    self.assertEqual(duplicate_clusters[0][0]['copies']      ,				2							)
    self.assertEqual(duplicate_clusters[0][0]['is_extreme']      ,				UpperCamelCase							)




