


'''simple docstring'''







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    ["image_processor", "tokenizer"]
     __magic_name__    			=    "BlipImageProcessor"
     __magic_name__    			=    "AutoTokenizer"







     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            super().__init__(snake_case__      ,	snake_case__   )

            # add QFormer tokenizer
            _lowerCAmelCase    :							List[Any]   			=       qformer_tokenizer







     def __call__( self      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = True      ,	snake_case__ = False      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = 0      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = False      ,	snake_case__ = True      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            if images is None and text is None:
                   raise ValueError('You have to specify at least images or text.'   )

            _lowerCAmelCase    :							Optional[int]   			=       BatchFeature()

            if text is not None:
                   _lowerCAmelCase    :							Any   			=       self.tokenizer(
                       text=snake_case__      ,	add_special_tokens=snake_case__      ,	padding=snake_case__      ,	truncation=snake_case__      ,	max_length=snake_case__      ,	stride=snake_case__      ,	pad_to_multiple_of=snake_case__      ,	return_attention_mask=snake_case__      ,	return_overflowing_tokens=snake_case__      ,	return_special_tokens_mask=snake_case__      ,	return_offsets_mapping=snake_case__      ,	return_token_type_ids=snake_case__      ,	return_length=snake_case__      ,	verbose=snake_case__      ,	return_tensors=snake_case__      ,	**snake_case__      ,	)
                   encoding.update(snake_case__   )
                   _lowerCAmelCase    :							Optional[int]   			=       self.qformer_tokenizer(
                       text=snake_case__      ,	add_special_tokens=snake_case__      ,	padding=snake_case__      ,	truncation=snake_case__      ,	max_length=snake_case__      ,	stride=snake_case__      ,	pad_to_multiple_of=snake_case__      ,	return_attention_mask=snake_case__      ,	return_overflowing_tokens=snake_case__      ,	return_special_tokens_mask=snake_case__      ,	return_offsets_mapping=snake_case__      ,	return_token_type_ids=snake_case__      ,	return_length=snake_case__      ,	verbose=snake_case__      ,	return_tensors=snake_case__      ,	**snake_case__      ,	)
                   _lowerCAmelCase    :							Any   			=       qformer_text_encoding.pop('input_ids'   )
                   _lowerCAmelCase    :							Union[str, Any]   			=       qformer_text_encoding.pop('attention_mask'   )

            if images is not None:
                   _lowerCAmelCase    :							Tuple   			=       self.image_processor(snake_case__      ,	return_tensors=snake_case__   )
                   encoding.update(snake_case__   )

            return encoding







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.tokenizer.model_input_names
            _lowerCAmelCase    :							Dict   			=       self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







     def a	( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            if os.path.isfile(snake_case__   ):
                   raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
            os.makedirs(snake_case__      ,	exist_ok=snake_case__   )
            _lowerCAmelCase    :							Optional[int]   			=       os.path.join(snake_case__      ,	'qformer_tokenizer'   )
            self.qformer_tokenizer.save_pretrained(snake_case__   )
            return super().save_pretrained(snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       AutoTokenizer.from_pretrained(snake_case__      ,	subfolder='qformer_tokenizer'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       cls._get_arguments_from_pretrained(snake_case__      ,	**snake_case__   )
            args.append(snake_case__   )
            return cls(*snake_case__   )





'''simple docstring'''







import pytest

import datasets


# Import fixture modules as plugins
lowerCAmelCase    :     List[str]					=						["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       for item in items:
              if any(marker in item.keywords for marker in ['integration', 'unit']					):
                     continue
              item.add_marker(pytest.mark.unit					)






def 			lowercase				(_A					):







       """simple docstring"""







       config.addinivalue_line('markers'   ,						'torchaudio_latest: mark test to run with torchaudio>=0.12'					)






@pytest.fixture(autouse=_A					)
def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							str   			=       tmp_path_factory.getbasetemp() / 'cache'
       _lowerCAmelCase    :							Dict   			=       test_hf_cache_home / 'datasets'
       _lowerCAmelCase    :							List[Any]   			=       test_hf_cache_home / 'metrics'
       _lowerCAmelCase    :							List[Any]   			=       test_hf_cache_home / 'modules'
       monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'   ,						str(_A					)					)
       monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'   ,						str(_A					)					)
       monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'   ,						str(_A					)					)
       _lowerCAmelCase    :							Dict   			=       test_hf_datasets_cache / 'downloads'
       monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'   ,						str(_A					)					)
       _lowerCAmelCase    :							Union[str, Any]   			=       test_hf_datasets_cache / 'downloads' / 'extracted'
       monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,						str(_A					)					)






@pytest.fixture(autouse=_A   ,						scope='session'					)
def 			lowercase				():







       """simple docstring"""







       datasets.disable_progress_bar()






@pytest.fixture(autouse=_A					)
def 			lowercase				(_A					):







       """simple docstring"""







       monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'   ,						_A					)






@pytest.fixture
def 			lowercase				(_A					):







       """simple docstring"""







       monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'   ,						_A					)

