

from typing import List, Optional, Union

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


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							['image_processor', 'tokenizer']
    lowerCamelCase			=							'BlipImageProcessor'
    lowerCamelCase			=							'AutoTokenizer'


    def __init__(      self :       List[Any],lowercase_ :       Optional[Any],lowercase_ :       List[str]		)->    Optional[int]:





        '''simple docstring'''







        A__          =   False
        super().__init__(lowercase_,lowercase_		)
        A__          =   self.image_processor


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





        '''simple docstring'''







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

        # Get only text
        if images is None:
            A__          =   self.tokenizer
            A__          =   self.tokenizer(
                text=lowercase_,add_special_tokens=lowercase_,padding=lowercase_,truncation=lowercase_,max_length=lowercase_,stride=lowercase_,pad_to_multiple_of=lowercase_,return_attention_mask=lowercase_,return_overflowing_tokens=lowercase_,return_special_tokens_mask=lowercase_,return_offsets_mapping=lowercase_,return_token_type_ids=lowercase_,return_length=lowercase_,verbose=lowercase_,return_tensors=lowercase_,**lowercase_,)
            return text_encoding

        # add pixel_values
        A__          =   self.image_processor(lowercase_,return_tensors=lowercase_		)

        if text is not None:
            A__          =   self.tokenizer(
                text=lowercase_,add_special_tokens=lowercase_,padding=lowercase_,truncation=lowercase_,max_length=lowercase_,stride=lowercase_,pad_to_multiple_of=lowercase_,return_attention_mask=lowercase_,return_overflowing_tokens=lowercase_,return_special_tokens_mask=lowercase_,return_offsets_mapping=lowercase_,return_token_type_ids=lowercase_,return_length=lowercase_,verbose=lowercase_,return_tensors=lowercase_,**lowercase_,)
        else:
            A__          =   None

        if text_encoding is not None:
            encoding_image_processor.update(lowercase_		)

        return encoding_image_processor


    def 						snake_case__     (      self :       int,*lowercase_ :       Any,**lowercase_ :       str		)->    List[str]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Optional[Any],*lowercase_ :       Tuple,**lowercase_ :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







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


    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def 						snake_case__     (      self :       List[str]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.tokenizer.model_input_names
        A__          =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)








import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline



@pytest.mark.integration
def 		A_				(     )						-> List[Any]:
 with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT     ):
  with pytest.raises(_lowerCAmelCase     ):
   requests.request("GET"							,   "https://huggingface.co"     )
  with pytest.raises(requests.exceptions.ConnectTimeout     ):
   requests.request("GET"							,   "https://huggingface.co"							,   timeout=1.0     )



@pytest.mark.integration
def 		A_				(     )						-> Tuple:
 with offline(OfflineSimulationMode.CONNECTION_FAILS     ):
  with pytest.raises(requests.exceptions.ConnectionError     ):
   requests.request("GET"							,   "https://huggingface.co"     )



def 		A_				(     )						-> Optional[int]:
 with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1     ):
  with pytest.raises(_lowerCAmelCase     ):
   http_head("https://huggingface.co"     )
