


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__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		['image_processor', 'tokenizer']
    __magic_name__        =		'BlipImageProcessor'
    __magic_name__        =		('BertTokenizer', 'BertTokenizerFast')
    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    False
         super().__init__(__snake_case      , __snake_case   )
         snake_case					=    self.image_processor
    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      , ):
         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:
              snake_case					=    self.tokenizer
              snake_case					=    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      , )
              return text_encoding

         # add pixel_values
         snake_case					=    self.image_processor(__snake_case      , return_tensors=__snake_case   )

         if text is not None:
              snake_case					=    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      , )
         else:
              snake_case					=    None

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

         return encoding_image_processor
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         return self.tokenizer.batch_decode(*__snake_case      , **__snake_case   )
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         return self.tokenizer.decode(*__snake_case      , **__snake_case   )




    @property
    def 		a_      (	self   ):
         snake_case					=    self.tokenizer.model_input_names
         snake_case					=    self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )








import re

from filelock import FileLock


try:
 import nltk

 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   True
except (ImportError, ModuleNotFoundError):
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   False

if NLTK_AVAILABLE:
 with FileLock(".lock") as lock:
  nltk.download("punkt", quiet=True)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     re.sub('''<n>'''							,''''''							,UpperCamelCase_    )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(UpperCamelCase_    )    )




