'''simple docstring'''

import warnings

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







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					["""image_processor""", """tokenizer"""]
 _SCREAMING_SNAKE_CASE     :       Dict      	=					"""CLIPImageProcessor"""
 _SCREAMING_SNAKE_CASE     :       Any      	=					("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None      ,       SCREAMING_SNAKE_CASE__   :	str=None      ,       **SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    List[Any]:
  __lowerCAmelCase											=						None
  if "feature_extractor" in kwargs:
   warnings.warn(
       """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
       """ instead."""      ,       SCREAMING_SNAKE_CASE__      ,       )
   __lowerCAmelCase											=						kwargs.pop("""feature_extractor"""							)

  __lowerCAmelCase											=						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__(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def __call__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None      ,       SCREAMING_SNAKE_CASE__   :	str=None      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=None      ,       **SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    Union[str, Any]:

  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:
   __lowerCAmelCase											=						self.tokenizer(SCREAMING_SNAKE_CASE__      ,       return_tensors=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)

  if images is not None:
   __lowerCAmelCase											=						self.image_processor(SCREAMING_SNAKE_CASE__      ,       return_tensors=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)

  if text is not None and images is not None:
   __lowerCAmelCase											=						image_features.pixel_values
   return encoding
  elif text is not None:
   return encoding
  else:
   return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__							)      ,       tensor_type=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[str]      ,       *SCREAMING_SNAKE_CASE__   :	Tuple      ,       **SCREAMING_SNAKE_CASE__   :	Dict							)  ->    List[str]:
  return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	str      ,       *SCREAMING_SNAKE_CASE__   :	str      ,       **SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    str:
  return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)
 @property
 def a					(      self   :	Optional[Any]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						self.tokenizer.model_input_names
  __lowerCAmelCase											=						self.image_processor.model_input_names
  return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
 @property
 def a					(      self   :	Dict							)  ->    Optional[Any]:
  warnings.warn(
      """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,       SCREAMING_SNAKE_CASE__      ,       )
  return self.image_processor_class





 @property
 def a					(      self   :	Union[str, Any]							)  ->    List[Any]:
  warnings.warn(
      """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,       SCREAMING_SNAKE_CASE__      ,       )
  return self.image_processor







'''simple docstring'''

import re

from filelock import FileLock


try:
 import nltk

 _A			:      int					      =				True
except (ImportError, ModuleNotFoundError):
 _A			:      Optional[Any]					      =				False

if NLTK_AVAILABLE:
 with FileLock('''.lock''') as lock:
  nltk.download('''punkt''', quiet=True)







def      UpperCamelCase_						(					snake_case_       :       str      )					->     str:






 '''simple docstring'''

 re.sub("""<n>"""						,		""""""						,		snake_case_      )  # 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(snake_case_      )      )






