





import pprint

import requests

lowerCAmelCase_						   =    """https://zenquotes.io/api"""


def     lowerCamelCase_	(      )->		list:
  return requests.get(API_ENDPOINT_URL + '/today'				).json()


def     lowerCamelCase_	(      )->		list:
  return requests.get(API_ENDPOINT_URL + '/random'				).json()


if __name__ == "__main__":
      lowerCAmelCase_						   =    random_quotes()
      pprint.pprint(response)











import warnings

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



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Union[str, Any]      					=["""image_processor""", """tokenizer"""]
  a_							:       Optional[int]      					="""CLIPImageProcessor"""
  a_							:       Optional[Any]      					=("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")







  def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				int														=	None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'      ,				UpperCamelCase      ,				)
      _snake_case   :				Optional[Any]														=	kwargs.pop('feature_extractor'							)

    _snake_case   :				Dict														=	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__(UpperCamelCase      ,				UpperCamelCase							)







  def __call__(      self      :					Dict      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    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:
      _snake_case   :				Optional[int]														=	self.tokenizer(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if images is not None:
      _snake_case   :				Optional[int]														=	self.image_processor(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if text is not None and images is not None:
      _snake_case   :				Optional[int]														=	image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**UpperCamelCase							)      ,				tensor_type=UpperCamelCase							)







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


    '''simple docstring'''





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







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


    '''simple docstring'''





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







  @property
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





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




