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    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  ['image_processor', 'tokenizer']
  lowercase_								=  'BlipImageProcessor'
  lowercase_								=  'AutoTokenizer'




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		False
       super().__init__(a_  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.image_processor




  def __call__(   self  :						List[Any]  ,			a_  :						ImageInput = None  ,			a_  :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,			a_  :						bool = True  ,			a_  :						Union[bool, str, PaddingStrategy] = False  ,			a_  :						Union[bool, str, TruncationStrategy] = None  ,			a_  :						Optional[int] = None  ,			a_  :						int = 0  ,			a_  :						Optional[int] = None  ,			a_  :						Optional[bool] = None  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = True  ,			a_  :						Optional[Union[str, TensorType]] = None  ,			**a_  :						int  ,			)->							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:
            SCREAMING_SNAKE_CASE__					:					int      =		self.tokenizer
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.tokenizer(
                text=a_  ,			add_special_tokens=a_  ,			padding=a_  ,			truncation=a_  ,			max_length=a_  ,			stride=a_  ,			pad_to_multiple_of=a_  ,			return_attention_mask=a_  ,			return_overflowing_tokens=a_  ,			return_special_tokens_mask=a_  ,			return_offsets_mapping=a_  ,			return_token_type_ids=a_  ,			return_length=a_  ,			verbose=a_  ,			return_tensors=a_  ,			**a_  ,			)
            return text_encoding

       # add pixel_values
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.image_processor(a_  ,			return_tensors=a_					)

       if text is not None:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.tokenizer(
                text=a_  ,			add_special_tokens=a_  ,			padding=a_  ,			truncation=a_  ,			max_length=a_  ,			stride=a_  ,			pad_to_multiple_of=a_  ,			return_attention_mask=a_  ,			return_overflowing_tokens=a_  ,			return_special_tokens_mask=a_  ,			return_offsets_mapping=a_  ,			return_token_type_ids=a_  ,			return_length=a_  ,			verbose=a_  ,			return_tensors=a_  ,			**a_  ,			)
       else:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		None

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

       return encoding_image_processor




  def 						__lowercase(   self  :						Any  ,			*a_  :						Tuple  ,			**a_  :						Tuple					)->							List[str]:




       """simple docstring"""
       return self.tokenizer.batch_decode(*a_  ,			**a_					)




  def 						__lowercase(   self  :						List[Any]  ,			*a_  :						Any  ,			**a_  :						Dict					)->							int:




       """simple docstring"""
       return self.tokenizer.decode(*a_  ,			**a_					)




  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def 						__lowercase(   self  :						Tuple					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)









'''simple docstring'''


import requests
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							{'''Content-Type''': '''application/json'''}
							_a      	=							requests.post(UpperCamelCase					,		json={'''text''': message_body}					,		headers=UpperCamelCase )
							if response.status_code != 200:
														_a      	=							(
														    '''Request to slack returned an error '''
														    f'{response.status_code}, the response is:\n{response.text}'
														)
														raise ValueError(UpperCamelCase )


if __name__ == "__main__":
				# Set the slack url to the one provided by Slack when you create the webhook at
				# https://my.slack.com/services/new/incoming-webhook/
				send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')

