






import torch





def 					snake_case	(  ):




	'''simple docstring'''







	if torch.cuda.is_available():
		__lowercase   							=	torch.cuda.device_count()
	else:
		__lowercase   							=	0
	print(F'Successfully ran on {num_gpus} GPUs'       )


if __name__ == "__main__":
						main()








'''simple docstring'''

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 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Dict									=       ['image_processor', 'tokenizer']
       _snake_case      :	int									=       'BlipImageProcessor'
       _snake_case      :	Tuple									=       ('BertTokenizer', 'BertTokenizerFast')





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

            '''simple docstring'''




            _UpperCamelCase							       =			False
            super().__init__(lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			self.image_processor





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

            # add pixel_values
            _UpperCamelCase							       =			self.image_processor(lowerCAmelCase__			,					return_tensors=lowerCAmelCase__	)

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

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

            return encoding_image_processor





       def snake_case__		(				self  :						Any			,					*lowerCAmelCase__  :						Union[str, Any]			,					**lowerCAmelCase__  :						Dict	)       ->	List[str]:

            '''simple docstring'''




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





       def snake_case__		(				self  :						Tuple			,					*lowerCAmelCase__  :						int			,					**lowerCAmelCase__  :						Optional[int]	)       ->	Optional[int]:

            '''simple docstring'''




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







       @property
       def snake_case__		(				self  :						Any	)       ->	str:

            '''simple docstring'''




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



