






"""simple docstring"""
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				['''image_processor''', '''tokenizer''']
	__magic_name__	      =				'''BlipImageProcessor'''
	__magic_name__	      =				'''AutoTokenizer'''





	def __init__(      self					: str	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: str						)						->			str:
				super().__init__(lowerCAmelCase_	,	lowerCAmelCase_						)

				# add QFormer tokenizer
				UpperCAmelCase_     : Tuple			     =					qformer_tokenizer





	def __call__(      self					: List[Any]	,	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_					: int	,	)						->			BatchFeature:
				if images is None and text is None:
							raise ValueError("You have to specify at least images or text."						)

				UpperCAmelCase_     : int			     =					BatchFeature()

				if text is not None:
							UpperCAmelCase_     : List[str]			     =					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_	,	)
							encoding.update(lowerCAmelCase_						)
							UpperCAmelCase_     : Optional[Any]			     =					self.qformer_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_	,	)
							UpperCAmelCase_     : Optional[int]			     =					qformer_text_encoding.pop("input_ids"						)
							UpperCAmelCase_     : Union[str, Any]			     =					qformer_text_encoding.pop("attention_mask"						)

				if images is not None:
							UpperCAmelCase_     : Tuple			     =					self.image_processor(lowerCAmelCase_	,	return_tensors=lowerCAmelCase_						)
							encoding.update(lowerCAmelCase_						)

				return encoding





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	*lowerCAmelCase_					: Dict	,	**lowerCAmelCase_					: Optional[int]						)						->			List[str]:
				return self.tokenizer.batch_decode(*lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	*lowerCAmelCase_					: Optional[int]	,	**lowerCAmelCase_					: str						)						->			Optional[int]:
				return self.tokenizer.decode(*lowerCAmelCase_	,	**lowerCAmelCase_						)





	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : List[Any]			     =					self.tokenizer.model_input_names
				UpperCAmelCase_     : Tuple			     =					self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: Optional[Any]	,	**lowerCAmelCase_					: int						)						->			Dict:
				if os.path.isfile(lowerCAmelCase_						):
							raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""						)
				os.makedirs(lowerCAmelCase_	,	exist_ok=lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					os.path.join(lowerCAmelCase_	,	"qformer_tokenizer"						)
				self.qformer_tokenizer.save_pretrained(lowerCAmelCase_						)
				return super().save_pretrained(lowerCAmelCase_	,	**lowerCAmelCase_						)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]	,	lowerCAmelCase_					: Union[str, Any]	,	**lowerCAmelCase_					: Dict						)						->			Dict:
				UpperCAmelCase_     : Any			     =					AutoTokenizer.from_pretrained(lowerCAmelCase_	,	subfolder="qformer_tokenizer"						)
				UpperCAmelCase_     : Optional[int]			     =					cls._get_arguments_from_pretrained(lowerCAmelCase_	,	**lowerCAmelCase_						)
				args.append(lowerCAmelCase_						)
				return cls(*lowerCAmelCase_						)











"""simple docstring"""
lowerCamelCase_					=      [
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]

def 				snake_case      (   A__						):
			UpperCAmelCase_     : List[str]			     =					{"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}
			UpperCAmelCase_     : Optional[Any]			     =					0
			UpperCAmelCase_     : Tuple			     =					0
			while place < len(A__						):
						if (place + 1 < len(A__						)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Union[str, Any]			     =					[]
			for arabic, roman in ROMAN:
						((UpperCAmelCase_)     ,	(UpperCAmelCase_))     : str			     =					divmod(A__	,A__						)
						result.append(roman * factor						)
						if number == 0:
									break
			return "".join(A__						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



