






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__ (							__snake_case							):
		__snake_case      :       List[str]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Any     =   "BlipImageProcessor"
		__snake_case      :       Any     =   "AutoTokenizer"







		def __init__(					self       ,A__       ,A__       ,A__		):
						super().__init__(A__       ,A__		)

						# add QFormer tokenizer
						_A	:				Optional[Any]   =      qformer_tokenizer







		def __call__(					self       ,A__ = None       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						if images is None and text is None:
										raise ValueError('''You have to specify at least images or text.'''		)

						_A	:				str   =      BatchFeature()

						if text is not None:
										_A	:				str   =      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__       ,)
										encoding.update(A__		)
										_A	:				Optional[int]   =      self.qformer_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__       ,)
										_A	:				List[Any]   =      qformer_text_encoding.pop('''input_ids'''		)
										_A	:				List[Any]   =      qformer_text_encoding.pop('''attention_mask'''		)

						if images is not None:
										_A	:				Optional[int]   =      self.image_processor(A__       ,return_tensors=A__		)
										encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def  A__      (					self		):
						_A	:				List[str]   =      self.tokenizer.model_input_names
						_A	:				Any   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)







		def  A__      (					self       ,A__       ,**A__		):
						if os.path.isfile(A__		):
										raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
						os.makedirs(A__       ,exist_ok=A__		)
						_A	:				List[Any]   =      os.path.join(A__       ,'''qformer_tokenizer'''		)
						self.qformer_tokenizer.save_pretrained(A__		)
						return super().save_pretrained(A__       ,**A__		)







		@classmethod
		def  A__      (					cls       ,A__       ,**A__		):
						_A	:				Union[str, Any]   =      AutoTokenizer.from_pretrained(A__       ,subfolder='''qformer_tokenizer'''		)
						_A	:				Tuple   =      cls._get_arguments_from_pretrained(A__       ,**A__		)
						args.append(A__		)
						return cls(*A__		)











from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						super().__init__(A__       ,A__		)







		def __call__(					self       ,A__       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      self.tokenizer(
						    text=A__       ,add_special_tokens=A__       ,padding=A__       ,truncation=A__       ,max_length=A__       ,stride=A__       ,pad_to_multiple_of=A__       ,return_token_type_ids=A__       ,return_attention_mask=A__       ,return_overflowing_tokens=A__       ,return_special_tokens_mask=A__       ,return_offsets_mapping=A__       ,return_length=A__       ,verbose=A__       ,return_tensors=A__       ,**A__       ,)
						# add pixel_values + pixel_mask
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



