




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


lowercase__     						=						logging.get_logger(__name__)






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      *lowercase  ,      **lowercase  ):
							warnings.warn(
							    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
							    ' use DeiTImageProcessor instead.'  ,      lowercase  ,      )
							super().__init__(*lowercase  ,      **lowercase  )





"""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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			["""image_processor""", """tokenizer"""]
			lowerCamelCase__      =			"""BlipImageProcessor"""
			lowerCamelCase__      =			"""AutoTokenizer"""
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							super().__init__(lowercase  ,      lowercase  )

							# add QFormer tokenizer
							_lowerCamelCase     :			int    =    qformer_tokenizer
			def __call__(  self  ,      lowercase = None  ,      lowercase = None  ,      lowercase = True  ,      lowercase = False  ,      lowercase = None  ,      lowercase = None  ,      lowercase = 0  ,      lowercase = None  ,      lowercase = None  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      **lowercase  ,      ):
							if images is None and text is None:
											raise ValueError('You have to specify at least images or text.'  )

							_lowerCamelCase     :			int    =    BatchFeature()

							if text is not None:
											_lowerCamelCase     :			List[str]    =    self.tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											encoding.update(lowercase  )
											_lowerCamelCase     :			List[str]    =    self.qformer_tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											_lowerCamelCase     :			List[Any]    =    qformer_text_encoding.pop('input_ids'  )
											_lowerCamelCase     :			Tuple    =    qformer_text_encoding.pop('attention_mask'  )

							if images is not None:
											_lowerCamelCase     :			int    =    self.image_processor(lowercase  ,      return_tensors=lowercase  )
											encoding.update(lowercase  )

							return encoding
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.batch_decode(*lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.decode(*lowercase  ,      **lowercase  )
			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.tokenizer.model_input_names
							_lowerCamelCase     :			Any    =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
			def 					A_    (  self  ,      lowercase  ,      **lowercase  ):
							if os.path.isfile(lowercase  ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''  )
							os.makedirs(lowercase  ,      exist_ok=lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    os.path.join(lowercase  ,      'qformer_tokenizer'  )
							self.qformer_tokenizer.save_pretrained(lowercase  )
							return super().save_pretrained(lowercase  ,      **lowercase  )






			@classmethod
			def 					A_    (  cls  ,      lowercase  ,      **lowercase  ):
							_lowerCamelCase     :			Union[str, Any]    =    AutoTokenizer.from_pretrained(lowercase  ,      subfolder='qformer_tokenizer'  )
							_lowerCamelCase     :			Dict    =    cls._get_arguments_from_pretrained(lowercase  ,      **lowercase  )
							args.append(lowercase  )
							return cls(*lowercase  )