





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_       (     lowercase__    ):
       lowerCamelCase_           =					["image_processor", "tokenizer"]
       lowerCamelCase_           =					"BlipImageProcessor"
       lowerCamelCase_           =					"AutoTokenizer"


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



         """simple docstring"""



         super().__init__(UpperCAmelCase__							,				UpperCAmelCase__		)

         # add QFormer tokenizer
         SCREAMING_SNAKE_CASE__			=      qformer_tokenizer


       def __call__(							self    :Union[str, 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    :Union[str, Any]							,				)   ->   Dict:



         """simple docstring"""



         if images is None and text is None:
           raise ValueError("""You have to specify at least images or text."""		)

         SCREAMING_SNAKE_CASE__			=      BatchFeature()

         if text is not None:
           SCREAMING_SNAKE_CASE__			=      self.tokenizer(
               text=UpperCAmelCase__							,				add_special_tokens=UpperCAmelCase__							,				padding=UpperCAmelCase__							,				truncation=UpperCAmelCase__							,				max_length=UpperCAmelCase__							,				stride=UpperCAmelCase__							,				pad_to_multiple_of=UpperCAmelCase__							,				return_attention_mask=UpperCAmelCase__							,				return_overflowing_tokens=UpperCAmelCase__							,				return_special_tokens_mask=UpperCAmelCase__							,				return_offsets_mapping=UpperCAmelCase__							,				return_token_type_ids=UpperCAmelCase__							,				return_length=UpperCAmelCase__							,				verbose=UpperCAmelCase__							,				return_tensors=UpperCAmelCase__							,				**UpperCAmelCase__							,				)
           encoding.update(UpperCAmelCase__		)
           SCREAMING_SNAKE_CASE__			=      self.qformer_tokenizer(
               text=UpperCAmelCase__							,				add_special_tokens=UpperCAmelCase__							,				padding=UpperCAmelCase__							,				truncation=UpperCAmelCase__							,				max_length=UpperCAmelCase__							,				stride=UpperCAmelCase__							,				pad_to_multiple_of=UpperCAmelCase__							,				return_attention_mask=UpperCAmelCase__							,				return_overflowing_tokens=UpperCAmelCase__							,				return_special_tokens_mask=UpperCAmelCase__							,				return_offsets_mapping=UpperCAmelCase__							,				return_token_type_ids=UpperCAmelCase__							,				return_length=UpperCAmelCase__							,				verbose=UpperCAmelCase__							,				return_tensors=UpperCAmelCase__							,				**UpperCAmelCase__							,				)
           SCREAMING_SNAKE_CASE__			=      qformer_text_encoding.pop("""input_ids"""		)
           SCREAMING_SNAKE_CASE__			=      qformer_text_encoding.pop("""attention_mask"""		)

         if images is not None:
           SCREAMING_SNAKE_CASE__			=      self.image_processor(UpperCAmelCase__							,				return_tensors=UpperCAmelCase__		)
           encoding.update(UpperCAmelCase__		)

         return encoding


       def      _snake_case  (							self    :Dict							,				*__A    :Optional[int]							,				**__A    :Optional[Any]		)   ->   Optional[int]:



         """simple docstring"""



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


       def      _snake_case  (							self    :str							,				*__A    :Dict							,				**__A    :Optional[Any]		)   ->   str:



         """simple docstring"""



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


       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def      _snake_case  (							self    :List[str]		)   ->   Tuple:



         """simple docstring"""



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


       def      _snake_case  (							self    :Dict							,				__A    :int							,				**__A    :str		)   ->   List[str]:



         """simple docstring"""



         if os.path.isfile(UpperCAmelCase__		):
           raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''		)
         os.makedirs(UpperCAmelCase__							,				exist_ok=UpperCAmelCase__		)
         SCREAMING_SNAKE_CASE__			=      os.path.join(UpperCAmelCase__							,				"""qformer_tokenizer"""		)
         self.qformer_tokenizer.save_pretrained(UpperCAmelCase__		)
         return super().save_pretrained(UpperCAmelCase__							,				**UpperCAmelCase__		)


       @classmethod
       def      _snake_case  (							cls    :Optional[int]							,				__A    :Any							,				**__A    :List[str]		)   ->   Dict:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      AutoTokenizer.from_pretrained(UpperCAmelCase__							,				subfolder="""qformer_tokenizer"""		)
         SCREAMING_SNAKE_CASE__			=      cls._get_arguments_from_pretrained(UpperCAmelCase__							,				**UpperCAmelCase__		)
         args.append(UpperCAmelCase__		)
         return cls(*UpperCAmelCase__		)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase   				=			logging.get_logger(__name__)




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"timm_backbone"


							def __init__(							self    :Union[str, Any]							,				__A    :str=None							,				__A    :Union[str, Any]=3							,				__A    :str=True							,				__A    :Any=True							,				__A    :Optional[Any]=None							,				**__A    :List[str]							,				)   ->   Tuple:



									"""simple docstring"""



									super().__init__(**__A		)
									SCREAMING_SNAKE_CASE__			=      backbone
									SCREAMING_SNAKE_CASE__			=      num_channels
									SCREAMING_SNAKE_CASE__			=      features_only
									SCREAMING_SNAKE_CASE__			=      use_pretrained_backbone
									SCREAMING_SNAKE_CASE__			=      True
									SCREAMING_SNAKE_CASE__			=      out_indices if out_indices is not None else (-1,)