



"""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    SCREAMING_SNAKE_CASE__ (							SCREAMING_SNAKE_CASE_   ):
				lowercase__      =   ["image_processor", "tokenizer"]
				lowercase__      =   "BlipImageProcessor"
				lowercase__      =   "AutoTokenizer"

				def __init__(				self			:				int ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				str):







						"""simple docstring"""







						super().__init__(UpperCamelCase__ ,  UpperCamelCase__)

						# add QFormer tokenizer
						lowercase_								= qformer_tokenizer

				def __call__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				int = None ,  lowerCAmelCase_			:				Tuple = None ,  lowerCAmelCase_			:				Dict = True ,  lowerCAmelCase_			:				List[str] = False ,  lowerCAmelCase_			:				Optional[int] = None ,  lowerCAmelCase_			:				Optional[int] = None ,  lowerCAmelCase_			:				Optional[Any] = 0 ,  lowerCAmelCase_			:				Optional[Any] = None ,  lowerCAmelCase_			:				Any = None ,  lowerCAmelCase_			:				str = False ,  lowerCAmelCase_			:				Tuple = False ,  lowerCAmelCase_			:				Dict = False ,  lowerCAmelCase_			:				List[str] = False ,  lowerCAmelCase_			:				List[Any] = False ,  lowerCAmelCase_			:				Tuple = True ,  lowerCAmelCase_			:				Optional[Any] = None ,  **lowerCAmelCase_			:				Any ,  ):







						"""simple docstring"""







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

						lowercase_								= BatchFeature()

						if text is not None:
								lowercase_								= 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__)
								lowercase_								= 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__ ,  )
								lowercase_								= qformer_text_encoding.pop("""input_ids""")
								lowercase_								= qformer_text_encoding.pop("""attention_mask""")

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

						return encoding

				def 					_UpperCAmelCase	(				self			:				Tuple ,  *lowerCAmelCase_			:				Any ,  **lowerCAmelCase_			:				Any):







						"""simple docstring"""







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

				def 					_UpperCAmelCase	(				self			:				int ,  *lowerCAmelCase_			:				int ,  **lowerCAmelCase_			:				List[str]):







						"""simple docstring"""







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

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







						"""simple docstring"""







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

				def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				Tuple ,  **lowerCAmelCase_			:				Dict):







						"""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__)
						lowercase_								= os.path.join(UpperCamelCase__ ,  """qformer_tokenizer""")
						self.qformer_tokenizer.save_pretrained(UpperCamelCase__)
						return super().save_pretrained(UpperCamelCase__ ,  **UpperCamelCase__)




				@classmethod
				def 					_UpperCAmelCase	(				cls			:				List[str] ,  lowerCAmelCase_			:				List[str] ,  **lowerCAmelCase_			:				int):







						"""simple docstring"""







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






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase						:		List[Any]			= {"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
       from .tokenization_bertweet import BertweetTokenizer

else:
       import sys

       UpperCAmelCase						:		Tuple			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

