



"""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 					a	(  UpperCamelCase_       ):
  UpperCamelCase	:				Union[str, Any]     						=					['image_processor', 'tokenizer']
  UpperCamelCase	:				str     						=					'BlipImageProcessor'
  UpperCamelCase	:				str     						=					'AutoTokenizer'



  def __init__(						self :					List[Any]       ,		lowerCAmelCase :					List[Any]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					List[Any]		)     ->					Optional[Any]:


     '''simple docstring'''




     super().__init__(__a       ,		__a		)

     # add QFormer tokenizer
     SCREAMING_SNAKE_CASE_: Optional[int]     			=qformer_tokenizer



  def __call__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Tuple = None       ,		lowerCAmelCase :					str = None       ,		lowerCAmelCase :					Optional[Any] = True       ,		lowerCAmelCase :					str = False       ,		lowerCAmelCase :					Tuple = None       ,		lowerCAmelCase :					Tuple = None       ,		lowerCAmelCase :					str = 0       ,		lowerCAmelCase :					Optional[int] = None       ,		lowerCAmelCase :					Any = None       ,		lowerCAmelCase :					List[Any] = False       ,		lowerCAmelCase :					int = False       ,		lowerCAmelCase :					str = False       ,		lowerCAmelCase :					List[str] = False       ,		lowerCAmelCase :					Tuple = False       ,		lowerCAmelCase :					Any = True       ,		lowerCAmelCase :					str = None       ,		**lowerCAmelCase :					Optional[Any]       ,		)     ->					BatchFeature:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: Dict     			=BatchFeature()

     if text is not None:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=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		)
        SCREAMING_SNAKE_CASE_: Dict     			=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       ,		)
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=qformer_text_encoding.pop("""input_ids"""		)
        SCREAMING_SNAKE_CASE_: List[str]     			=qformer_text_encoding.pop("""attention_mask"""		)

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

     return encoding



  def 	lowerCamelCase__       (						self :					str       ,		*lowerCAmelCase :					Tuple       ,		**lowerCAmelCase :					Optional[int]		)     ->					Dict:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Tuple       ,		*lowerCAmelCase :					Optional[int]       ,		**lowerCAmelCase :					Dict		)     ->					Optional[Any]:


     '''simple docstring'''




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



  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def 	lowerCamelCase__       (						self :					int		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE_: Tuple     			=self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Union[str, Any]       ,		**lowerCAmelCase :					List[str]		)     ->					List[Any]:


     '''simple docstring'''




     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		)
     SCREAMING_SNAKE_CASE_: str     			=os.path.join(__a       ,		"""qformer_tokenizer"""		)
     self.qformer_tokenizer.save_pretrained(__a		)
     return super().save_pretrained(__a       ,		**__a		)





  @classmethod
  def 	lowerCamelCase__       (						cls :					str       ,		lowerCAmelCase :					Optional[int]       ,		**lowerCAmelCase :					str		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=AutoTokenizer.from_pretrained(__a       ,		subfolder="""qformer_tokenizer"""		)
     SCREAMING_SNAKE_CASE_: Dict     			=cls._get_arguments_from_pretrained(__a       ,		**__a		)
     args.append(__a		)
     return cls(*__a		)








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

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase     =			{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			["""DPTFeatureExtractor"""]
   _UpperCAmelCase     =			["""DPTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPTForDepthEstimation""",
       """DPTForSemanticSegmentation""",
       """DPTModel""",
       """DPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_dpt import DPTFeatureExtractor
      from .image_processing_dpt import DPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpt import (
          DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPTForDepthEstimation,
          DPTForSemanticSegmentation,
          DPTModel,
          DPTPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



