


"""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  (					a__				):

     '''simple docstring'''
     A_       :	int    =		['image_processor', 'tokenizer']
     A_       :	Any    =		'BlipImageProcessor'
     A_       :	List[Any]    =		'AutoTokenizer'


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							str:
          super().__init__(__UpperCAmelCase    ,				__UpperCAmelCase							)

          # add QFormer tokenizer
          _a    	=				qformer_tokenizer


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

          _a    	=				BatchFeature()

          if text is not None:
               _a    	=				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							)
               _a    	=				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    ,				)
               _a    	=				qformer_text_encoding.pop('''input_ids'''							)
               _a    	=				qformer_text_encoding.pop('''attention_mask'''							)

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

          return encoding


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							List[str]:
          return self.tokenizer.batch_decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							Optional[Any]:
          return self.tokenizer.decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				self.tokenizer.model_input_names
          _a    	=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							Any:
          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							)
          _a    	=				os.path.join(__UpperCAmelCase    ,				'''qformer_tokenizer'''							)
          self.qformer_tokenizer.save_pretrained(__UpperCAmelCase							)
          return super().save_pretrained(__UpperCAmelCase    ,				**__UpperCAmelCase							)





     @classmethod
     def 							_UpperCAmelCase      (      cls    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							Dict:
          _a    	=				AutoTokenizer.from_pretrained(__UpperCAmelCase    ,				subfolder='''qformer_tokenizer'''							)
          _a    	=				cls._get_arguments_from_pretrained(__UpperCAmelCase    ,				**__UpperCAmelCase							)
          args.append(__UpperCAmelCase							)
          return cls(*__UpperCAmelCase							)



"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case		=       {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''Pix2StructImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Pix2StructPreTrainedModel''',
       '''Pix2StructForConditionalGeneration''',
       '''Pix2StructVisionModel''',
       '''Pix2StructTextModel''',
   ]

if TYPE_CHECKING:
   from .configuration_pixastruct import (
       PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       PixaStructConfig,
       PixaStructTextConfig,
       PixaStructVisionConfig,
   )
   from .processing_pixastruct import PixaStructProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_pixastruct import PixaStructImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pixastruct import (
          PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
          PixaStructForConditionalGeneration,
          PixaStructPreTrainedModel,
          PixaStructTextModel,
          PixaStructVisionModel,
      )

else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)