





'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase		      =	'''BlipImageProcessor'''
     lowerCAmelCase		      =	('''BertTokenizer''', '''BertTokenizerFast''')


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	False
          super().__init__(_UpperCAmelCase						,     _UpperCAmelCase)
          __A						:			Optional[int]								=	self.image_processor


     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						,     ):

          '''simple docstring'''






          if images is None and text is None:
               raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
               __A						:			str								=	self.tokenizer
               __A						:			List[Any]								=	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						,     )
               return text_encoding

          # add pixel_values
          __A						:			Union[str, Any]								=	self.image_processor(_UpperCAmelCase						,     return_tensors=_UpperCAmelCase)

          if text is not None:
               __A						:			str								=	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						,     )
          else:
               __A						:			Optional[Any]								=	None

          if text_encoding is not None:
               encoding_image_processor.update(_UpperCAmelCase)

          return encoding_image_processor


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






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


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	self.tokenizer.model_input_names
          __A						:			Optional[Any]								=	self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






'''simple docstring'''




import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
  lowercase__   :				Dict      =   {
      '''linear''': PIL.Image.Resampling.BILINEAR,
      '''bilinear''': PIL.Image.Resampling.BILINEAR,
      '''bicubic''': PIL.Image.Resampling.BICUBIC,
      '''lanczos''': PIL.Image.Resampling.LANCZOS,
      '''nearest''': PIL.Image.Resampling.NEAREST,
  }
else:
  lowercase__   :				Any      =   {
      '''linear''': PIL.Image.LINEAR,
      '''bilinear''': PIL.Image.BILINEAR,
      '''bicubic''': PIL.Image.BICUBIC,
      '''lanczos''': PIL.Image.LANCZOS,
      '''nearest''': PIL.Image.NEAREST,
  }






def 	_lowerCAmelCase      (  __snake_case  :			Any      )					->					Optional[Any]:
     __A						:			Dict								=	(images / 2 + 0.5).clamp(0			,							1      )
     __A						:			str								=	images.cpu().permute(0			,							2			,							3			,							1      ).float().numpy()
     __A						:			Dict								=	numpy_to_pil(__snake_case      )
     return images







def 	_lowerCAmelCase      (  __snake_case  :			List[Any]      )					->					Optional[Any]:
     if images.ndim == 3:
          __A						:			List[Any]								=	images[None, ...]
     __A						:			List[str]								=	(images * 2_55).round().astype('uint8'      )
     if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          __A						:			str								=	[Image.fromarray(image.squeeze()			,							mode='L'      ) for image in images]
     else:
          __A						:			str								=	[Image.fromarray(__snake_case      ) for image in images]

     return pil_images