



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 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     ['''image_processor''', '''tokenizer''']
   SCREAMING_SNAKE_CASE__							=     '''BlipImageProcessor'''
   SCREAMING_SNAKE_CASE__							=     '''AutoTokenizer'''





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  False
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  self.image_processor





   def __call__(	self       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = False       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = 0       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = False       ,     _lowerCAmelCase = False       ,     _lowerCAmelCase = False       ,     _lowerCAmelCase = False       ,     _lowerCAmelCase = False       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         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:
               _lowerCAmelCase 			=  self.tokenizer
               _lowerCAmelCase 			=  self.tokenizer(
                   text=_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase       ,     padding=_lowerCAmelCase       ,     truncation=_lowerCAmelCase       ,     max_length=_lowerCAmelCase       ,     stride=_lowerCAmelCase       ,     pad_to_multiple_of=_lowerCAmelCase       ,     return_attention_mask=_lowerCAmelCase       ,     return_overflowing_tokens=_lowerCAmelCase       ,     return_special_tokens_mask=_lowerCAmelCase       ,     return_offsets_mapping=_lowerCAmelCase       ,     return_token_type_ids=_lowerCAmelCase       ,     return_length=_lowerCAmelCase       ,     verbose=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase       ,     )
               return text_encoding

         # add pixel_values
         _lowerCAmelCase 			=  self.image_processor(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase			)

         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(
                   text=_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase       ,     padding=_lowerCAmelCase       ,     truncation=_lowerCAmelCase       ,     max_length=_lowerCAmelCase       ,     stride=_lowerCAmelCase       ,     pad_to_multiple_of=_lowerCAmelCase       ,     return_attention_mask=_lowerCAmelCase       ,     return_overflowing_tokens=_lowerCAmelCase       ,     return_special_tokens_mask=_lowerCAmelCase       ,     return_offsets_mapping=_lowerCAmelCase       ,     return_token_type_ids=_lowerCAmelCase       ,     return_length=_lowerCAmelCase       ,     verbose=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase       ,     )
         else:
               _lowerCAmelCase 			=  None

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

         return encoding_image_processor





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer.model_input_names
         _lowerCAmelCase 			=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
    from .camera import create_pan_cameras
    from .pipeline_shap_e import ShapEPipeline
    from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
    from .renderer import (
        BoundingBoxVolume,
        ImportanceRaySampler,
        MLPNeRFModelOutput,
        MLPNeRSTFModel,
        ShapEParamsProjModel,
        ShapERenderer,
        StratifiedRaySampler,
        VoidNeRFModel,
    )