


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__							:  Optional[int]      =['image_processor', 'tokenizer']
      UpperCamelCase__							:  Optional[int]      ='BlipImageProcessor'
      UpperCamelCase__							:  str      ='AutoTokenizer'






      def __init__(					self						:     Dict			,   lowercase_						:     Any			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     List[str] )					->						str:
             """simple docstring"""




             super().__init__(lowercase_			,   lowercase_ )

             # add QFormer tokenizer
             _lowerCamelCase       :		Any		=qformer_tokenizer






      def __call__(					self						:     List[str]			,   lowercase_						:     ImageInput = None			,   lowercase_						:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,   lowercase_						:     bool = True			,   lowercase_						:     Union[bool, str, PaddingStrategy] = False			,   lowercase_						:     Union[bool, str, TruncationStrategy] = None			,   lowercase_						:     Optional[int] = None			,   lowercase_						:     int = 0			,   lowercase_						:     Optional[int] = None			,   lowercase_						:     Optional[bool] = None			,   lowercase_						:     bool = False			,   lowercase_						:     bool = False			,   lowercase_						:     bool = False			,   lowercase_						:     bool = False			,   lowercase_						:     bool = False			,   lowercase_						:     bool = True			,   lowercase_						:     Optional[Union[str, TensorType]] = None			,   **lowercase_						:     Union[str, Any]			,   )					->						BatchFeature:
             """simple docstring"""




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

             _lowerCamelCase       :		str		=BatchFeature()

             if text is not None:
                    _lowerCamelCase       :		Dict		=self.tokenizer(
                        text=lowercase_			,   add_special_tokens=lowercase_			,   padding=lowercase_			,   truncation=lowercase_			,   max_length=lowercase_			,   stride=lowercase_			,   pad_to_multiple_of=lowercase_			,   return_attention_mask=lowercase_			,   return_overflowing_tokens=lowercase_			,   return_special_tokens_mask=lowercase_			,   return_offsets_mapping=lowercase_			,   return_token_type_ids=lowercase_			,   return_length=lowercase_			,   verbose=lowercase_			,   return_tensors=lowercase_			,   **lowercase_			,   )
                    encoding.update(lowercase_ )
                    _lowerCamelCase       :		Optional[int]		=self.qformer_tokenizer(
                        text=lowercase_			,   add_special_tokens=lowercase_			,   padding=lowercase_			,   truncation=lowercase_			,   max_length=lowercase_			,   stride=lowercase_			,   pad_to_multiple_of=lowercase_			,   return_attention_mask=lowercase_			,   return_overflowing_tokens=lowercase_			,   return_special_tokens_mask=lowercase_			,   return_offsets_mapping=lowercase_			,   return_token_type_ids=lowercase_			,   return_length=lowercase_			,   verbose=lowercase_			,   return_tensors=lowercase_			,   **lowercase_			,   )
                    _lowerCamelCase       :		Any		=qformer_text_encoding.pop('input_ids' )
                    _lowerCamelCase       :		Tuple		=qformer_text_encoding.pop('attention_mask' )

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

             return encoding






      def        lowerCamelCase       (					self						:     Optional[Any]			,   *lowercase_						:     int			,   **lowercase_						:     List[str] )					->						Optional[int]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   *lowercase_						:     List[Any]			,   **lowercase_						:     Dict )					->						Optional[Any]:
             """simple docstring"""




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






      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def        lowerCamelCase       (					self						:     Tuple )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=self.tokenizer.model_input_names
             _lowerCamelCase       :		int		=self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     List[Any]			,   **lowercase_						:     Optional[int] )					->						List[str]:
             """simple docstring"""




             if os.path.isfile(lowercase_ ):
                    raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
             os.makedirs(lowercase_			,   exist_ok=lowercase_ )
             _lowerCamelCase       :		List[Any]		=os.path.join(lowercase_			,   'qformer_tokenizer' )
             self.qformer_tokenizer.save_pretrained(lowercase_ )
             return super().save_pretrained(lowercase_			,   **lowercase_ )






      @classmethod
      def        lowerCamelCase       (					cls						:     str			,   lowercase_						:     str			,   **lowercase_						:     Optional[Any] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=AutoTokenizer.from_pretrained(lowercase_			,   subfolder='qformer_tokenizer' )
             _lowerCamelCase       :		Optional[int]		=cls._get_arguments_from_pretrained(lowercase_			,   **lowercase_ )
             args.append(lowercase_ )
             return cls(*lowercase_ )






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,
 )


