


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		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  ["image_processor", "tokenizer"]
							__UpperCAmelCase   :							List[str]					  =  "BlipImageProcessor"
							__UpperCAmelCase   :							List[str]					  =  "AutoTokenizer"

							def __init__(							self	:  int   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  List[Any]    )     ->   Union[str, Any]:
														super().__init__(lowerCamelCase   ,     lowerCamelCase    )

														# add QFormer tokenizer
														__snake_case		:      Any										=       qformer_tokenizer

							def __call__(							self	:  List[str]   ,     lowerCamelCase	:  ImageInput = None   ,     lowerCamelCase	:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Union[bool, str, PaddingStrategy] = False   ,     lowerCamelCase	:  Union[bool, str, TruncationStrategy] = None   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  int = 0   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[bool] = None   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     **lowerCamelCase	:  Tuple   ,     )     ->   BatchFeature:
														if images is None and text is None:
																					raise ValueError("You have to specify at least images or text."    )

														__snake_case		:      List[str]										=       BatchFeature()

														if text is not None:
																					__snake_case		:      int										=       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   ,     )
																					encoding.update(lowerCamelCase    )
																					__snake_case		:      Optional[int]										=       self.qformer_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   ,     )
																					__snake_case		:      List[str]										=       qformer_text_encoding.pop("input_ids"    )
																					__snake_case		:      int										=       qformer_text_encoding.pop("attention_mask"    )

														if images is not None:
																					__snake_case		:      Optional[Any]										=       self.image_processor(lowerCamelCase   ,     return_tensors=lowerCamelCase    )
																					encoding.update(lowerCamelCase    )

														return encoding

							def    __snake_case (							self	:  List[str]   ,     *lowerCamelCase	:  Tuple   ,     **lowerCamelCase	:  Tuple    )     ->   str:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  List[str]   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   str:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )

							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def    __snake_case (							self	:  Dict    )     ->   Any:
														__snake_case		:      Dict										=       self.tokenizer.model_input_names
														__snake_case		:      Dict										=       self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )

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






							@classmethod
							def    __snake_case (							cls	:  Union[str, Any]   ,     lowerCamelCase	:  int   ,     **lowerCamelCase	:  List[Any]    )     ->   Union[str, Any]:
														__snake_case		:      int										=       AutoTokenizer.from_pretrained(lowerCamelCase   ,     subfolder="qformer_tokenizer"    )
														__snake_case		:      str										=       cls._get_arguments_from_pretrained(lowerCamelCase   ,     **lowerCamelCase    )
														args.append(lowerCamelCase    )
														return cls(*lowerCamelCase    )



from ..utils import DummyObject, requires_backends

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							int					  =  ["speech"]

							def __init__(							self	:  List[Any]   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Dict:
														requires_backends(self   ,     ["speech"]    )

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  ["speech"]

							def __init__(							self	:  int   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  List[Any]    )     ->   Optional[int]:
														requires_backends(self   ,     ["speech"]    )

