


"""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 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					['''image_processor''', '''tokenizer''']
   __UpperCAmelCase							       =					'''BlipImageProcessor'''
   __UpperCAmelCase							       =					'''AutoTokenizer'''





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

      # add QFormer tokenizer
      _lowerCamelCase	:					Dict   =		qformer_tokenizer





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

      _lowerCamelCase	:					Tuple   =		BatchFeature()

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

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

      return encoding





   def 		UpperCamelCase_  (							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  List[str]:
      return self.tokenizer.decode(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					List[Any]   =		self.tokenizer.model_input_names
      _lowerCamelCase	:					List[str]   =		self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





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





   @classmethod
   def 		UpperCamelCase_  (							cls      ,   SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE      ,   subfolder="""qformer_tokenizer""")
      _lowerCamelCase	:					Optional[Any]   =		cls._get_arguments_from_pretrained(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
      args.append(SCREAMING_SNAKE_CASE)
      return cls(*SCREAMING_SNAKE_CASE)





"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''ibert'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=3_0522      ,   SCREAMING_SNAKE_CASE=768      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3072      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=1e-1_2      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE="absolute"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE="none"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Any:
      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE      ,   bos_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		vocab_size
      _lowerCamelCase	:					Dict   =		hidden_size
      _lowerCamelCase	:					List[str]   =		num_hidden_layers
      _lowerCamelCase	:					int   =		num_attention_heads
      _lowerCamelCase	:					Tuple   =		hidden_act
      _lowerCamelCase	:					str   =		intermediate_size
      _lowerCamelCase	:					Union[str, Any]   =		hidden_dropout_prob
      _lowerCamelCase	:					Tuple   =		attention_probs_dropout_prob
      _lowerCamelCase	:					Any   =		max_position_embeddings
      _lowerCamelCase	:					Dict   =		type_vocab_size
      _lowerCamelCase	:					List[Any]   =		initializer_range
      _lowerCamelCase	:					Dict   =		layer_norm_eps
      _lowerCamelCase	:					List[Any]   =		position_embedding_type
      _lowerCamelCase	:					Any   =		quant_mode
      _lowerCamelCase	:					List[str]   =		force_dequant







class 	lowercase__				(					A_				):





   @property
   def 		UpperCamelCase_  (							self)  ->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
         _lowerCamelCase	:					Dict   =		{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         _lowerCamelCase	:					Optional[int]   =		{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ])

