





'''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'''




from collections import OrderedDict
from typing import Mapping

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


lowercase__   :				Optional[Any]      =   logging.get_logger(__name__)

lowercase__   :				str      =   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''roberta-prelayernorm'''


     def __init__(						self						,     _UpperCAmelCase=5_0265						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,     bos_token_id=_UpperCAmelCase						,     eos_token_id=_UpperCAmelCase						,     **_UpperCAmelCase)

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




class 			SCREAMING_SNAKE_CASE   (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						:			List[str]								=	{0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						:			Tuple								=	{0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])