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 		_lowercase						(					_UpperCAmelCase       ):
 lowercase				     =   ['image_processor', 'tokenizer']
 lowercase				     =   'BlipImageProcessor'
 lowercase				     =   'AutoTokenizer'

 def __init__( self	:							List[Any]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					False
  super().__init__(lowercase_   ,      lowercase_			)
  UpperCamelCase_					:						Tuple =					self.image_processor

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

  """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:
   UpperCamelCase_					:						str =					self.tokenizer
   UpperCamelCase_					:						Optional[Any] =					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_   ,      )
   return text_encoding

  # add pixel_values
  UpperCamelCase_					:						Optional[int] =					self.image_processor(lowercase_   ,      return_tensors=lowercase_			)

  if text is not None:
   UpperCamelCase_					:						Union[str, Any] =					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_   ,      )
  else:
   UpperCamelCase_					:						str =					None

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

  return encoding_image_processor

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      *snake_case	:							Any   ,      **snake_case	:							str			)   ->						List[str]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      *snake_case	:							Tuple   ,      **snake_case	:							Optional[Any]			)   ->						Tuple:

  """simple docstring"""





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


 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.tokenizer.model_input_names
  UpperCamelCase_					:						Union[str, Any] =					self.image_processor.model_input_names
  return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






from typing import Mapping

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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							't5'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self :       Union[str, Any],lowercase_ :       int=3_2_1_2_8,lowercase_ :       int=5_1_2,lowercase_ :       List[str]=6_4,lowercase_ :       Tuple=2_0_4_8,lowercase_ :       Any=6,lowercase_ :       List[str]=None,lowercase_ :       Union[str, Any]=8,lowercase_ :       int=3_2,lowercase_ :       Dict=1_2_8,lowercase_ :       Optional[int]=0.1,lowercase_ :       List[str]=1E-6,lowercase_ :       Tuple=1.0,lowercase_ :       Any="relu",lowercase_ :       Union[str, Any]=True,lowercase_ :       Optional[Any]=True,lowercase_ :       int=0,lowercase_ :       str=1,**lowercase_ :       str,)->    Any:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   d_model
        A__          =   d_kv
        A__          =   d_ff
        A__          =   num_layers
        A__          =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A__          =   num_heads
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   feed_forward_proj
        A__          =   use_cache

        A__          =   self.feed_forward_proj.split('-'		)
        A__          =   act_info[-1]
        A__          =   act_info[0] == 'gated'

        if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 2:
            raise ValueError(
                F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                '\'gated-gelu\' or \'relu\''		)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            A__          =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,**lowercase_,)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    def 						snake_case__     (      self :       Tuple		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        A__          =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
            A__          =   'past_encoder_sequence + sequence'
            A__          =   {0: 'batch'}
            A__          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
            A__          =   {0: 'batch', 1: 'decoder_sequence'}
            A__          =   {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
            self.fill_with_past_key_values_(lowercase_,direction='inputs'		)

        return common_inputs


    @property
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        return 1_3

