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        snake_case__							(					_UpperCamelCase				):
   _SCREAMING_SNAKE_CASE						:			Dict         =   ["image_processor", "tokenizer"]
   _SCREAMING_SNAKE_CASE						:			List[Any]         =   "BlipImageProcessor"
   _SCREAMING_SNAKE_CASE						:			Optional[int]         =   ("BertTokenizer", "BertTokenizerFast")



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




         '''simple docstring'''




         snake_case_  :      Dict  	=							False
         super().__init__(a_      ,						a_ )
         snake_case_  :      str  	=							self.image_processor



   def __call__(					self : Union[str, Any]      ,						A__ : Tuple = None      ,						A__ : Any = None      ,						A__ : str = True      ,						A__ : Tuple = False      ,						A__ : Optional[Any] = None      ,						A__ : Optional[Any] = None      ,						A__ : Dict = 0      ,						A__ : List[Any] = None      ,						A__ : int = None      ,						A__ : Dict = False      ,						A__ : Dict = False      ,						A__ : Any = False      ,						A__ : List[str] = False      ,						A__ : int = False      ,						A__ : int = True      ,						A__ : List[str] = None      ,						**A__ : Union[str, 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:
               snake_case_  :      Tuple  	=							self.tokenizer
               snake_case_  :      Any  	=							self.tokenizer(
                   text=a_      ,						add_special_tokens=a_      ,						padding=a_      ,						truncation=a_      ,						max_length=a_      ,						stride=a_      ,						pad_to_multiple_of=a_      ,						return_attention_mask=a_      ,						return_overflowing_tokens=a_      ,						return_special_tokens_mask=a_      ,						return_offsets_mapping=a_      ,						return_token_type_ids=a_      ,						return_length=a_      ,						verbose=a_      ,						return_tensors=a_      ,						**a_      ,						)
               return text_encoding

         # add pixel_values
         snake_case_  :      Union[str, Any]  	=							self.image_processor(a_      ,						return_tensors=a_ )

         if text is not None:
               snake_case_  :      Optional[int]  	=							self.tokenizer(
                   text=a_      ,						add_special_tokens=a_      ,						padding=a_      ,						truncation=a_      ,						max_length=a_      ,						stride=a_      ,						pad_to_multiple_of=a_      ,						return_attention_mask=a_      ,						return_overflowing_tokens=a_      ,						return_special_tokens_mask=a_      ,						return_offsets_mapping=a_      ,						return_token_type_ids=a_      ,						return_length=a_      ,						verbose=a_      ,						return_tensors=a_      ,						**a_      ,						)
         else:
               snake_case_  :      Tuple  	=							None

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

         return encoding_image_processor



   def        UpperCAmelCase__							(					self : Tuple      ,						*A__ : Dict      ,						**A__ : List[Any] )				->  List[str]:




         '''simple docstring'''




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



   def        UpperCAmelCase__							(					self : str      ,						*A__ : List[str]      ,						**A__ : Any )				->  Union[str, Any]:




         '''simple docstring'''




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



   @property
   def        UpperCAmelCase__							(					self : List[str] )				->  Dict:




         '''simple docstring'''




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






'''simple docstring'''




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 							lowercase_						(		nn.Module			):

  '''simple docstring'''





  def __init__(  self ,			a_ = 1_6 ,			a_ = 8_8 ,			a_ = None ,			a_ = 1 ,			a_ = 0.0 ,			a_ = 3_2 ,			a_ = None ,			a_ = False ,			a_ = None ,			a_ = None ,			a_ = "geglu" ,			a_ = None ,			)				->					List[str]:


    """simple docstring"""
    super().__init__()
    UpperCAmelCase    				=						nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=a_ ,			attention_head_dim=a_ ,			in_channels=a_ ,			num_layers=a_ ,			dropout=a_ ,			norm_num_groups=a_ ,			cross_attention_dim=a_ ,			attention_bias=a_ ,			sample_size=a_ ,			num_vector_embeds=a_ ,			activation_fn=a_ ,			num_embeds_ada_norm=a_ ,			)
            for _ in range(2  )
        ]  )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase    				=						0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase    				=						[7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase    				=						[1, 0]





  def     snake_case_      (  self ,			a_ ,			a_ ,			a_=None ,			a_=None ,			a_=None ,			a_ = True ,			)				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						hidden_states

    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						0
    # attention_mask is not used yet
    for i in range(2  ):
      # for each of the two transformers, pass the corresponding condition tokens
      UpperCAmelCase    				=						encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
      UpperCAmelCase    				=						self.transformer_index_for_condition[i]
      UpperCAmelCase    				=						self.transformers[transformer_index](
          a_ ,			encoder_hidden_states=a_ ,			timestep=a_ ,			cross_attention_kwargs=a_ ,			return_dict=a_ ,			)[0]
      encoded_states.append(encoded_state - input_states  )
      tokens_start += self.condition_lengths[i]

    UpperCAmelCase    				=						encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase    				=						output_states + input_states

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=a_  )


