



"""simple docstring"""






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class 			__snake_case			(      nn.Module):




  def __init__(					self	:		Tuple		,   __lowerCAmelCase	:		int = 1_6		,   __lowerCAmelCase	:		int = 8_8		,   __lowerCAmelCase	:		Optional[int] = None		,   __lowerCAmelCase	:		int = 1		,   __lowerCAmelCase	:		float = 0.0		,   __lowerCAmelCase	:		int = 3_2		,   __lowerCAmelCase	:		Optional[int] = None		,   __lowerCAmelCase	:		bool = False		,   __lowerCAmelCase	:		Optional[int] = None		,   __lowerCAmelCase	:		Optional[int] = None		,   __lowerCAmelCase	:		str = "geglu"		,   __lowerCAmelCase	:		Optional[int] = None		,   ):






         """simple docstring"""






         super().__init__()
         _lowerCamelCase		:							int            =					nn.ModuleList(
             [
                 TransformeraDModel(
                     num_attention_heads=_lowerCAmelCase		,   attention_head_dim=_lowerCAmelCase		,   in_channels=_lowerCAmelCase		,   num_layers=_lowerCAmelCase		,   dropout=_lowerCAmelCase		,   norm_num_groups=_lowerCAmelCase		,   cross_attention_dim=_lowerCAmelCase		,   attention_bias=_lowerCAmelCase		,   sample_size=_lowerCAmelCase		,   num_vector_embeds=_lowerCAmelCase		,   activation_fn=_lowerCAmelCase		,   num_embeds_ada_norm=_lowerCAmelCase		,   )
                 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
         _lowerCamelCase		:							Tuple            =					0.5

         # The shape of `encoder_hidden_states` is expected to be
         # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
         _lowerCamelCase		:							List[Any]            =					[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])`
         _lowerCamelCase		:							str            =					[1, 0]




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]		,   __lowerCAmelCase	:		Tuple		,   __lowerCAmelCase	:		Optional[Any]		,   __lowerCAmelCase	:		str=None		,   __lowerCAmelCase	:		Dict=None		,   __lowerCAmelCase	:		int=None		,   __lowerCAmelCase	:		bool = True		,   ):






         """simple docstring"""






         _lowerCamelCase		:							int            =					hidden_states

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

         _lowerCamelCase		:							Tuple            =					encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
         _lowerCamelCase		:							Any            =					output_states + input_states

         if not return_dict:
                return (output_states,)

         return TransformeraDModelOutput(sample=_lowerCAmelCase						)






import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  parse(importlib.metadata.version('''torch'''))



def       _lowerCAmelCase    (				lowerCAmelCase_				:Union[str, Version]		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:str				)->Any:
   '''simple docstring'''






   if operation not in STR_OPERATION_TO_FUNC.keys():
      raise ValueError(F'''`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys()				)}, received {operation}'''				)
   snake_case_            =       STR_OPERATION_TO_FUNC[operation]
   if isinstance(lowerCAmelCase_		,    lowerCAmelCase_				):
      snake_case_            =       parse(importlib.metadata.version(lowerCAmelCase_				)				)
   return operation(lowerCAmelCase_		,    parse(lowerCAmelCase_				)				)



def       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:str				)->Any:
   '''simple docstring'''






   return compare_versions(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)




