
"""simple docstring"""


from typing import List

from .keymap import KEYMAP, get_character






def 					_snake_case (						snake_case__      :   str   ):

	def decorator(snake_case__      :   Optional[Any]   ):
		A        =      getattr(__lowercase		,					'handle_key'		,					[]   )
		handle += [key]
		setattr(__lowercase		,					'handle_key'		,					__lowercase   )
		return func

	return decorator






def 					_snake_case (						*snake_case__      :   List[str]   ):

	def decorator(snake_case__      :   Union[str, Any]   ):
		A        =      getattr(__lowercase		,					'handle_key'		,					[]   )
		handle += keys
		setattr(__lowercase		,					'handle_key'		,					__lowercase   )
		return func

	return decorator







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						def __new__(       cls   :					Union[str, Any]     ,A_   :					str     ,A_   :					List[str]     ,A_   :					Any					)   ->      List[Any]:
							A        =      super().__new__(cls     ,A_     ,A_     ,A_					)
							if not hasattr(A_     ,'key_handler'					):
								setattr(A_     ,'key_handler'     ,{}					)
							setattr(A_     ,'handle_input'     ,KeyHandler.handle_input					)

							for value in attrs.values():
								A        =      getattr(A_     ,'handle_key'     ,[]					)
								for key in handled_keys:
									A        =      value
							return new_cls




						@staticmethod
						def   _SCREAMING_SNAKE_CASE					(       cls   :					List[Any]					)   ->      List[Any]:
							A        =      get_character()
							if char != KEYMAP["undefined"]:
								A        =      ord(A_					)
							A        =      cls.key_handler.get(A_					)
							if handler:
								A        =      char
								return handler(cls					)
							else:
								return None






def 					_snake_case (						cls      :   str   ):
	return KeyHandler(cls.__name__		,					cls.__bases__		,					cls.__dict__.copy()   )

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  lowercase	:    int = 16			,  lowercase	:    int = 88			,  lowercase	:    Optional[int] = None			,  lowercase	:    int = 1			,  lowercase	:    float = 0.0			,  lowercase	:    int = 32			,  lowercase	:    Optional[int] = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    str = "geglu"			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowercase			,  attention_head_dim=lowercase			,  in_channels=lowercase			,  num_layers=lowercase			,  dropout=lowercase			,  norm_num_groups=lowercase			,  cross_attention_dim=lowercase			,  attention_bias=lowercase			,  sample_size=lowercase			,  num_vector_embeds=lowercase			,  activation_fn=lowercase			,  num_embeds_ada_norm=lowercase			,  )
				        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
				_snake_case		=			0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				_snake_case		=			[77, 257]

				# 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])`
				_snake_case		=			[1, 0]





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


				'''simple docstring'''


				_snake_case		=			hidden_states

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

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

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowercase	)