
"""simple docstring"""







from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				A__ (			nn.Module):

				def __init__(   self ,	_SCREAMING_SNAKE_CASE = 16 ,	_SCREAMING_SNAKE_CASE = 88 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 1 ,	_SCREAMING_SNAKE_CASE = 0.0 ,	_SCREAMING_SNAKE_CASE = 32 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = "geglu" ,	_SCREAMING_SNAKE_CASE = None ,	):
					super().__init__()
					__lowerCAmelCase    :		Tuple		    =   nn.ModuleList(
					    [
					        TransformeraDModel(
					            num_attention_heads=UpperCAmelCase__ ,	attention_head_dim=UpperCAmelCase__ ,	in_channels=UpperCAmelCase__ ,	num_layers=UpperCAmelCase__ ,	dropout=UpperCAmelCase__ ,	norm_num_groups=UpperCAmelCase__ ,	cross_attention_dim=UpperCAmelCase__ ,	attention_bias=UpperCAmelCase__ ,	sample_size=UpperCAmelCase__ ,	num_vector_embeds=UpperCAmelCase__ ,	activation_fn=UpperCAmelCase__ ,	num_embeds_ada_norm=UpperCAmelCase__ ,	)
					        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    :		Optional[Any]		    =   [77, 2_57]

					# 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    :		int		    =   [1, 0]







				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE = True ,	):
					__lowerCAmelCase    :		Tuple		    =   hidden_states

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

					__lowerCAmelCase    :		Optional[int]		    =   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
					__lowerCAmelCase    :		Optional[int]		    =   output_states + input_states

					if not return_dict:
						return (output_states,)

					return TransformeraDModelOutput(sample=UpperCAmelCase__				)

"""simple docstring"""







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Tuple		    =   HfArgumentParser(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   parser.parse_args_into_dataclasses()[0]
	__lowerCAmelCase    :		int		    =   TensorFlowBenchmark(args=_UpperCamelCase	)
	try:
		__lowerCAmelCase    :		Dict		    =   parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		__lowerCAmelCase    :		List[Any]		    =   'Arg --no_{0} is no longer used, please use --no-{0} instead.'
		__lowerCAmelCase    :		Union[str, Any]		    =   ' '.join(str(_UpperCamelCase	).split(' '	)[:-1]	)
		__lowerCAmelCase    :		List[str]		    =   ''
		__lowerCAmelCase    :		Any		    =   eval(str(_UpperCamelCase	).split(' '	)[-1]	)
		__lowerCAmelCase    :		Dict		    =   []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(_UpperCamelCase	)
		if len(_UpperCamelCase	) > 0:
			__lowerCAmelCase    :		Any		    =   full_error_msg + begin_error_msg + str(_UpperCamelCase	)
		raise ValueError(_UpperCamelCase	)
	benchmark.run()


if __name__ == "__main__":
							main()