



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,						lowerCamelCase__ = 16,						lowerCamelCase__ = 88,						lowerCamelCase__ = None,						lowerCamelCase__ = 1,						lowerCamelCase__ = 0.0,						lowerCamelCase__ = 32,						lowerCamelCase__ = None,						lowerCamelCase__ = False,						lowerCamelCase__ = None,						lowerCamelCase__ = None,						lowerCamelCase__ = "geglu",						lowerCamelCase__ = None,						):
        super().__init__()
        A					:			List[Any]		=  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
        A					:			int		=  0.5

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






    def      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__=None,						lowerCamelCase__=None,						lowerCamelCase__=None,						lowerCamelCase__ = True,						):
        A					:			Union[str, Any]		=  hidden_states

        A					:			List[str]		=  []
        A					:			Optional[int]		=  0
        # attention_mask is not used yet
        for i in range(2						):
            # for each of the two transformers, pass the corresponding condition tokens
            A					:			Dict		=  encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            A					:			Optional[int]		=  self.transformer_index_for_condition[i]
            A					:			List[str]		=  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]

        A					:			Tuple		=  encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        A					:			Union[str, Any]		=  output_states + input_states

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=lowerCamelCase__						)







def        __a			(   __lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for data in source_data:
										for i, el in enumerate(__lowerCAmelCase   ):
															if len(__lowerCAmelCase   ) < i + 1:
																				data_lists.append([]   )
															data_lists[i].append(float(__lowerCAmelCase   )   )
					return data_lists

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for dlist, weight in zip(__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     str						=				min(__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE							:     int						=				max(__lowerCAmelCase   )

										SCREAMING_SNAKE_CASE							:     list[float]						=				[]
										# for weight 0 score is 1 - actual score
										if weight == 0:
															for item in dlist:
																				try:
																									score.append(1 - ((item - mind) / (maxd - mind))   )
																				except ZeroDivisionError:
																									score.append(1   )

										elif weight == 1:
															for item in dlist:
																				try:
																									score.append((item - mind) / (maxd - mind)   )
																				except ZeroDivisionError:
																									score.append(0   )

        # weight not 0 or 1
										else:
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				F'''Invalid weight of {weight:f} provided'''
															raise ValueError(__lowerCAmelCase   )

										score_lists.append(__lowerCAmelCase   )

					return score_lists

def        __a			(   __lowerCAmelCase   )     ->     list[float]:
					SCREAMING_SNAKE_CASE							:     list[float]						=				[0 for i in range(len(score_lists[0]   )   )]

					for slist in score_lists:
										for j, ele in enumerate(__lowerCAmelCase   ):
															SCREAMING_SNAKE_CASE							:     str						=				final_scores[j] + ele

					return final_scores



def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				get_data(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				calculate_each_score(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				generate_final_scores(__lowerCAmelCase   )

					# append scores to source data
					for i, ele in enumerate(__lowerCAmelCase   ):
										source_data[i].append(__lowerCAmelCase   )

					return source_data