



import flax.linen as nn
import jax
import jax.numpy as jnp






class SCREAMING_SNAKE_CASE__	(						nn.Module       ):




    '''simple docstring'''






    __lowerCamelCase     :					int
    __lowerCamelCase     :					jnp.dtype   = jnp.floataa






    def      _lowerCAmelCase   (			self						):
        A					:			List[Any]		=  nn.Conv(
            self.out_channels,						kernel_size=(3, 3),						strides=(1, 1),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)






    def __call__(			self,						lowerCamelCase__						):
        A					:			Union[str, Any]		=  hidden_states.shape
        A					:			int		=  jax.image.resize(
            lowerCamelCase__,						shape=(batch, height * 2, width * 2, channels),						method="""nearest""",						)
        A					:			Any		=  self.conv(lowerCamelCase__						)
        return hidden_states






class SCREAMING_SNAKE_CASE__	(						nn.Module       ):




    '''simple docstring'''






    __lowerCamelCase     :					int
    __lowerCamelCase     :					jnp.dtype   = jnp.floataa






    def      _lowerCAmelCase   (			self						):
        A					:			str		=  nn.Conv(
            self.out_channels,						kernel_size=(3, 3),						strides=(2, 2),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)






    def __call__(			self,						lowerCamelCase__						):
        A					:			Optional[int]		=  self.conv(lowerCamelCase__						)
        return hidden_states






class SCREAMING_SNAKE_CASE__	(						nn.Module       ):




    '''simple docstring'''






    __lowerCamelCase     :					int
    __lowerCamelCase     :					int   = None
    __lowerCamelCase     :					float   = 0.0
    __lowerCamelCase     :					bool   = None
    __lowerCamelCase     :					jnp.dtype   = jnp.floataa






    def      _lowerCAmelCase   (			self						):
        A					:			Any		=  self.in_channels if self.out_channels is None else self.out_channels

        A					:			List[Any]		=  nn.GroupNorm(num_groups=32,						epsilon=1e-5						)
        A					:			Any		=  nn.Conv(
            lowerCamelCase__,						kernel_size=(3, 3),						strides=(1, 1),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)

        A					:			Optional[Any]		=  nn.Dense(lowerCamelCase__,						dtype=self.dtype						)

        A					:			List[str]		=  nn.GroupNorm(num_groups=32,						epsilon=1e-5						)
        A					:			Optional[Any]		=  nn.Dropout(self.dropout_prob						)
        A					:			Optional[Any]		=  nn.Conv(
            lowerCamelCase__,						kernel_size=(3, 3),						strides=(1, 1),						padding=((1, 1), (1, 1)),						dtype=self.dtype,						)

        A					:			Dict		=  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        A					:			Optional[int]		=  None
        if use_nin_shortcut:
            A					:			List[str]		=  nn.Conv(
                lowerCamelCase__,						kernel_size=(1, 1),						strides=(1, 1),						padding="""VALID""",						dtype=self.dtype,						)






    def __call__(			self,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__=True						):
        A					:			List[str]		=  hidden_states
        A					:			List[Any]		=  self.norma(lowerCamelCase__						)
        A					:			Dict		=  nn.swish(lowerCamelCase__						)
        A					:			Optional[Any]		=  self.conva(lowerCamelCase__						)

        A					:			str		=  self.time_emb_proj(nn.swish(lowerCamelCase__						)						)
        A					:			List[str]		=  jnp.expand_dims(jnp.expand_dims(lowerCamelCase__,						1						),						1						)
        A					:			int		=  hidden_states + temb

        A					:			Optional[int]		=  self.norma(lowerCamelCase__						)
        A					:			Optional[Any]		=  nn.swish(lowerCamelCase__						)
        A					:			str		=  self.dropout(lowerCamelCase__,						lowerCamelCase__						)
        A					:			Union[str, Any]		=  self.conva(lowerCamelCase__						)

        if self.conv_shortcut is not None:
            A					:			str		=  self.conv_shortcut(lowerCamelCase__						)

        return hidden_states + residual







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