

'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  torch.FloatTensor






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__      ):




  """simple docstring"""




  @register_to_config
  def __init__(      self   :     List[str]					,lowercase__   :     int = 6_5_5_3_6					,lowercase__   :     Optional[int] = None					,lowercase__   :     int = 2					,lowercase__   :     int = 2					,lowercase__   :     int = 0					,lowercase__   :     str = "fourier"					,lowercase__   :     bool = True					,lowercase__   :     bool = False					,lowercase__   :     float = 0.0					,lowercase__   :     Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,lowercase__   :     Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,lowercase__   :     Tuple[str] = "UNetMidBlock1D"					,lowercase__   :     str = None					,lowercase__   :     Tuple[int] = (3_2, 3_2, 6_4)					,lowercase__   :     str = None					,lowercase__   :     int = 8					,lowercase__   :     int = 1					,lowercase__   :     bool = False					,):
   super().__init__()
   __lowercase							  = sample_size

   # time
   if time_embedding_type == "fourier":
    __lowercase							  = GaussianFourierProjection(
        embedding_size=8					,set_W_to_weight=lowercase__					,log=lowercase__					,flip_sin_to_cos=lowercase__			)
    __lowercase							  = 2 * block_out_channels[0]
   elif time_embedding_type == "positional":
    __lowercase							  = Timesteps(
        block_out_channels[0]					,flip_sin_to_cos=lowercase__					,downscale_freq_shift=lowercase__			)
    __lowercase							  = block_out_channels[0]

   if use_timestep_embedding:
    __lowercase							  = block_out_channels[0] * 4
    __lowercase							  = TimestepEmbedding(
        in_channels=lowercase__					,time_embed_dim=lowercase__					,act_fn=lowercase__					,out_dim=block_out_channels[0]					,)

   __lowercase							  = nn.ModuleList([]			)
   __lowercase							  = None
   __lowercase							  = nn.ModuleList([]			)
   __lowercase							  = None

   # down
   __lowercase							  = in_channels
   for i, down_block_type in enumerate(lowercase__			):
    __lowercase							  = output_channel
    __lowercase							  = block_out_channels[i]

    if i == 0:
     input_channel += extra_in_channels

    __lowercase							  = i == len(lowercase__			) - 1

    __lowercase							  = get_down_block(
        lowercase__					,num_layers=lowercase__					,in_channels=lowercase__					,out_channels=lowercase__					,temb_channels=block_out_channels[0]					,add_downsample=not is_final_block or downsample_each_block					,)
    self.down_blocks.append(lowercase__			)

   # mid
   __lowercase							  = get_mid_block(
       lowercase__					,in_channels=block_out_channels[-1]					,mid_channels=block_out_channels[-1]					,out_channels=block_out_channels[-1]					,embed_dim=block_out_channels[0]					,num_layers=lowercase__					,add_downsample=lowercase__					,)

   # up
   __lowercase							  = list(reversed(lowercase__			)			)
   __lowercase							  = reversed_block_out_channels[0]
   if out_block_type is None:
    __lowercase							  = out_channels
   else:
    __lowercase							  = block_out_channels[0]

   for i, up_block_type in enumerate(lowercase__			):
    __lowercase							  = output_channel
    __lowercase							  = (
        reversed_block_out_channels[i + 1] if i < len(lowercase__			) - 1 else final_upsample_channels
    )

    __lowercase							  = i == len(lowercase__			) - 1

    __lowercase							  = get_up_block(
        lowercase__					,num_layers=lowercase__					,in_channels=lowercase__					,out_channels=lowercase__					,temb_channels=block_out_channels[0]					,add_upsample=not is_final_block					,)
    self.up_blocks.append(lowercase__			)
    __lowercase							  = output_channel

   # out
   __lowercase							  = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,3_2			)
   __lowercase							  = get_out_block(
       out_block_type=lowercase__					,num_groups_out=lowercase__					,embed_dim=block_out_channels[0]					,out_channels=lowercase__					,act_fn=lowercase__					,fc_dim=block_out_channels[-1] // 4					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     torch.FloatTensor					,lowercase__   :     Union[torch.Tensor, float, int]					,lowercase__   :     bool = True					,):
   __lowercase							  = timestep
   if not torch.is_tensor(lowercase__			):
    __lowercase							  = torch.tensor([timesteps]					,dtype=torch.long					,device=sample.device			)
   elif torch.is_tensor(lowercase__			) and len(timesteps.shape			) == 0:
    __lowercase							  = timesteps[None].to(sample.device			)

   __lowercase							  = self.time_proj(lowercase__			)
   if self.config.use_timestep_embedding:
    __lowercase							  = self.time_mlp(lowercase__			)
   else:
    __lowercase							  = timestep_embed[..., None]
    __lowercase							  = timestep_embed.repeat([1, 1, sample.shape[2]]			).to(sample.dtype			)
    __lowercase							  = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])			)

   # 2. down
   __lowercase							  = ()
   for downsample_block in self.down_blocks:
    __lowercase  ,	__lowercase							  = downsample_block(hidden_states=lowercase__					,temb=lowercase__			)
    down_block_res_samples += res_samples

   # 3. mid
   if self.mid_block:
    __lowercase							  = self.mid_block(lowercase__					,lowercase__			)

   # 4. up
   for i, upsample_block in enumerate(self.up_blocks			):
    __lowercase							  = down_block_res_samples[-1:]
    __lowercase							  = down_block_res_samples[:-1]
    __lowercase							  = upsample_block(lowercase__					,res_hidden_states_tuple=lowercase__					,temb=lowercase__			)

   # 5. post-process
   if self.out_block:
    __lowercase							  = self.out_block(lowercase__					,lowercase__			)

   if not return_dict:
    return (sample,)

   return UNetaDOutput(sample=lowercase__			)





def 				__lowerCamelCase						( UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      if len(UpperCAmelCase_						) != len(UpperCAmelCase_						):
            raise ValueError('''The length of profit and weight must be same.'''						)
      if max_weight <= 0:
            raise ValueError('''max_weight must greater than zero.'''						)
      if any(p < 0 for p in profit						):
            raise ValueError('''Profit can not be negative.'''						)
      if any(w < 0 for w in weight						):
            raise ValueError('''Weight can not be negative.'''						)

      # List created to store profit gained for the 1kg in case of each weight
      # respectively.  Calculate and append profit/weight for each element.
      a						:Optional[int] 							=       [p / w for p, w in zip(UpperCAmelCase_ ,  UpperCAmelCase_						)]

      # Creating a copy of the list and sorting profit/weight in ascending order
      a						:List[Any] 							=       sorted(UpperCAmelCase_						)

      # declaring useful variables
      a						:Dict 							=       len(UpperCAmelCase_						)
      a						:Tuple 							=       0
      a						:List[Any] 							=       0
      a						:str 							=       0

      # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
      while limit <= max_weight and i < length:
            # flag value for encountered greatest element in sorted_profit_by_weight
            a						:List[Any] 							=       sorted_profit_by_weight[length - i - 1]
            a						:Optional[Any] 							=       profit_by_weight.index(UpperCAmelCase_						)
            a						:Optional[int] 							=       -1

            # check if the weight encountered is less than the total weight
            # encountered before.
            if max_weight - limit >= weight[index]:
                  limit += weight[index]
                  # Adding profit gained for the given weight 1 ===
                  # weight[index]/weight[index]
                  gain += 1 * profit[index]
            else:
                  # Since the weight encountered is greater than limit, therefore take the
                  # required number of remaining kgs and calculate profit for it.
                  # weight remaining / weight[index]
                  gain += (max_weight - limit) / weight[index] * profit[index]
                  break
            i += 1
      return gain


if __name__ == "__main__":
     print(
         '''Input profits, weights, and then max_weight (all positive ints) separated by '''
         '''spaces.'''
     )

     snake_case				:    Union[str, Any]       				=     [int(x) for x in input('''Input profits separated by spaces: ''').split()]
     snake_case				:    Tuple       				=     [int(x) for x in input('''Input weights separated by spaces: ''').split()]
     snake_case				:    str       				=     int(input('''Max weight allowed: '''))

     # Function Call
     calc_profit(profit, weight, max_weight)





