
"""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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				torch.FloatTensor

class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_				):




      @register_to_config
      def __init__(				self ,							lowercase_ = 6_5_5_3_6 ,							lowercase_ = None ,							lowercase_ = 2 ,							lowercase_ = 2 ,							lowercase_ = 0 ,							lowercase_ = "fourier" ,							lowercase_ = True ,							lowercase_ = False ,							lowercase_ = 0.0 ,							lowercase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,							lowercase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,							lowercase_ = "UNetMidBlock1D" ,							lowercase_ = None ,							lowercase_ = (3_2, 3_2, 6_4) ,							lowercase_ = None ,							lowercase_ = 8 ,							lowercase_ = 1 ,							lowercase_ = False ,							)    ->       List[str]:
          super().__init__()
          UpperCAmelCase          =       sample_size

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

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

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

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

              if i == 0:
                  input_channel += extra_in_channels

              UpperCAmelCase          =       i == len(lowercase_				) - 1

              UpperCAmelCase          =       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
          UpperCAmelCase          =       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
          UpperCAmelCase          =       list(reversed(lowercase_				)				)
          UpperCAmelCase          =       reversed_block_out_channels[0]
          if out_block_type is None:
              UpperCAmelCase          =       out_channels
          else:
              UpperCAmelCase          =       block_out_channels[0]

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

              UpperCAmelCase          =       i == len(lowercase_				) - 1

              UpperCAmelCase          =       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_				)
              UpperCAmelCase          =       output_channel

          # out
          UpperCAmelCase          =       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,							3_2				)
          UpperCAmelCase          =       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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ = True ,							)    ->       Union[UNetaDOutput, Tuple]:
          UpperCAmelCase          =       timestep
          if not torch.is_tensor(lowercase_				):
              UpperCAmelCase          =       torch.tensor([timesteps] ,							dtype=torch.long ,							device=sample.device				)
          elif torch.is_tensor(lowercase_				) and len(timesteps.shape				) == 0:
              UpperCAmelCase          =       timesteps[None].to(sample.device				)

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

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

          # 3. mid
          if self.mid_block:
              UpperCAmelCase          =       self.mid_block(lowercase_ ,							lowercase_				)

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

          # 5. post-process
          if self.out_block:
              UpperCAmelCase          =       self.out_block(lowercase_ ,							lowercase_				)

          if not return_dict:
              return (sample,)

          return UNetaDOutput(sample=lowercase_				)





"""simple docstring"""

from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       Counter(lowerCAmelCase			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



