code: 
  
  
  
  
  
  from dataclasses import dataclass
  from typing import Any, Dict, Optional
  
  import torch
  import torch.nn.functional as F
  from torch import nn
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..models.embeddings import ImagePositionalEmbeddings
  from ..utils import BaseOutput, deprecate
  from .attention import BasicTransformerBlock
  from .embeddings import PatchEmbed
  from .modeling_utils import ModelMixin
  
  @dataclass
  class 				A    (__UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							42
  
  
  class 				A    (__UpperCAmelCase       ,__UpperCAmelCase				):
  
  
  
        @register_to_config
        def __init__(					self     ,  lowercase_ = 16     ,  lowercase_ = 88     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = 1     ,  lowercase_ = 0.0     ,  lowercase_ = 32     ,  lowercase_ = None     ,  lowercase_ = False     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = "geglu"     ,  lowercase_ = None     ,  lowercase_ = False     ,  lowercase_ = False     ,  lowercase_ = False     ,  lowercase_ = "layer_norm"     ,  lowercase_ = True     ,  )      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               super().__init__()
               _snake_case      :							int     				=						use_linear_projection
               _snake_case      :							Optional[Any]     				=						num_attention_heads
               _snake_case      :							Optional[int]     				=						attention_head_dim
               _snake_case      :							Any     				=						num_attention_heads * attention_head_dim
  
               # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
               # Define whether input is continuous or discrete depending on configuration
               _snake_case      :							List[str]     				=						(in_channels is not None) and (patch_size is None)
               _snake_case      :							Optional[int]     				=						num_vector_embeds is not None
               _snake_case      :							Union[str, Any]     				=						in_channels is not None and patch_size is not None
  
               if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
                      _snake_case      :							List[Any]     				=						(
                          F'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
                          ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
                          ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
                          ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
                          ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
                      )
                      deprecate('''norm_type!=num_embeds_ada_norm'''     ,  '''1.0.0'''     ,  lowercase_     ,  standard_warn=lowercase_					)
                      _snake_case      :							List[Any]     				=						'''ada_norm'''
  
               if self.is_input_continuous and self.is_input_vectorized:
                      raise ValueError(
                          F'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
                          ''' sure that either `in_channels` or `num_vector_embeds` is None.'''					)
               elif self.is_input_vectorized and self.is_input_patches:
                      raise ValueError(
                          F'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
                          ''' sure that either `num_vector_embeds` or `num_patches` is None.'''					)
               elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
                      raise ValueError(
                          F'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
                          F''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'''					)
  
               # 2. Define input layers
               if self.is_input_continuous:
                      _snake_case      :							Any     				=						in_channels
  
                      _snake_case      :							int     				=						torch.nn.GroupNorm(num_groups=lowercase_     ,  num_channels=lowercase_     ,  eps=1E-6     ,  affine=lowercase_					)
                      if use_linear_projection:
                             _snake_case      :							Optional[int]     				=						nn.Linear(lowercase_     ,  lowercase_					)
                      else:
                             _snake_case      :							Optional[Any]     				=						nn.Convad(lowercase_     ,  lowercase_     ,  kernel_size=1     ,  stride=1     ,  padding=0					)
               elif self.is_input_vectorized:
                      assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
                      assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
  
                      _snake_case      :							Tuple     				=						sample_size
                      _snake_case      :							List[Any]     				=						sample_size
                      _snake_case      :							Tuple     				=						num_vector_embeds
                      _snake_case      :							Any     				=						self.height * self.width
  
                      _snake_case      :							Optional[int]     				=						ImagePositionalEmbeddings(
                          num_embed=lowercase_     ,  embed_dim=lowercase_     ,  height=self.height     ,  width=self.width					)
               elif self.is_input_patches:
                      assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
  
                      _snake_case      :							Tuple     				=						sample_size
                      _snake_case      :							Tuple     				=						sample_size
  
                      _snake_case      :							Optional[Any]     				=						patch_size
                      _snake_case      :							str     				=						PatchEmbed(
                          height=lowercase_     ,  width=lowercase_     ,  patch_size=lowercase_     ,  in_channels=lowercase_     ,  embed_dim=lowercase_     ,  )
  
               # 3. Define transformers blocks
               _snake_case      :							Optional[int]     				=						nn.ModuleList(
                   [
                       BasicTransformerBlock(
                           lowercase_     ,  lowercase_     ,  lowercase_     ,  dropout=lowercase_     ,  cross_attention_dim=lowercase_     ,  activation_fn=lowercase_     ,  num_embeds_ada_norm=lowercase_     ,  attention_bias=lowercase_     ,  only_cross_attention=lowercase_     ,  upcast_attention=lowercase_     ,  norm_type=lowercase_     ,  norm_elementwise_affine=lowercase_     ,  )
                       for d in range(lowercase_					)
                   ]					)
  
               # 4. Define output layers
               _snake_case      :							Any     				=						in_channels if out_channels is None else out_channels
               if self.is_input_continuous:
                      # TODO: should use out_channels for continuous projections
                      if use_linear_projection:
                             _snake_case      :							Optional[Any]     				=						nn.Linear(lowercase_     ,  lowercase_					)
                      else:
                             _snake_case      :							Optional[Any]     				=						nn.Convad(lowercase_     ,  lowercase_     ,  kernel_size=1     ,  stride=1     ,  padding=0					)
               elif self.is_input_vectorized:
                      _snake_case      :							Optional[Any]     				=						nn.LayerNorm(lowercase_					)
                      _snake_case      :							Dict     				=						nn.Linear(lowercase_     ,  self.num_vector_embeds - 1					)
               elif self.is_input_patches:
                      _snake_case      :							int     				=						nn.LayerNorm(lowercase_     ,  elementwise_affine=lowercase_     ,  eps=1E-6					)
                      _snake_case      :							Union[str, Any]     				=						nn.Linear(lowercase_     ,  2 * inner_dim					)
                      _snake_case      :							Union[str, Any]     				=						nn.Linear(lowercase_     ,  patch_size * patch_size * self.out_channels					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = True     ,  )      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               if attention_mask is not None and attention_mask.ndim == 2:
                      # assume that mask is expressed as:
                      #   (1 = keep,      0 = discard)
                      # convert mask into a bias that can be added to attention scores:
                      #       (keep = +0,     discard = -10000.0)
                      _snake_case      :							Union[str, Any]     				=						(1 - attention_mask.to(hidden_states.dtype					)) * -1_0000.0
                      _snake_case      :							List[Any]     				=						attention_mask.unsqueeze(1					)
  
               # convert encoder_attention_mask to a bias the same way we do for attention_mask
               if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                      _snake_case      :							Optional[Any]     				=						(1 - encoder_attention_mask.to(hidden_states.dtype					)) * -1_0000.0
                      _snake_case      :							str     				=						encoder_attention_mask.unsqueeze(1					)
  
               # 1. Input
               if self.is_input_continuous:
                      _snake_case				,    _snake_case				,    _snake_case				,    _snake_case      :							Any     				=						hidden_states.shape
                      _snake_case      :							Optional[int]     				=						hidden_states
  
                      _snake_case      :							str     				=						self.norm(lowercase_					)
                      if not self.use_linear_projection:
                             _snake_case      :							Optional[int]     				=						self.proj_in(lowercase_					)
                             _snake_case      :							Optional[int]     				=						hidden_states.shape[1]
                             _snake_case      :							Tuple     				=						hidden_states.permute(0     ,  2     ,  3     ,  1					).reshape(lowercase_     ,  height * width     ,  lowercase_					)
                      else:
                             _snake_case      :							List[str]     				=						hidden_states.shape[1]
                             _snake_case      :							Tuple     				=						hidden_states.permute(0     ,  2     ,  3     ,  1					).reshape(lowercase_     ,  height * width     ,  lowercase_					)
                             _snake_case      :							Optional[int]     				=						self.proj_in(lowercase_					)
               elif self.is_input_vectorized:
                      _snake_case      :							Optional[int]     				=						self.latent_image_embedding(lowercase_					)
               elif self.is_input_patches:
                      _snake_case      :							Dict     				=						self.pos_embed(lowercase_					)
  
               # 2. Blocks
               for block in self.transformer_blocks:
                      _snake_case      :							Dict     				=						block(
                          lowercase_     ,  attention_mask=lowercase_     ,  encoder_hidden_states=lowercase_     ,  encoder_attention_mask=lowercase_     ,  timestep=lowercase_     ,  cross_attention_kwargs=lowercase_     ,  class_labels=lowercase_     ,  )
  
               # 3. Output
               if self.is_input_continuous:
                      if not self.use_linear_projection:
                             _snake_case      :							int     				=						hidden_states.reshape(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					).permute(0     ,  3     ,  1     ,  2					).contiguous()
                             _snake_case      :							Union[str, Any]     				=						self.proj_out(lowercase_					)
                      else:
                             _snake_case      :							Any     				=						self.proj_out(lowercase_					)
                             _snake_case      :							List[str]     				=						hidden_states.reshape(lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					).permute(0     ,  3     ,  1     ,  2					).contiguous()
  
                      _snake_case      :							int     				=						hidden_states + residual
               elif self.is_input_vectorized:
                      _snake_case      :							Dict     				=						self.norm_out(lowercase_					)
                      _snake_case      :							List[Any]     				=						self.out(lowercase_					)
                      # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                      _snake_case      :							Tuple     				=						logits.permute(0     ,  2     ,  1					)
  
                      # log(p(x_0))
                      _snake_case      :							int     				=						F.log_softmax(logits.double()     ,  dim=1					).float()
               elif self.is_input_patches:
                      # TODO: cleanup!
                      _snake_case      :							Tuple     				=						self.transformer_blocks[0].norma.emb(
                          lowercase_     ,  lowercase_     ,  hidden_dtype=hidden_states.dtype					)
                      _snake_case				,    _snake_case      :							Union[str, Any]     				=						self.proj_out_a(F.silu(lowercase_					)					).chunk(2     ,  dim=1					)
                      _snake_case      :							List[str]     				=						self.norm_out(lowercase_					) * (1 + scale[:, None]) + shift[:, None]
                      _snake_case      :							Tuple     				=						self.proj_out_a(lowercase_					)
  
                      # unpatchify
                      _snake_case      :							Any     				=						int(hidden_states.shape[1] ** 0.5					)
                      _snake_case      :							Optional[int]     				=						hidden_states.reshape(
                          shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)					)
                      _snake_case      :							str     				=						torch.einsum('''nhwpqc->nchpwq'''     ,  lowercase_					)
                      _snake_case      :							Union[str, Any]     				=						hidden_states.reshape(
                          shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)					)
  
               if not return_dict:
                      return (output,)
  
               return TransformeraDModelOutput(sample=lowercase_					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def 						A_			(  lowercase_  )			->						list:
         if len(lowercase_  ) <= 1:
                return data
         else:
                return [
                    *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                    data[0],
                    *quick_sort([e for e in data[1:] if e > data[0]]  ),
                ]
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
  
  
  
  
  
  