code: 
  
  
  
  from collections import namedtuple
  
  __UpperCAmelCase      = namedtuple('''from_to''', '''from_ to''')
  
  __UpperCAmelCase      = {
      '''millimeter''': '''mm''',
      '''centimeter''': '''cm''',
      '''meter''': '''m''',
      '''kilometer''': '''km''',
      '''inch''': '''in''',
      '''inche''': '''in''',  # Trailing 's' has been stripped off
      '''feet''': '''ft''',
      '''foot''': '''ft''',
      '''yard''': '''yd''',
      '''mile''': '''mi''',
  }
  
  __UpperCAmelCase      = {
      '''mm''': from_to(0.0_01, 1_000),
      '''cm''': from_to(0.01, 100),
      '''m''': from_to(1, 1),
      '''km''': from_to(1_000, 0.0_01),
      '''in''': from_to(0.02_54, 39.3_701),
      '''ft''': from_to(0.30_48, 3.2_80_84),
      '''yd''': from_to(0.91_44, 1.0_93_61),
      '''mi''': from_to(1_609.34, 0.0_00_62_13_71),
  }
  def 	UpperCamelCase			(     snake_case__ :					Tuple ,  snake_case__ :					List[str] ,  snake_case__ :					Any			)			->  float:
     UpperCamelCase					:						Tuple					=			from_type.lower().rstrip('s'			)
     UpperCamelCase					:						Tuple					=			TYPE_CONVERSION.get(lowercase_ ,  lowercase_			)
     UpperCamelCase					:						Union[str, Any]					=			to_type.lower().rstrip('s'			)
     UpperCamelCase					:						Tuple					=			TYPE_CONVERSION.get(lowercase_ ,  lowercase_			)
     if new_from not in METRIC_CONVERSION:
        UpperCamelCase					:						int					=			(
            F"""Invalid \'from_type\' value: {from_type!r}.\n"""
            F"""Conversion abbreviations are: {", ".join(lowercase_			)}"""
        )
        raise ValueError(lowercase_			)
     if new_to not in METRIC_CONVERSION:
        UpperCamelCase					:						str					=			(
            F"""Invalid \'to_type\' value: {to_type!r}.\n"""
            F"""Conversion abbreviations are: {", ".join(lowercase_			)}"""
        )
        raise ValueError(lowercase_			)
     return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
  
  
  
  
style_context: 
  
  
  
  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        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      torch.FloatTensor
  
  
  
  
  
  
  class        lowerCAmelCase_	(						a__ ,					a__							):
         @register_to_config
         def __init__(    self,    SCREAMING_SNAKE_CASE_ = 16,    SCREAMING_SNAKE_CASE_ = 88,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = 1,    SCREAMING_SNAKE_CASE_ = 0.0,    SCREAMING_SNAKE_CASE_ = 32,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = False,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = "geglu",    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = False,    SCREAMING_SNAKE_CASE_ = False,    SCREAMING_SNAKE_CASE_ = False,    SCREAMING_SNAKE_CASE_ = "layer_norm",    SCREAMING_SNAKE_CASE_ = True,    )  ->	Union[str, Any]:
            super().__init__()
            UpperCamelCase					:						Tuple					=			use_linear_projection
            UpperCamelCase					:						int					=			num_attention_heads
            UpperCamelCase					:						Dict					=			attention_head_dim
            UpperCamelCase					:						Optional[int]					=			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
            UpperCamelCase					:						Optional[Any]					=			(in_channels is not None) and (patch_size is None)
            UpperCamelCase					:						Tuple					=			num_vector_embeds is not None
            UpperCamelCase					:						Optional[int]					=			in_channels is not None and patch_size is not None
  
            if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
               UpperCamelCase					:						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',    SCREAMING_SNAKE_CASE_,    standard_warn=SCREAMING_SNAKE_CASE_ )
               UpperCamelCase					:						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:
               UpperCamelCase					:						Optional[int]					=			in_channels
  
               UpperCamelCase					:						str					=			torch.nn.GroupNorm(num_groups=SCREAMING_SNAKE_CASE_,    num_channels=SCREAMING_SNAKE_CASE_,    eps=1e-6,    affine=SCREAMING_SNAKE_CASE_ )
               if use_linear_projection:
                  UpperCamelCase					:						Dict					=			nn.Linear(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
               else:
                  UpperCamelCase					:						Any					=			nn.Convad(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    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"
  
               UpperCamelCase					:						Union[str, Any]					=			sample_size
               UpperCamelCase					:						int					=			sample_size
               UpperCamelCase					:						Optional[Any]					=			num_vector_embeds
               UpperCamelCase					:						str					=			self.height * self.width
  
               UpperCamelCase					:						Union[str, Any]					=			ImagePositionalEmbeddings(
                   num_embed=SCREAMING_SNAKE_CASE_,    embed_dim=SCREAMING_SNAKE_CASE_,    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"
  
               UpperCamelCase					:						int					=			sample_size
               UpperCamelCase					:						Optional[Any]					=			sample_size
  
               UpperCamelCase					:						List[str]					=			patch_size
               UpperCamelCase					:						Tuple					=			PatchEmbed(
                   height=SCREAMING_SNAKE_CASE_,    width=SCREAMING_SNAKE_CASE_,    patch_size=SCREAMING_SNAKE_CASE_,    in_channels=SCREAMING_SNAKE_CASE_,    embed_dim=SCREAMING_SNAKE_CASE_,    )
  
            # 3. Define transformers blocks
            UpperCamelCase					:						Any					=			nn.ModuleList(
                [
                    BasicTransformerBlock(
                        SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    dropout=SCREAMING_SNAKE_CASE_,    cross_attention_dim=SCREAMING_SNAKE_CASE_,    activation_fn=SCREAMING_SNAKE_CASE_,    num_embeds_ada_norm=SCREAMING_SNAKE_CASE_,    attention_bias=SCREAMING_SNAKE_CASE_,    only_cross_attention=SCREAMING_SNAKE_CASE_,    upcast_attention=SCREAMING_SNAKE_CASE_,    norm_type=SCREAMING_SNAKE_CASE_,    norm_elementwise_affine=SCREAMING_SNAKE_CASE_,    )
                    for d in range(SCREAMING_SNAKE_CASE_ )
                ] )
  
            # 4. Define output layers
            UpperCamelCase					:						Optional[int]					=			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:
                  UpperCamelCase					:						Optional[Any]					=			nn.Linear(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
               else:
                  UpperCamelCase					:						List[str]					=			nn.Convad(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    kernel_size=1,    stride=1,    padding=0 )
            elif self.is_input_vectorized:
               UpperCamelCase					:						Optional[Any]					=			nn.LayerNorm(SCREAMING_SNAKE_CASE_ )
               UpperCamelCase					:						Any					=			nn.Linear(SCREAMING_SNAKE_CASE_,    self.num_vector_embeds - 1 )
            elif self.is_input_patches:
               UpperCamelCase					:						List[str]					=			nn.LayerNorm(SCREAMING_SNAKE_CASE_,    elementwise_affine=SCREAMING_SNAKE_CASE_,    eps=1e-6 )
               UpperCamelCase					:						Tuple					=			nn.Linear(SCREAMING_SNAKE_CASE_,    2 * inner_dim )
               UpperCamelCase					:						Any					=			nn.Linear(SCREAMING_SNAKE_CASE_,    patch_size * patch_size * self.out_channels )
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = True,    )  ->	Optional[Any]:
            # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
            #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
            #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
            # expects mask of shape:
            #   [batch, key_tokens]
            # adds singleton query_tokens dimension:
            #   [batch,                    1, key_tokens]
            # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
            #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
            #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
            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)
               UpperCamelCase					:						List[str]					=			(1 - attention_mask.to(hidden_states.dtype )) * -1_00_00.0
               UpperCamelCase					:						Tuple					=			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:
               UpperCamelCase					:						int					=			(1 - encoder_attention_mask.to(hidden_states.dtype )) * -1_00_00.0
               UpperCamelCase					:						Dict					=			encoder_attention_mask.unsqueeze(1 )
  
            # 1. Input
            if self.is_input_continuous:
               UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			hidden_states.shape
               UpperCamelCase					:						str					=			hidden_states
  
               UpperCamelCase					:						Union[str, Any]					=			self.norm(SCREAMING_SNAKE_CASE_ )
               if not self.use_linear_projection:
                  UpperCamelCase					:						Tuple					=			self.proj_in(SCREAMING_SNAKE_CASE_ )
                  UpperCamelCase					:						Optional[int]					=			hidden_states.shape[1]
                  UpperCamelCase					:						Tuple					=			hidden_states.permute(0,    2,    3,    1 ).reshape(SCREAMING_SNAKE_CASE_,    height * width,    SCREAMING_SNAKE_CASE_ )
               else:
                  UpperCamelCase					:						Optional[int]					=			hidden_states.shape[1]
                  UpperCamelCase					:						Dict					=			hidden_states.permute(0,    2,    3,    1 ).reshape(SCREAMING_SNAKE_CASE_,    height * width,    SCREAMING_SNAKE_CASE_ )
                  UpperCamelCase					:						Union[str, Any]					=			self.proj_in(SCREAMING_SNAKE_CASE_ )
            elif self.is_input_vectorized:
               UpperCamelCase					:						Union[str, Any]					=			self.latent_image_embedding(SCREAMING_SNAKE_CASE_ )
            elif self.is_input_patches:
               UpperCamelCase					:						Any					=			self.pos_embed(SCREAMING_SNAKE_CASE_ )
  
            # 2. Blocks
            for block in self.transformer_blocks:
               UpperCamelCase					:						Optional[int]					=			block(
                   SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    encoder_hidden_states=SCREAMING_SNAKE_CASE_,    encoder_attention_mask=SCREAMING_SNAKE_CASE_,    timestep=SCREAMING_SNAKE_CASE_,    cross_attention_kwargs=SCREAMING_SNAKE_CASE_,    class_labels=SCREAMING_SNAKE_CASE_,    )
  
            # 3. Output
            if self.is_input_continuous:
               if not self.use_linear_projection:
                  UpperCamelCase					:						Tuple					=			hidden_states.reshape(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ).permute(0,    3,    1,    2 ).contiguous()
                  UpperCamelCase					:						int					=			self.proj_out(SCREAMING_SNAKE_CASE_ )
               else:
                  UpperCamelCase					:						Optional[int]					=			self.proj_out(SCREAMING_SNAKE_CASE_ )
                  UpperCamelCase					:						Optional[int]					=			hidden_states.reshape(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ).permute(0,    3,    1,    2 ).contiguous()
  
               UpperCamelCase					:						Any					=			hidden_states + residual
            elif self.is_input_vectorized:
               UpperCamelCase					:						List[Any]					=			self.norm_out(SCREAMING_SNAKE_CASE_ )
               UpperCamelCase					:						Optional[int]					=			self.out(SCREAMING_SNAKE_CASE_ )
               # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
               UpperCamelCase					:						Tuple					=			logits.permute(0,    2,    1 )
  
               # log(p(x_0))
               UpperCamelCase					:						List[str]					=			F.log_softmax(logits.double(),    dim=1 ).float()
            elif self.is_input_patches:
               # TODO: cleanup!
               UpperCamelCase					:						int					=			self.transformer_blocks[0].norma.emb(
                   SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    hidden_dtype=hidden_states.dtype )
               UpperCamelCase			,  UpperCamelCase					:						int					=			self.proj_out_a(F.silu(SCREAMING_SNAKE_CASE_ ) ).chunk(2,    dim=1 )
               UpperCamelCase					:						Dict					=			self.norm_out(SCREAMING_SNAKE_CASE_ ) * (1 + scale[:, None]) + shift[:, None]
               UpperCamelCase					:						Any					=			self.proj_out_a(SCREAMING_SNAKE_CASE_ )
  
               # unpatchify
               UpperCamelCase					:						Union[str, Any]					=			int(hidden_states.shape[1] ** 0.5 )
               UpperCamelCase					:						Tuple					=			hidden_states.reshape(
                   shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) )
               UpperCamelCase					:						int					=			torch.einsum('nhwpqc->nchpwq',    SCREAMING_SNAKE_CASE_ )
               UpperCamelCase					:						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=SCREAMING_SNAKE_CASE_ )
  
  
  
  