code: 
  
  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 UNetMidBlockaD, get_down_block, get_up_block
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: torch.FloatTensor
  
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case	, __snake_case  ):
  
  
  
  
  
      @register_to_config
      def __init__(   self		,     a = None		,     a = 3		,     a = 3		,     a = False		,     a = "positional"		,     a = 0		,     a = True		,     a = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,     a = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,     a = (224, 448, 672, 896)		,     a = 2		,     a = 1		,     a = 1		,     a = "conv"		,     a = "conv"		,     a = "silu"		,     a = 8		,     a = 32		,     a = 1e-5		,     a = "default"		,     a = True		,     a = None		,     a = None		,     ):
           super().__init__()
  
           lowercase__					:     List[Any]   = sample_size
           lowercase__					:     List[str]   = block_out_channels[0] * 4
  
           # Check inputs
           if len(a) != len(a):
                raise ValueError(
                    f"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.""")
  
           if len(a) != len(a):
                raise ValueError(
                    f"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.""")
  
           # input
           lowercase__					:     Dict   = nn.Convad(a		,     block_out_channels[0]		,     kernel_size=3		,     padding=(1, 1))
  
           # time
           if time_embedding_type == "fourier":
                lowercase__					:     str   = GaussianFourierProjection(embedding_size=block_out_channels[0]		,     scale=16)
                lowercase__					:     List[Any]   = 2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                lowercase__					:     List[Any]   = Timesteps(block_out_channels[0]		,     a		,     a)
                lowercase__					:     Optional[int]   = block_out_channels[0]
  
           lowercase__					:     Tuple   = TimestepEmbedding(a		,     a)
  
           # class embedding
           if class_embed_type is None and num_class_embeds is not None:
                lowercase__					:     int   = nn.Embedding(a		,     a)
           elif class_embed_type == "timestep":
                lowercase__					:     Optional[Any]   = TimestepEmbedding(a		,     a)
           elif class_embed_type == "identity":
                lowercase__					:     str   = nn.Identity(a		,     a)
           else:
                lowercase__					:     Tuple   = None
  
           lowercase__					:     List[str]   = nn.ModuleList([])
           lowercase__					:     List[Any]   = None
           lowercase__					:     Dict   = nn.ModuleList([])
  
           # down
           lowercase__					:     Optional[int]   = block_out_channels[0]
           for i, down_block_type in enumerate(a):
                lowercase__					:     Dict   = output_channel
                lowercase__					:     Optional[Any]   = block_out_channels[i]
                lowercase__					:     str   = i == len(a) - 1
  
                lowercase__					:     Any   = get_down_block(
                    a		,     num_layers=a		,     in_channels=a		,     out_channels=a		,     temb_channels=a		,     add_downsample=not is_final_block		,     resnet_eps=a		,     resnet_act_fn=a		,     resnet_groups=a		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,     downsample_padding=a		,     resnet_time_scale_shift=a		,     downsample_type=a		,     )
                self.down_blocks.append(a)
  
           # mid
           lowercase__					:     List[Any]   = UNetMidBlockaD(
               in_channels=block_out_channels[-1]		,     temb_channels=a		,     resnet_eps=a		,     resnet_act_fn=a		,     output_scale_factor=a		,     resnet_time_scale_shift=a		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]		,     resnet_groups=a		,     add_attention=a		,     )
  
           # up
           lowercase__					:     List[Any]   = list(reversed(a))
           lowercase__					:     Tuple   = reversed_block_out_channels[0]
           for i, up_block_type in enumerate(a):
                lowercase__					:     List[str]   = output_channel
                lowercase__					:     List[Any]   = reversed_block_out_channels[i]
                lowercase__					:     Any   = reversed_block_out_channels[min(i + 1		,     len(a) - 1)]
  
                lowercase__					:     Union[str, Any]   = i == len(a) - 1
  
                lowercase__					:     Union[str, Any]   = get_up_block(
                    a		,     num_layers=layers_per_block + 1		,     in_channels=a		,     out_channels=a		,     prev_output_channel=a		,     temb_channels=a		,     add_upsample=not is_final_block		,     resnet_eps=a		,     resnet_act_fn=a		,     resnet_groups=a		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,     resnet_time_scale_shift=a		,     upsample_type=a		,     )
                self.up_blocks.append(a)
                lowercase__					:     List[Any]   = output_channel
  
           # out
           lowercase__					:     Optional[int]   = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,     32)
           lowercase__					:     str   = nn.GroupNorm(num_channels=block_out_channels[0]		,     num_groups=a		,     eps=a)
           lowercase__					:     Optional[int]   = nn.SiLU()
           lowercase__					:     Optional[Any]   = nn.Convad(block_out_channels[0]		,     a		,     kernel_size=3		,     padding=1)
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a = None		,     a = True		,     ):
           # 0. center input if necessary
           if self.config.center_input_sample:
                lowercase__					:     List[Any]   = 2 * sample - 1.0
  
           # 1. time
           lowercase__					:     List[str]   = timestep
           if not torch.is_tensor(a):
                lowercase__					:     Dict   = torch.tensor([timesteps]		,     dtype=torch.long		,     device=sample.device)
           elif torch.is_tensor(a) and len(timesteps.shape) == 0:
                lowercase__					:     List[Any]   = timesteps[None].to(sample.device)
  
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           lowercase__					:     Union[str, Any]   = timesteps * torch.ones(sample.shape[0]		,     dtype=timesteps.dtype		,     device=timesteps.device)
  
           lowercase__					:     Optional[int]   = self.time_proj(a)
  
           # timesteps does not contain any weights and will always return f32 tensors
           # but time_embedding might actually be running in fp16. so we need to cast here.
           # there might be better ways to encapsulate this.
           lowercase__					:     List[str]   = t_emb.to(dtype=self.dtype)
           lowercase__					:     List[Any]   = self.time_embedding(a)
  
           if self.class_embedding is not None:
                if class_labels is None:
                     raise ValueError('class_labels should be provided when doing class conditioning')
  
                if self.config.class_embed_type == "timestep":
                     lowercase__					:     Dict   = self.time_proj(a)
  
                lowercase__					:     str   = self.class_embedding(a).to(dtype=self.dtype)
                lowercase__					:     List[Any]   = emb + class_emb
  
           # 2. pre-process
           lowercase__					:     List[Any]   = sample
           lowercase__					:     List[Any]   = self.conv_in(a)
  
           # 3. down
           lowercase__					:     str   = (sample,)
           for downsample_block in self.down_blocks:
                if hasattr(a		,     'skip_conv'):
                     lowercase__   ,				lowercase__   ,				lowercase__					:     Optional[int]   = downsample_block(
                         hidden_states=a		,     temb=a		,     skip_sample=a)
                else:
                     lowercase__   ,				lowercase__					:     List[Any]   = downsample_block(hidden_states=a		,     temb=a)
  
                down_block_res_samples += res_samples
  
           # 4. mid
           lowercase__					:     Tuple   = self.mid_block(a		,     a)
  
           # 5. up
           lowercase__					:     List[str]   = None
           for upsample_block in self.up_blocks:
                lowercase__					:     Any   = down_block_res_samples[-len(upsample_block.resnets) :]
                lowercase__					:     Optional[Any]   = down_block_res_samples[: -len(upsample_block.resnets)]
  
                if hasattr(a		,     'skip_conv'):
                     lowercase__   ,				lowercase__					:     Optional[int]   = upsample_block(a		,     a		,     a		,     a)
                else:
                     lowercase__					:     List[Any]   = upsample_block(a		,     a		,     a)
  
          # 6. post-process
           lowercase__					:     Optional[int]   = self.conv_norm_out(a)
           lowercase__					:     List[str]   = self.conv_act(a)
           lowercase__					:     Optional[Any]   = self.conv_out(a)
  
           if skip_sample is not None:
                sample += skip_sample
  
           if self.config.time_embedding_type == "fourier":
                lowercase__					:     List[Any]   = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
                lowercase__					:     Any   = sample / timesteps
  
           if not return_dict:
                return (sample,)
  
           return UNetaDOutput(sample=a)
  
  
style_context: 
  
  from transformers.models.cpm.tokenization_cpm import CpmTokenizer
  from transformers.testing_utils import custom_tokenizers
  
  from ..xlnet.test_modeling_xlnet import XLNetModelTest
  
  
  @custom_tokenizers
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
           return True
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
           lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
           lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
           lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()
  
           lowercase__					:     Any   = tokenizer.tokenize(a)
           self.assertListEqual(a		,     a)
  
           lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]
  
           lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)
  
           lowercase__					:     Optional[Any]   = tokenizer.decode(a)
           self.assertEqual(a		,     a)
  
  