code: 
  
  
  """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 UNetMidBlockaD, get_down_block, get_up_block
  
  
  
  
  
  
  @dataclass
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						42
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   @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				,					)      ->  str:
       super().__init__()
  
       a					=sample_size
       a					=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
       a					=nn.Convad(__A				,					block_out_channels[0]				,					kernel_size=3				,					padding=(1, 1)		)
  
       # time
       if time_embedding_type == "fourier":
           a					=GaussianFourierProjection(embedding_size=block_out_channels[0]				,					scale=16		)
           a					=2 * block_out_channels[0]
       elif time_embedding_type == "positional":
           a					=Timesteps(block_out_channels[0]				,					__A				,					__A		)
           a					=block_out_channels[0]
  
       a					=TimestepEmbedding(__A				,					__A		)
  
       # class embedding
       if class_embed_type is None and num_class_embeds is not None:
           a					=nn.Embedding(__A				,					__A		)
       elif class_embed_type == "timestep":
           a					=TimestepEmbedding(__A				,					__A		)
       elif class_embed_type == "identity":
           a					=nn.Identity(__A				,					__A		)
       else:
           a					=None
  
       a					=nn.ModuleList([]		)
       a					=None
       a					=nn.ModuleList([]		)
  
       # down
       a					=block_out_channels[0]
       for i, down_block_type in enumerate(__A		):
           a					=output_channel
           a					=block_out_channels[i]
           a					=i == len(__A		) - 1
  
           a					=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
       a					=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
       a					=list(reversed(__A		)		)
       a					=reversed_block_out_channels[0]
       for i, up_block_type in enumerate(__A		):
           a					=output_channel
           a					=reversed_block_out_channels[i]
           a					=reversed_block_out_channels[min(i + 1				,					len(__A		) - 1		)]
  
           a					=i == len(__A		) - 1
  
           a					=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		)
           a					=output_channel
  
       # out
       a					=norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,					32		)
       a					=nn.GroupNorm(num_channels=block_out_channels[0]				,					num_groups=__A				,					eps=__A		)
       a					=nn.SiLU()
       a					=nn.Convad(block_out_channels[0]				,					__A				,					kernel_size=3				,					padding=1		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A = None				,					__A = True				,					)      ->  Union[UNetaDOutput, Tuple]:
       # 0. center input if necessary
       if self.config.center_input_sample:
           a					=2 * sample - 1.0
  
       # 1. time
       a					=timestep
       if not torch.is_tensor(__A		):
           a					=torch.tensor([timesteps]				,					dtype=torch.long				,					device=sample.device		)
       elif torch.is_tensor(__A		) and len(timesteps.shape		) == 0:
           a					=timesteps[None].to(sample.device		)
  
       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       a					=timesteps * torch.ones(sample.shape[0]				,					dtype=timesteps.dtype				,					device=timesteps.device		)
  
       a					=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.
       a					=t_emb.to(dtype=self.dtype		)
       a					=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":
               a					=self.time_proj(__A		)
  
           a					=self.class_embedding(__A		).to(dtype=self.dtype		)
           a					=emb + class_emb
  
       # 2. pre-process
       a					=sample
       a					=self.conv_in(__A		)
  
       # 3. down
       a					=(sample,)
       for downsample_block in self.down_blocks:
           if hasattr(__A				,					'''skip_conv'''		):
               a	,   a	,   a					=downsample_block(
                   hidden_states=__A				,					temb=__A				,					skip_sample=__A		)
           else:
               a	,   a					=downsample_block(hidden_states=__A				,					temb=__A		)
  
           down_block_res_samples += res_samples
  
       # 4. mid
       a					=self.mid_block(__A				,					__A		)
  
       # 5. up
       a					=None
       for upsample_block in self.up_blocks:
           a					=down_block_res_samples[-len(upsample_block.resnets		) :]
           a					=down_block_res_samples[: -len(upsample_block.resnets		)]
  
           if hasattr(__A				,					'''skip_conv'''		):
               a	,   a					=upsample_block(__A				,					__A				,					__A				,					__A		)
           else:
               a					=upsample_block(__A				,					__A				,					__A		)
  
          # 6. post-process
       a					=self.conv_norm_out(__A		)
       a					=self.conv_act(__A		)
       a					=self.conv_out(__A		)
  
       if skip_sample is not None:
           sample += skip_sample
  
       if self.config.time_embedding_type == "fourier":
           a					=timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]		)))		)
           a					=sample / timesteps
  
       if not return_dict:
           return (sample,)
  
       return UNetaDOutput(sample=__A		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Tuple    		=						{
      """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"mobilevitv2"
  
  
   def __init__(	self				,					__A=3				,					__A=256				,					__A=2				,					__A=2.0				,					__A="swish"				,					__A=3				,					__A=32				,					__A=0.1				,					__A=0.02				,					__A=1E-5				,					__A=512				,					__A=[6, 12, 18]				,					__A=0.1				,					__A=255				,					__A=[2, 4, 3]				,					__A=[128, 192, 256]				,					__A=1.0				,					__A=2				,					__A=0.0				,					__A=0.0				,					**__A				,					)      ->  int:
       super().__init__(**__A		)
  
       a					=num_channels
       a					=image_size
       a					=patch_size
       a					=expand_ratio
       a					=hidden_act
       a					=conv_kernel_size
       a					=output_stride
       a					=initializer_range
       a					=layer_norm_eps
       a					=n_attn_blocks
       a					=base_attn_unit_dims
       a					=width_multiplier
       a					=ffn_multiplier
       a					=ffn_dropout
       a					=attn_dropout
       a					=classifier_dropout_prob
  
       # decode head attributes for semantic segmentation
       a					=aspp_out_channels
       a					=atrous_rates
       a					=aspp_dropout_prob
       a					=semantic_loss_ignore_index
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						version.parse("1.11"    )
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]		)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]		)
  
  
  
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  float:
       return 1E-4