





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    snake_case_				(		_UpperCAmelCase						):
  A_										=    42






class    snake_case_				(		_UpperCAmelCase     ,_UpperCAmelCase						):






  @register_to_config
  def __init__(							self :     List[Any]   ,  _snake_case :     List[str] = None   ,  _snake_case :     List[Any] = 3   ,  _snake_case :     Tuple = 3   ,  _snake_case :     Dict = False   ,  _snake_case :     Tuple = "positional"   ,  _snake_case :     Any = 0   ,  _snake_case :     Optional[Any] = True   ,  _snake_case :     List[Any] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")   ,  _snake_case :     int = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")   ,  _snake_case :     List[str] = (224, 448, 672, 896)   ,  _snake_case :     Tuple = 2   ,  _snake_case :     Optional[Any] = 1   ,  _snake_case :     Any = 1   ,  _snake_case :     Dict = "conv"   ,  _snake_case :     Dict = "conv"   ,  _snake_case :     Optional[int] = "silu"   ,  _snake_case :     List[str] = 8   ,  _snake_case :     str = 32   ,  _snake_case :     str = 1E-5   ,  _snake_case :     Any = "default"   ,  _snake_case :     str = True   ,  _snake_case :     Optional[Any] = None   ,  _snake_case :     str = None   ,  )->Optional[int]:






   '''simple docstring'''

   super().__init__()

   __lowerCAmelCase     :       str						=   sample_size
   __lowerCAmelCase     :       Dict						=   block_out_channels[0] * 4

   # Check inputs
   if len(lowercase__			) != len(lowercase__			):
    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(lowercase__			) != len(lowercase__			):
    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
   __lowerCAmelCase     :       Tuple						=   nn.Convad(lowercase__   ,  block_out_channels[0]   ,  kernel_size=3   ,  padding=(1, 1)			)

   # time
   if time_embedding_type == "fourier":
    __lowerCAmelCase     :       Tuple						=   GaussianFourierProjection(embedding_size=block_out_channels[0]   ,  scale=16			)
    __lowerCAmelCase     :       Tuple						=   2 * block_out_channels[0]
   elif time_embedding_type == "positional":
    __lowerCAmelCase     :       Any						=   Timesteps(block_out_channels[0]   ,  lowercase__   ,  lowercase__			)
    __lowerCAmelCase     :       List[str]						=   block_out_channels[0]

   __lowerCAmelCase     :       Dict						=   TimestepEmbedding(lowercase__   ,  lowercase__			)

   # class embedding
   if class_embed_type is None and num_class_embeds is not None:
    __lowerCAmelCase     :       int						=   nn.Embedding(lowercase__   ,  lowercase__			)
   elif class_embed_type == "timestep":
    __lowerCAmelCase     :       Tuple						=   TimestepEmbedding(lowercase__   ,  lowercase__			)
   elif class_embed_type == "identity":
    __lowerCAmelCase     :       Dict						=   nn.Identity(lowercase__   ,  lowercase__			)
   else:
    __lowerCAmelCase     :       str						=   None

   __lowerCAmelCase     :       Optional[Any]						=   nn.ModuleList([]			)
   __lowerCAmelCase     :       Dict						=   None
   __lowerCAmelCase     :       Optional[Any]						=   nn.ModuleList([]			)

   # down
   __lowerCAmelCase     :       List[str]						=   block_out_channels[0]
   for i, down_block_type in enumerate(lowercase__			):
    __lowerCAmelCase     :       Tuple						=   output_channel
    __lowerCAmelCase     :       Tuple						=   block_out_channels[i]
    __lowerCAmelCase     :       Tuple						=   i == len(lowercase__			) - 1

    __lowerCAmelCase     :       List[str]						=   get_down_block(
        lowercase__   ,  num_layers=lowercase__   ,  in_channels=lowercase__   ,  out_channels=lowercase__   ,  temb_channels=lowercase__   ,  add_downsample=not is_final_block   ,  resnet_eps=lowercase__   ,  resnet_act_fn=lowercase__   ,  resnet_groups=lowercase__   ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel   ,  downsample_padding=lowercase__   ,  resnet_time_scale_shift=lowercase__   ,  downsample_type=lowercase__   ,  )
    self.down_blocks.append(lowercase__			)

   # mid
   __lowerCAmelCase     :       int						=   UNetMidBlockaD(
       in_channels=block_out_channels[-1]   ,  temb_channels=lowercase__   ,  resnet_eps=lowercase__   ,  resnet_act_fn=lowercase__   ,  output_scale_factor=lowercase__   ,  resnet_time_scale_shift=lowercase__   ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]   ,  resnet_groups=lowercase__   ,  add_attention=lowercase__   ,  )

   # up
   __lowerCAmelCase     :       List[str]						=   list(reversed(lowercase__			)			)
   __lowerCAmelCase     :       Union[str, Any]						=   reversed_block_out_channels[0]
   for i, up_block_type in enumerate(lowercase__			):
    __lowerCAmelCase     :       Optional[Any]						=   output_channel
    __lowerCAmelCase     :       int						=   reversed_block_out_channels[i]
    __lowerCAmelCase     :       Dict						=   reversed_block_out_channels[min(i + 1   ,  len(lowercase__			) - 1			)]

    __lowerCAmelCase     :       str						=   i == len(lowercase__			) - 1

    __lowerCAmelCase     :       Optional[int]						=   get_up_block(
        lowercase__   ,  num_layers=layers_per_block + 1   ,  in_channels=lowercase__   ,  out_channels=lowercase__   ,  prev_output_channel=lowercase__   ,  temb_channels=lowercase__   ,  add_upsample=not is_final_block   ,  resnet_eps=lowercase__   ,  resnet_act_fn=lowercase__   ,  resnet_groups=lowercase__   ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel   ,  resnet_time_scale_shift=lowercase__   ,  upsample_type=lowercase__   ,  )
    self.up_blocks.append(lowercase__			)
    __lowerCAmelCase     :       str						=   output_channel

   # out
   __lowerCAmelCase     :       str						=   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,  32			)
   __lowerCAmelCase     :       List[str]						=   nn.GroupNorm(num_channels=block_out_channels[0]   ,  num_groups=lowercase__   ,  eps=lowercase__			)
   __lowerCAmelCase     :       Tuple						=   nn.SiLU()
   __lowerCAmelCase     :       str						=   nn.Convad(block_out_channels[0]   ,  lowercase__   ,  kernel_size=3   ,  padding=1			)






  def       UpperCAmelCase__						(							self :     Dict   ,  _snake_case :     str   ,  _snake_case :     str   ,  _snake_case :     Union[str, Any] = None   ,  _snake_case :     Dict = True   ,  )->List[str]:






   '''simple docstring'''

   if self.config.center_input_sample:
    __lowerCAmelCase     :       Dict						=   2 * sample - 1.0

   # 1. time
   __lowerCAmelCase     :       Union[str, Any]						=   timestep
   if not torch.is_tensor(lowercase__			):
    __lowerCAmelCase     :       Optional[int]						=   torch.tensor([timesteps]   ,  dtype=torch.long   ,  device=sample.device			)
   elif torch.is_tensor(lowercase__			) and len(timesteps.shape			) == 0:
    __lowerCAmelCase     :       int						=   timesteps[None].to(sample.device			)

   # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
   __lowerCAmelCase     :       Union[str, Any]						=   timesteps * torch.ones(sample.shape[0]   ,  dtype=timesteps.dtype   ,  device=timesteps.device			)

   __lowerCAmelCase     :       Optional[Any]						=   self.time_proj(lowercase__			)

   # 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.
   __lowerCAmelCase     :       List[str]						=   t_emb.to(dtype=self.dtype			)
   __lowerCAmelCase     :       Tuple						=   self.time_embedding(lowercase__			)

   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":
     __lowerCAmelCase     :       Dict						=   self.time_proj(lowercase__			)

    __lowerCAmelCase     :       int						=   self.class_embedding(lowercase__			).to(dtype=self.dtype			)
    __lowerCAmelCase     :       Optional[Any]						=   emb + class_emb

   # 2. pre-process
   __lowerCAmelCase     :       str						=   sample
   __lowerCAmelCase     :       Optional[Any]						=   self.conv_in(lowercase__			)

   # 3. down
   __lowerCAmelCase     :       Optional[int]						=   (sample,)
   for downsample_block in self.down_blocks:
    if hasattr(lowercase__   ,  """skip_conv"""			):
     __lowerCAmelCase     :       Any						=   downsample_block(
         hidden_states=lowercase__   ,  temb=lowercase__   ,  skip_sample=lowercase__			)
    else:
     __lowerCAmelCase     :       int						=   downsample_block(hidden_states=lowercase__   ,  temb=lowercase__			)

    down_block_res_samples += res_samples

   # 4. mid
   __lowerCAmelCase     :       Dict						=   self.mid_block(lowercase__   ,  lowercase__			)

   # 5. up
   __lowerCAmelCase     :       List[Any]						=   None
   for upsample_block in self.up_blocks:
    __lowerCAmelCase     :       Tuple						=   down_block_res_samples[-len(upsample_block.resnets			) :]
    __lowerCAmelCase     :       Any						=   down_block_res_samples[: -len(upsample_block.resnets			)]

    if hasattr(lowercase__   ,  """skip_conv"""			):
     __lowerCAmelCase     :       int						=   upsample_block(lowercase__   ,  lowercase__   ,  lowercase__   ,  lowercase__			)
    else:
     __lowerCAmelCase     :       Union[str, Any]						=   upsample_block(lowercase__   ,  lowercase__   ,  lowercase__			)

        # 6. post-process
   __lowerCAmelCase     :       str						=   self.conv_norm_out(lowercase__			)
   __lowerCAmelCase     :       Dict						=   self.conv_act(lowercase__			)
   __lowerCAmelCase     :       Dict						=   self.conv_out(lowercase__			)

   if skip_sample is not None:
    sample += skip_sample

   if self.config.time_embedding_type == "fourier":
    __lowerCAmelCase     :       Optional[int]						=   timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]			)))			)
    __lowerCAmelCase     :       int						=   sample / timesteps

   if not return_dict:
    return (sample,)

   return UNetaDOutput(sample=lowercase__			)


'''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


snake_case_       =       logging.get_logger(__name__)

snake_case_       =       {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							"mobilevit"




    def __init__(					self				,     lowercase__=3				,     lowercase__=256				,     lowercase__=2				,     lowercase__=[144, 192, 240]				,     lowercase__=[16, 32, 64, 96, 128, 160, 640]				,     lowercase__=4				,     lowercase__=2.0				,     lowercase__=4.0				,     lowercase__="silu"				,     lowercase__=3				,     lowercase__=32				,     lowercase__=0.1				,     lowercase__=0.0				,     lowercase__=0.1				,     lowercase__=0.02				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=256				,     lowercase__=[6, 12, 18]				,     lowercase__=0.1				,     lowercase__=255				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			List[str]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       patch_size
          SCREAMING_SNAKE_CASE_      :			List[str]     =       hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       neck_hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       num_attention_heads
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       mlp_ratio
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       expand_ratio
          SCREAMING_SNAKE_CASE_      :			str     =       hidden_act
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       conv_kernel_size
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       output_stride
          SCREAMING_SNAKE_CASE_      :			Tuple     =       hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :			int     =       attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Tuple     =       classifier_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       initializer_range
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       layer_norm_eps
          SCREAMING_SNAKE_CASE_      :			str     =       qkv_bias

          # decode head attributes for semantic segmentation
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       aspp_out_channels
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       atrous_rates
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       aspp_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Any     =       semantic_loss_ignore_index






class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							version.parse("1.11"      )




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if self.task == "image-classification":
                return OrderedDict([("logits", {0: "batch"})]			)
          else:
                return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]			)







    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 1e-4

