





"""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 get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				torch.FloatTensor






class  _SCREAMING_SNAKE_CASE(       A   ,     A  ):




       @register_to_config
       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 6_55_36		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = "fourier"		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,SCREAMING_SNAKE_CASE__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,SCREAMING_SNAKE_CASE__ = "UNetMidBlock1D"		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = (32, 32, 64)		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 8		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = False		,)      ->				Dict:





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  sample_size

             # time
             if time_embedding_type == "fourier":
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  GaussianFourierProjection(
                       embedding_size=8		,set_W_to_weight=SCREAMING_SNAKE_CASE__		,log=SCREAMING_SNAKE_CASE__		,flip_sin_to_cos=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  2 * block_out_channels[0]
             elif time_embedding_type == "positional":
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  Timesteps(
                       block_out_channels[0]		,flip_sin_to_cos=SCREAMING_SNAKE_CASE__		,downscale_freq_shift=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :int		    =  block_out_channels[0]

             if use_timestep_embedding:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  block_out_channels[0] * 4
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  TimestepEmbedding(
                       in_channels=SCREAMING_SNAKE_CASE__		,time_embed_dim=SCREAMING_SNAKE_CASE__		,act_fn=SCREAMING_SNAKE_CASE__		,out_dim=block_out_channels[0]		,)

             __SCREAMING_SNAKE_CASE       :List[str]		    =  nn.ModuleList([]							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  None
             __SCREAMING_SNAKE_CASE       :Tuple		    =  nn.ModuleList([]							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  None

             # down
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  in_channels
             for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :Dict		    =  output_channel
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  block_out_channels[i]

                   if i == 0:
                         input_channel += extra_in_channels

                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  i == len(SCREAMING_SNAKE_CASE__							) - 1

                   __SCREAMING_SNAKE_CASE       :Dict		    =  get_down_block(
                       SCREAMING_SNAKE_CASE__		,num_layers=SCREAMING_SNAKE_CASE__		,in_channels=SCREAMING_SNAKE_CASE__		,out_channels=SCREAMING_SNAKE_CASE__		,temb_channels=block_out_channels[0]		,add_downsample=not is_final_block or downsample_each_block		,)
                   self.down_blocks.append(SCREAMING_SNAKE_CASE__							)

             # mid
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  get_mid_block(
                 SCREAMING_SNAKE_CASE__		,in_channels=block_out_channels[-1]		,mid_channels=block_out_channels[-1]		,out_channels=block_out_channels[-1]		,embed_dim=block_out_channels[0]		,num_layers=SCREAMING_SNAKE_CASE__		,add_downsample=SCREAMING_SNAKE_CASE__		,)

             # up
             __SCREAMING_SNAKE_CASE       :List[str]		    =  list(reversed(SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  reversed_block_out_channels[0]
             if out_block_type is None:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  out_channels
             else:
                   __SCREAMING_SNAKE_CASE       :Any		    =  block_out_channels[0]

             for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  output_channel
                   __SCREAMING_SNAKE_CASE       :Dict		    =  (
                       reversed_block_out_channels[i + 1] if i < len(SCREAMING_SNAKE_CASE__							) - 1 else final_upsample_channels
                   )

                   __SCREAMING_SNAKE_CASE       :Any		    =  i == len(SCREAMING_SNAKE_CASE__							) - 1

                   __SCREAMING_SNAKE_CASE       :Any		    =  get_up_block(
                       SCREAMING_SNAKE_CASE__		,num_layers=SCREAMING_SNAKE_CASE__		,in_channels=SCREAMING_SNAKE_CASE__		,out_channels=SCREAMING_SNAKE_CASE__		,temb_channels=block_out_channels[0]		,add_upsample=not is_final_block		,)
                   self.up_blocks.append(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :int		    =  output_channel

             # out
             __SCREAMING_SNAKE_CASE       :int		    =  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,32							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  get_out_block(
                 out_block_type=SCREAMING_SNAKE_CASE__		,num_groups_out=SCREAMING_SNAKE_CASE__		,embed_dim=block_out_channels[0]		,out_channels=SCREAMING_SNAKE_CASE__		,act_fn=SCREAMING_SNAKE_CASE__		,fc_dim=block_out_channels[-1] // 4		,)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)      ->				Union[UNetaDOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  timestep
             if not torch.is_tensor(SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :Any		    =  torch.tensor([timesteps]		,dtype=torch.long		,device=sample.device							)
             elif torch.is_tensor(SCREAMING_SNAKE_CASE__							) and len(timesteps.shape							) == 0:
                   __SCREAMING_SNAKE_CASE       :int		    =  timesteps[None].to(sample.device							)

             __SCREAMING_SNAKE_CASE       :str		    =  self.time_proj(SCREAMING_SNAKE_CASE__							)
             if self.config.use_timestep_embedding:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.time_mlp(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE       :int		    =  timestep_embed[..., None]
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

             # 2. down
             __SCREAMING_SNAKE_CASE       :Dict		    =  ()
             for downsample_block in self.down_blocks:
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[int]		    =  downsample_block(hidden_states=SCREAMING_SNAKE_CASE__		,temb=SCREAMING_SNAKE_CASE__							)
                   down_block_res_samples += res_samples

             # 3. mid
             if self.mid_block:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  self.mid_block(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             # 4. up
             for i, upsample_block in enumerate(self.up_blocks							):
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  down_block_res_samples[-1:]
                   __SCREAMING_SNAKE_CASE       :Any		    =  down_block_res_samples[:-1]
                   __SCREAMING_SNAKE_CASE       :str		    =  upsample_block(SCREAMING_SNAKE_CASE__		,res_hidden_states_tuple=SCREAMING_SNAKE_CASE__		,temb=SCREAMING_SNAKE_CASE__							)

             # 5. post-process
             if self.out_block:
                   __SCREAMING_SNAKE_CASE       :str		    =  self.out_block(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             if not return_dict:
                   return (sample,)

             return UNetaDOutput(sample=SCREAMING_SNAKE_CASE__							)






"""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_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  '''levit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=[1_28, 2_56, 3_84]		,SCREAMING_SNAKE_CASE__=[4, 8, 12]		,SCREAMING_SNAKE_CASE__=[4, 4, 4]		,SCREAMING_SNAKE_CASE__=[16, 16, 16]		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=[2, 2, 2]		,SCREAMING_SNAKE_CASE__=[2, 2, 2]		,SCREAMING_SNAKE_CASE__=0.0_2		,**SCREAMING_SNAKE_CASE__		,)      ->				Tuple:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  image_size
             __SCREAMING_SNAKE_CASE       :Dict		    =  num_channels
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  kernel_size
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  stride
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  padding
             __SCREAMING_SNAKE_CASE       :Tuple		    =  hidden_sizes
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  depths
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  key_dim
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  drop_path_rate
             __SCREAMING_SNAKE_CASE       :Tuple		    =  patch_size
             __SCREAMING_SNAKE_CASE       :int		    =  attention_ratio
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  mlp_ratio
             __SCREAMING_SNAKE_CASE       :str		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [
                 ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
                 ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
             ]






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				int     						=  version.parse('''1.11'''  )




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






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




       @property
       def    _UpperCamelCase					(						self							)      ->				float:





             """simple docstring"""






             return 1E-4