





'''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__				):
     lowerCAmelCase						     =			42




class 					SCREAMING_SNAKE_CASE       (a__   ,						a__				):


     @register_to_config
     def __init__(		self		,      _UpperCAmelCase = 6_5536		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 2		,      _UpperCAmelCase = 2		,      _UpperCAmelCase = 0		,      _UpperCAmelCase = "fourier"		,      _UpperCAmelCase = True		,      _UpperCAmelCase = False		,      _UpperCAmelCase = 0.0		,      _UpperCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,      _UpperCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,      _UpperCAmelCase = "UNetMidBlock1D"		,      _UpperCAmelCase = None		,      _UpperCAmelCase = (32, 32, 64)		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 8		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = False		,      ):

          '''simple docstring'''






          super().__init__()
          __A		:					Dict						=			sample_size

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

          if use_timestep_embedding:
               __A		:					Optional[Any]						=			block_out_channels[0] * 4
               __A		:					Optional[int]						=			TimestepEmbedding(
                   in_channels=_UpperCAmelCase		,      time_embed_dim=_UpperCAmelCase		,      act_fn=_UpperCAmelCase		,      out_dim=block_out_channels[0]		,      )

          __A		:					Dict						=			nn.ModuleList([])
          __A		:					Dict						=			None
          __A		:					Tuple						=			nn.ModuleList([])
          __A		:					Tuple						=			None

          # down
          __A		:					Any						=			in_channels
          for i, down_block_type in enumerate(_UpperCAmelCase):
               __A		:					Tuple						=			output_channel
               __A		:					Optional[Any]						=			block_out_channels[i]

               if i == 0:
                    input_channel += extra_in_channels

               __A		:					List[str]						=			i == len(_UpperCAmelCase) - 1

               __A		:					int						=			get_down_block(
                   _UpperCAmelCase		,      num_layers=_UpperCAmelCase		,      in_channels=_UpperCAmelCase		,      out_channels=_UpperCAmelCase		,      temb_channels=block_out_channels[0]		,      add_downsample=not is_final_block or downsample_each_block		,      )
               self.down_blocks.append(_UpperCAmelCase)

          # mid
          __A		:					str						=			get_mid_block(
              _UpperCAmelCase		,      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=_UpperCAmelCase		,      add_downsample=_UpperCAmelCase		,      )

          # up
          __A		:					Optional[int]						=			list(reversed(_UpperCAmelCase))
          __A		:					Optional[int]						=			reversed_block_out_channels[0]
          if out_block_type is None:
               __A		:					str						=			out_channels
          else:
               __A		:					List[Any]						=			block_out_channels[0]

          for i, up_block_type in enumerate(_UpperCAmelCase):
               __A		:					Optional[Any]						=			output_channel
               __A		:					Optional[Any]						=			(
                   reversed_block_out_channels[i + 1] if i < len(_UpperCAmelCase) - 1 else final_upsample_channels
               )

               __A		:					Dict						=			i == len(_UpperCAmelCase) - 1

               __A		:					str						=			get_up_block(
                   _UpperCAmelCase		,      num_layers=_UpperCAmelCase		,      in_channels=_UpperCAmelCase		,      out_channels=_UpperCAmelCase		,      temb_channels=block_out_channels[0]		,      add_upsample=not is_final_block		,      )
               self.up_blocks.append(_UpperCAmelCase)
               __A		:					Optional[int]						=			output_channel

          # out
          __A		:					str						=			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,      32)
          __A		:					Optional[Any]						=			get_out_block(
              out_block_type=_UpperCAmelCase		,      num_groups_out=_UpperCAmelCase		,      embed_dim=block_out_channels[0]		,      out_channels=_UpperCAmelCase		,      act_fn=_UpperCAmelCase		,      fc_dim=block_out_channels[-1] // 4		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = True		,      ):

          '''simple docstring'''






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

          __A		:					List[Any]						=			self.time_proj(_UpperCAmelCase)
          if self.config.use_timestep_embedding:
               __A		:					Dict						=			self.time_mlp(_UpperCAmelCase)
          else:
               __A		:					Dict						=			timestep_embed[..., None]
               __A		:					Tuple						=			timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
               __A		:					List[Any]						=			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

          # 2. down
          __A		:					int						=			()
          for downsample_block in self.down_blocks:
               __A					,__A		:					int						=			downsample_block(hidden_states=_UpperCAmelCase		,      temb=_UpperCAmelCase)
               down_block_res_samples += res_samples

          # 3. mid
          if self.mid_block:
               __A		:					Optional[int]						=			self.mid_block(_UpperCAmelCase		,      _UpperCAmelCase)

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

          # 5. post-process
          if self.out_block:
               __A		:					Dict						=			self.out_block(_UpperCAmelCase		,      _UpperCAmelCase)

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=_UpperCAmelCase)






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     :						List[str]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Tuple						       =  {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class       a							(   a__  ):
 snake_case__             =       '''glpn'''



 def __init__(     self				,       _snake_case=3				,       _snake_case=4				,       _snake_case=[2, 2, 2, 2]				,       _snake_case=[8, 4, 2, 1]				,       _snake_case=[32, 64, 1_60, 2_56]				,       _snake_case=[7, 3, 3, 3]				,       _snake_case=[4, 2, 2, 2]				,       _snake_case=[1, 2, 5, 8]				,       _snake_case=[4, 4, 4, 4]				,       _snake_case="gelu"				,       _snake_case=0.0				,       _snake_case=0.0				,       _snake_case=0.02				,       _snake_case=0.1				,       _snake_case=1E-6				,       _snake_case=64				,       _snake_case=10				,       _snake_case=-1				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(**_snake_case     )

  lowerCAmelCase    				= num_channels
  lowerCAmelCase    				= num_encoder_blocks
  lowerCAmelCase    				= depths
  lowerCAmelCase    				= sr_ratios
  lowerCAmelCase    				= hidden_sizes
  lowerCAmelCase    				= patch_sizes
  lowerCAmelCase    				= strides
  lowerCAmelCase    				= mlp_ratios
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= drop_path_rate
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= decoder_hidden_size
  lowerCAmelCase    				= max_depth
  lowerCAmelCase    				= head_in_index





