





'''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      lowerCAmelCase							(      __UpperCAmelCase    ):
 UpperCAmelCase__									=	42


class      lowerCAmelCase							(      __UpperCAmelCase,						__UpperCAmelCase    ):
 @register_to_config
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   int = 65536    ,  UpperCAmelCase						:   Optional[int] = None    ,  UpperCAmelCase						:   int = 2    ,  UpperCAmelCase						:   int = 2    ,  UpperCAmelCase						:   int = 0    ,  UpperCAmelCase						:   str = "fourier"    ,  UpperCAmelCase						:   bool = True    ,  UpperCAmelCase						:   bool = False    ,  UpperCAmelCase						:   float = 0.0    ,  UpperCAmelCase						:   Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")    ,  UpperCAmelCase						:   Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")    ,  UpperCAmelCase						:   Tuple[str] = "UNetMidBlock1D"    ,  UpperCAmelCase						:   str = None    ,  UpperCAmelCase						:   Tuple[int] = (32, 32, 64)    ,  UpperCAmelCase						:   str = None    ,  UpperCAmelCase						:   int = 8    ,  UpperCAmelCase						:   int = 1    ,  UpperCAmelCase						:   bool = False    ,  )    ->			Dict:
     super().__init__()
     lowerCamelCase__					:   str      				=	sample_size

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

     if use_timestep_embedding:
         lowerCamelCase__					:   Any      				=	block_out_channels[0] * 4
         lowerCamelCase__					:   List[Any]      				=	TimestepEmbedding(
             in_channels=lowerCAmelCase_    ,  time_embed_dim=lowerCAmelCase_    ,  act_fn=lowerCAmelCase_    ,  out_dim=block_out_channels[0]    ,  )

     lowerCamelCase__					:   Optional[int]      				=	nn.ModuleList([]			)
     lowerCamelCase__					:   Tuple      				=	None
     lowerCamelCase__					:   Dict      				=	nn.ModuleList([]			)
     lowerCamelCase__					:   Optional[int]      				=	None

     # down
     lowerCamelCase__					:   Union[str, Any]      				=	in_channels
     for i, down_block_type in enumerate(lowerCAmelCase_			):
         lowerCamelCase__					:   Union[str, Any]      				=	output_channel
         lowerCamelCase__					:   Any      				=	block_out_channels[i]

         if i == 0:
             input_channel += extra_in_channels

         lowerCamelCase__					:   int      				=	i == len(lowerCAmelCase_			) - 1

         lowerCamelCase__					:   str      				=	get_down_block(
             lowerCAmelCase_    ,  num_layers=lowerCAmelCase_    ,  in_channels=lowerCAmelCase_    ,  out_channels=lowerCAmelCase_    ,  temb_channels=block_out_channels[0]    ,  add_downsample=not is_final_block or downsample_each_block    ,  )
         self.down_blocks.append(lowerCAmelCase_			)

     # mid
     lowerCamelCase__					:   List[str]      				=	get_mid_block(
         lowerCAmelCase_    ,  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=lowerCAmelCase_    ,  add_downsample=lowerCAmelCase_    ,  )

     # up
     lowerCamelCase__					:   str      				=	list(reversed(lowerCAmelCase_			)			)
     lowerCamelCase__					:   Any      				=	reversed_block_out_channels[0]
     if out_block_type is None:
         lowerCamelCase__					:   Union[str, Any]      				=	out_channels
     else:
         lowerCamelCase__					:   int      				=	block_out_channels[0]

     for i, up_block_type in enumerate(lowerCAmelCase_			):
         lowerCamelCase__					:   List[str]      				=	output_channel
         lowerCamelCase__					:   Union[str, Any]      				=	(
             reversed_block_out_channels[i + 1] if i < len(lowerCAmelCase_			) - 1 else final_upsample_channels
         )

         lowerCamelCase__					:   List[Any]      				=	i == len(lowerCAmelCase_			) - 1

         lowerCamelCase__					:   Dict      				=	get_up_block(
             lowerCAmelCase_    ,  num_layers=lowerCAmelCase_    ,  in_channels=lowerCAmelCase_    ,  out_channels=lowerCAmelCase_    ,  temb_channels=block_out_channels[0]    ,  add_upsample=not is_final_block    ,  )
         self.up_blocks.append(lowerCAmelCase_			)
         lowerCamelCase__					:   List[str]      				=	output_channel

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







 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   torch.FloatTensor    ,  UpperCAmelCase						:   Union[torch.Tensor, float, int]    ,  UpperCAmelCase						:   bool = True    ,  )    ->			Optional[Any]:
     lowerCamelCase__					:   List[Any]      				=	timestep
     if not torch.is_tensor(lowerCAmelCase_			):
         lowerCamelCase__					:   Tuple      				=	torch.tensor([timesteps]    ,  dtype=torch.long    ,  device=sample.device			)
     elif torch.is_tensor(lowerCAmelCase_			) and len(timesteps.shape			) == 0:
         lowerCamelCase__					:   int      				=	timesteps[None].to(sample.device			)

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

     # 2. down
     lowerCamelCase__					:   Tuple      				=	()
     for downsample_block in self.down_blocks:
         lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	downsample_block(hidden_states=lowerCAmelCase_    ,  temb=lowerCAmelCase_			)
         down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
         lowerCamelCase__					:   Any      				=	self.mid_block(lowerCAmelCase_    ,  lowerCAmelCase_			)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks			):
         lowerCamelCase__					:   Any      				=	down_block_res_samples[-1:]
         lowerCamelCase__					:   Union[str, Any]      				=	down_block_res_samples[:-1]
         lowerCamelCase__					:   List[str]      				=	upsample_block(lowerCAmelCase_    ,  res_hidden_states_tuple=lowerCAmelCase_    ,  temb=lowerCAmelCase_			)

     # 5. post-process
     if self.out_block:
         lowerCamelCase__					:   Any      				=	self.out_block(lowerCAmelCase_    ,  lowerCAmelCase_			)

     if not return_dict:
         return (sample,)

     return UNetaDOutput(sample=lowerCAmelCase_			)












import enum
import shutil
import sys


_UpperCAmelCase     ,_UpperCAmelCase       : List[Any]			     =       shutil.get_terminal_size()

_UpperCAmelCase       : Union[str, Any]			     =       {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}

class      lowerCAmelCase							(      enum.Enum    ):
 UpperCAmelCase__									=	0
 UpperCAmelCase__									=	1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  int:
    sys.stdout.write(str(_UpperCAmelCase				) + end				)
    sys.stdout.flush()


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  Union[str, Any]:
    forceWrite(F"""\u001b[{color}m{content}\u001b[0m"""		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    forceWrite('\r'				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[Any]:
    forceWrite(F"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""				)


def  SCREAMING_SNAKE_CASE   ( )    ->  int:
    forceWrite(' ' * TERMINAL_WIDTH				)
    reset_cursor()


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    reset_cursor()
    forceWrite('-' * TERMINAL_WIDTH				)





