




'''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      UpperCamelCase       (	snake_case__       ):
  a     :torch.FloatTensor






class      UpperCamelCase       (	snake_case__      ,		snake_case__       ):



  @register_to_config
  def __init__(			self		,     __lowerCAmelCase = None		,     __lowerCAmelCase = 3		,     __lowerCAmelCase = 3		,     __lowerCAmelCase = False		,     __lowerCAmelCase = "positional"		,     __lowerCAmelCase = 0		,     __lowerCAmelCase = True		,     __lowerCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,     __lowerCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,     __lowerCAmelCase = (224, 448, 672, 896)		,     __lowerCAmelCase = 2		,     __lowerCAmelCase = 1		,     __lowerCAmelCase = 1		,     __lowerCAmelCase = "conv"		,     __lowerCAmelCase = "conv"		,     __lowerCAmelCase = "silu"		,     __lowerCAmelCase = 8		,     __lowerCAmelCase = 32		,     __lowerCAmelCase = 1e-5		,     __lowerCAmelCase = "default"		,     __lowerCAmelCase = True		,     __lowerCAmelCase = None		,     __lowerCAmelCase = None		,     ):







      '''simple docstring'''


      super().__init__()

      _a				:							Tuple							       =      sample_size
      _a				:							List[str]							       =      block_out_channels[0] * 4

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

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

      _a				:							Optional[Any]							       =      TimestepEmbedding(__lowerCAmelCase		,     __lowerCAmelCase )

      # class embedding
      if class_embed_type is None and num_class_embeds is not None:
          _a				:							Optional[Any]							       =      nn.Embedding(__lowerCAmelCase		,     __lowerCAmelCase )
      elif class_embed_type == "timestep":
          _a				:							Any							       =      TimestepEmbedding(__lowerCAmelCase		,     __lowerCAmelCase )
      elif class_embed_type == "identity":
          _a				:							str							       =      nn.Identity(__lowerCAmelCase		,     __lowerCAmelCase )
      else:
          _a				:							Dict							       =      None

      _a				:							List[Any]							       =      nn.ModuleList([] )
      _a				:							str							       =      None
      _a				:							Tuple							       =      nn.ModuleList([] )

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

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

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

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

          _a				:							Optional[int]							       =      i == len(__lowerCAmelCase ) - 1

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

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



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = None		,     __lowerCAmelCase = True		,     ):







      '''simple docstring'''


      if self.config.center_input_sample:
          _a				:							str							       =      2 * sample - 1.0

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

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

      _a				:							Dict							       =      self.time_proj(__lowerCAmelCase )

      # 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				:							Optional[Any]							       =      t_emb.to(dtype=self.dtype )
      _a				:							List[str]							       =      self.time_embedding(__lowerCAmelCase )

      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				:							Any							       =      self.time_proj(__lowerCAmelCase )

          _a				:							Any							       =      self.class_embedding(__lowerCAmelCase ).to(dtype=self.dtype )
          _a				:							List[Any]							       =      emb + class_emb

      # 2. pre-process
      _a				:							str							       =      sample
      _a				:							str							       =      self.conv_in(__lowerCAmelCase )

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

          down_block_res_samples += res_samples

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

      # 5. up
      _a				:							List[Any]							       =      None
      for upsample_block in self.up_blocks:
          _a				:							Optional[Any]							       =      down_block_res_samples[-len(upsample_block.resnets ) :]
          _a				:							Union[str, Any]							       =      down_block_res_samples[: -len(upsample_block.resnets )]

          if hasattr(__lowerCAmelCase		,     """skip_conv""" ):
              _a       , _a				:							List[str]							       =      upsample_block(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
          else:
              _a				:							Tuple							       =      upsample_block(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )

        # 6. post-process
      _a				:							List[Any]							       =      self.conv_norm_out(__lowerCAmelCase )
      _a				:							Optional[int]							       =      self.conv_act(__lowerCAmelCase )
      _a				:							Any							       =      self.conv_out(__lowerCAmelCase )

      if skip_sample is not None:
          sample += skip_sample

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

      if not return_dict:
          return (sample,)

      return UNetaDOutput(sample=__lowerCAmelCase )







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
		import torch

__magic_name__       =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
		from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):





		def 		__snake_case						(    cls				):
							"""simple docstring"""






							return cls





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			):
					"""simple docstring"""






					lowercase						   =       device
					lowercase						   =       shape[0]

					lowercase						   =       layout or torch.strided
					lowercase						   =       device or torch.device('cpu'				)

					if generator is not None:
										lowercase						   =       generator.device.type if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase				) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															lowercase						   =       'cpu'
															if device != "mps":
																				logger.info(
																				    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																				    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																				    f""" slighly speed up this function by passing a generator that was created on the {device} device."""				)
										elif gen_device_type != device.type and gen_device_type == "cuda":
															raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""				)

					if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase				):
										lowercase						   =       (1,) + shape[1:]
										lowercase						   =       [
										    torch.randn(_UpperCAmelCase       ,			generator=generator[i]       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				)
										    for i in range(_UpperCAmelCase				)
										]
										lowercase						   =       torch.cat(_UpperCAmelCase       ,			dim=0				).to(_UpperCAmelCase				)
					else:
										lowercase						   =       torch.randn(_UpperCAmelCase       ,			generator=_UpperCAmelCase       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				).to(_UpperCAmelCase				)

					return latents





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if is_torch_version('<'       ,			'2.0.0'				) or not hasattr(_UpperCAmelCase       ,			'_dynamo'				):
										return False
					return isinstance(_UpperCAmelCase       ,			torch._dynamo.eval_frame.OptimizedModule				)






