



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        lowerCAmelCase_	(						_a							):
       UpperCAmelCase__       :      torch.FloatTensor






class        lowerCAmelCase_	(						_a ,					_a							):
       @register_to_config
       def __init__(    self,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = 3,    SCREAMING_SNAKE_CASE_ = 3,    SCREAMING_SNAKE_CASE_ = False,    SCREAMING_SNAKE_CASE_ = "positional",    SCREAMING_SNAKE_CASE_ = 0,    SCREAMING_SNAKE_CASE_ = True,    SCREAMING_SNAKE_CASE_ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),    SCREAMING_SNAKE_CASE_ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),    SCREAMING_SNAKE_CASE_ = (224, 448, 672, 896),    SCREAMING_SNAKE_CASE_ = 2,    SCREAMING_SNAKE_CASE_ = 1,    SCREAMING_SNAKE_CASE_ = 1,    SCREAMING_SNAKE_CASE_ = "conv",    SCREAMING_SNAKE_CASE_ = "conv",    SCREAMING_SNAKE_CASE_ = "silu",    SCREAMING_SNAKE_CASE_ = 8,    SCREAMING_SNAKE_CASE_ = 32,    SCREAMING_SNAKE_CASE_ = 1e-5,    SCREAMING_SNAKE_CASE_ = "default",    SCREAMING_SNAKE_CASE_ = True,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = None,    )  ->	List[Any]:
          super().__init__()

          UpperCamelCase					:						Tuple					=			sample_size
          UpperCamelCase					:						int					=			block_out_channels[0] * 4

          # Check inputs
          if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
             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(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
             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
          UpperCamelCase					:						Optional[int]					=			nn.Convad(SCREAMING_SNAKE_CASE_,    block_out_channels[0],    kernel_size=3,    padding=(1, 1) )

          # time
          if time_embedding_type == "fourier":
             UpperCamelCase					:						Optional[Any]					=			GaussianFourierProjection(embedding_size=block_out_channels[0],    scale=16 )
             UpperCamelCase					:						Any					=			2 * block_out_channels[0]
          elif time_embedding_type == "positional":
             UpperCamelCase					:						List[str]					=			Timesteps(block_out_channels[0],    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
             UpperCamelCase					:						Optional[int]					=			block_out_channels[0]

          UpperCamelCase					:						List[Any]					=			TimestepEmbedding(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )

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

          UpperCamelCase					:						str					=			nn.ModuleList([] )
          UpperCamelCase					:						Optional[int]					=			None
          UpperCamelCase					:						Tuple					=			nn.ModuleList([] )

          # down
          UpperCamelCase					:						List[Any]					=			block_out_channels[0]
          for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase					:						Any					=			output_channel
             UpperCamelCase					:						int					=			block_out_channels[i]
             UpperCamelCase					:						Optional[Any]					=			i == len(SCREAMING_SNAKE_CASE_ ) - 1

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

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

          # up
          UpperCamelCase					:						List[Any]					=			list(reversed(SCREAMING_SNAKE_CASE_ ) )
          UpperCamelCase					:						Union[str, Any]					=			reversed_block_out_channels[0]
          for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase					:						List[Any]					=			output_channel
             UpperCamelCase					:						str					=			reversed_block_out_channels[i]
             UpperCamelCase					:						Tuple					=			reversed_block_out_channels[min(i + 1,    len(SCREAMING_SNAKE_CASE_ ) - 1 )]

             UpperCamelCase					:						Optional[Any]					=			i == len(SCREAMING_SNAKE_CASE_ ) - 1

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

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






       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None,    SCREAMING_SNAKE_CASE_ = True,    )  ->	Union[UNetaDOutput, Tuple]:
          # 0. center input if necessary
          if self.config.center_input_sample:
             UpperCamelCase					:						Dict					=			2 * sample - 1.0

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

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

          UpperCamelCase					:						Union[str, Any]					=			self.time_proj(SCREAMING_SNAKE_CASE_ )

          # 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.
          UpperCamelCase					:						List[str]					=			t_emb.to(dtype=self.dtype )
          UpperCamelCase					:						str					=			self.time_embedding(SCREAMING_SNAKE_CASE_ )

          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":
                UpperCamelCase					:						List[Any]					=			self.time_proj(SCREAMING_SNAKE_CASE_ )

             UpperCamelCase					:						int					=			self.class_embedding(SCREAMING_SNAKE_CASE_ ).to(dtype=self.dtype )
             UpperCamelCase					:						Dict					=			emb + class_emb

          # 2. pre-process
          UpperCamelCase					:						int					=			sample
          UpperCamelCase					:						List[str]					=			self.conv_in(SCREAMING_SNAKE_CASE_ )

          # 3. down
          UpperCamelCase					:						List[str]					=			(sample,)
          for downsample_block in self.down_blocks:
             if hasattr(SCREAMING_SNAKE_CASE_,    'skip_conv' ):
                UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			downsample_block(
                    hidden_states=SCREAMING_SNAKE_CASE_,    temb=SCREAMING_SNAKE_CASE_,    skip_sample=SCREAMING_SNAKE_CASE_ )
             else:
                UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			downsample_block(hidden_states=SCREAMING_SNAKE_CASE_,    temb=SCREAMING_SNAKE_CASE_ )

             down_block_res_samples += res_samples

          # 4. mid
          UpperCamelCase					:						Optional[int]					=			self.mid_block(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )

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

             if hasattr(SCREAMING_SNAKE_CASE_,    'skip_conv' ):
                UpperCamelCase			,  UpperCamelCase					:						str					=			upsample_block(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
             else:
                UpperCamelCase					:						Tuple					=			upsample_block(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )

             # 6. post-process
          UpperCamelCase					:						int					=			self.conv_norm_out(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase					:						Optional[Any]					=			self.conv_act(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase					:						int					=			self.conv_out(SCREAMING_SNAKE_CASE_ )

          if skip_sample is not None:
             sample += skip_sample

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

          if not return_dict:
             return (sample,)

          return UNetaDOutput(sample=SCREAMING_SNAKE_CASE_ )








import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__UpperCAmelCase      = logging.getLogger(__name__)
def 	UpperCamelCase			(     )			->  List[str]:
   UpperCamelCase					:						Union[str, Any]					=			ArgumentParser()
   UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			parser.parse_known_args()
   for arg in unknown:
      if arg.startswith(('-', '--')			):
         parser.add_argument(arg.split('='			)[0]			)

   return parser.parse_args()
def 	UpperCamelCase			(     )			->  Optional[Any]:
   UpperCamelCase					:						int					=			parse_args()
   UpperCamelCase					:						Dict					=			8888
   UpperCamelCase					:						Union[str, Any]					=			int(os.environ['SM_NUM_GPUS']			)
   UpperCamelCase					:						Optional[Any]					=			json.loads(os.environ['SM_HOSTS']			)
   UpperCamelCase					:						Dict					=			len(snake_case__			)
   UpperCamelCase					:						int					=			os.environ['SM_CURRENT_HOST']
   UpperCamelCase					:						List[str]					=			hosts.index(snake_case__			)
   UpperCamelCase					:						Optional[Any]					=			'INFO'

   if num_nodes > 1:
      UpperCamelCase					:						List[Any]					=			F"""python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
   else:
      UpperCamelCase					:						Any					=			F"""python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
   try:
      subprocess.run(snake_case__ ,  shell=snake_case__			)
   except Exception as e:
      logger.info(snake_case__			)


if __name__ == "__main__":
    main()



