


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__UpperCamelCase       :       Dict						       =						{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__UpperCamelCase       :       str						       =						{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__UpperCamelCase       :       Tuple						       =						{
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__UpperCamelCase       :       List[Any]						       =						{
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__UpperCamelCase       :       Dict						       =						{
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__UpperCamelCase       :       Optional[Any]						       =						{
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

def 			_a  (				SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



 if isinstance(snake_case__    , snake_case__     ):
  return v
 if v.lower() in ("yes", "true", "t", "y", "1"):
  return True
 elif v.lower() in ("no", "false", "f", "n", "0"):
  return False
 else:
  raise argparse.ArgumentTypeError('''boolean value expected'''     )

def 			_a  (				SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Union[str, Any]=False     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   checkpoint[F"{old_prefix}.in_layers.0.weight"]
 UpperCamelCase__					:    int          =   checkpoint[F"{old_prefix}.in_layers.0.bias"]
 UpperCamelCase__					:    int          =   checkpoint[F"{old_prefix}.in_layers.2.weight"]
 UpperCamelCase__					:    Tuple          =   checkpoint[F"{old_prefix}.in_layers.2.bias"]
 UpperCamelCase__					:    List[Any]          =   checkpoint[F"{old_prefix}.emb_layers.1.weight"]
 UpperCamelCase__					:    Any          =   checkpoint[F"{old_prefix}.emb_layers.1.bias"]
 UpperCamelCase__					:    str          =   checkpoint[F"{old_prefix}.out_layers.0.weight"]
 UpperCamelCase__					:    Any          =   checkpoint[F"{old_prefix}.out_layers.0.bias"]
 UpperCamelCase__					:    Dict          =   checkpoint[F"{old_prefix}.out_layers.3.weight"]
 UpperCamelCase__					:    Optional[Any]          =   checkpoint[F"{old_prefix}.out_layers.3.bias"]

 if has_skip:
  UpperCamelCase__					:    Dict          =   checkpoint[F"{old_prefix}.skip_connection.weight"]
  UpperCamelCase__					:    str          =   checkpoint[F"{old_prefix}.skip_connection.bias"]

 return new_checkpoint

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				Optional[Any]=None     ):







 """simple docstring"""



 UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__					:    List[str]          =   checkpoint[F"{old_prefix}.qkv.weight"].chunk(3    , dim=0     )
 UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__					:    Optional[Any]          =   checkpoint[F"{old_prefix}.qkv.bias"].chunk(3    , dim=0     )

 UpperCamelCase__					:    List[str]          =   checkpoint[F"{old_prefix}.norm.weight"]
 UpperCamelCase__					:    List[str]          =   checkpoint[F"{old_prefix}.norm.bias"]

 UpperCamelCase__					:    Any          =   weight_q.squeeze(-1     ).squeeze(-1     )
 UpperCamelCase__					:    Optional[Any]          =   bias_q.squeeze(-1     ).squeeze(-1     )
 UpperCamelCase__					:    Dict          =   weight_k.squeeze(-1     ).squeeze(-1     )
 UpperCamelCase__					:    Tuple          =   bias_k.squeeze(-1     ).squeeze(-1     )
 UpperCamelCase__					:    Any          =   weight_v.squeeze(-1     ).squeeze(-1     )
 UpperCamelCase__					:    Any          =   bias_v.squeeze(-1     ).squeeze(-1     )

 UpperCamelCase__					:    Optional[int]          =   (
     checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1     ).squeeze(-1     )
 )
 UpperCamelCase__					:    List[str]          =   checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1     ).squeeze(-1     )

 return new_checkpoint

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				Optional[int]     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   torch.load(snake_case__    , map_location='''cpu'''     )
 UpperCamelCase__					:    List[str]          =   {}

 UpperCamelCase__					:    str          =   checkpoint['''time_embed.0.weight''']
 UpperCamelCase__					:    Union[str, Any]          =   checkpoint['''time_embed.0.bias''']
 UpperCamelCase__					:    Union[str, Any]          =   checkpoint['''time_embed.2.weight''']
 UpperCamelCase__					:    Dict          =   checkpoint['''time_embed.2.bias''']

 if unet_config["num_class_embeds"] is not None:
  UpperCamelCase__					:    int          =   checkpoint['''label_emb.weight''']

 UpperCamelCase__					:    Dict          =   checkpoint['''input_blocks.0.0.weight''']
 UpperCamelCase__					:    str          =   checkpoint['''input_blocks.0.0.bias''']

 UpperCamelCase__					:    List[Any]          =   unet_config['''down_block_types''']
 UpperCamelCase__					:    int          =   unet_config['''layers_per_block''']
 UpperCamelCase__					:    Union[str, Any]          =   unet_config['''attention_head_dim''']
 UpperCamelCase__					:    Optional[int]          =   unet_config['''block_out_channels''']
 UpperCamelCase__					:    int          =   1
 UpperCamelCase__					:    str          =   channels_list[0]

 for i, layer_type in enumerate(snake_case__     ):
  UpperCamelCase__					:    Dict          =   channels_list[i]
  UpperCamelCase__					:    Optional[Any]          =   current_channels != prev_channels
  if layer_type == "ResnetDownsampleBlock2D":
   for j in range(snake_case__     ):
    UpperCamelCase__					:    Tuple          =   F"down_blocks.{i}.resnets.{j}"
    UpperCamelCase__					:    int          =   F"input_blocks.{current_layer}.0"
    UpperCamelCase__					:    List[str]          =   True if j == 0 and downsample_block_has_skip else False
    UpperCamelCase__					:    int          =   convert_resnet(snake_case__    , snake_case__    , snake_case__    , snake_case__    , has_skip=snake_case__     )
    current_layer += 1

  elif layer_type == "AttnDownBlock2D":
   for j in range(snake_case__     ):
    UpperCamelCase__					:    Optional[Any]          =   F"down_blocks.{i}.resnets.{j}"
    UpperCamelCase__					:    Tuple          =   F"input_blocks.{current_layer}.0"
    UpperCamelCase__					:    str          =   True if j == 0 and downsample_block_has_skip else False
    UpperCamelCase__					:    Tuple          =   convert_resnet(snake_case__    , snake_case__    , snake_case__    , snake_case__    , has_skip=snake_case__     )
    UpperCamelCase__					:    Optional[Any]          =   F"down_blocks.{i}.attentions.{j}"
    UpperCamelCase__					:    Tuple          =   F"input_blocks.{current_layer}.1"
    UpperCamelCase__					:    List[str]          =   convert_attention(
        snake_case__    , snake_case__    , snake_case__    , snake_case__    , snake_case__     )
    current_layer += 1

  if i != len(snake_case__     ) - 1:
   UpperCamelCase__					:    Optional[Any]          =   F"down_blocks.{i}.downsamplers.0"
   UpperCamelCase__					:    List[str]          =   F"input_blocks.{current_layer}.0"
   UpperCamelCase__					:    Optional[Any]          =   convert_resnet(snake_case__    , snake_case__    , snake_case__    , snake_case__     )
   current_layer += 1

  UpperCamelCase__					:    str          =   current_channels

 # hardcoded the mid-block for now
 UpperCamelCase__					:    List[str]          =   '''mid_block.resnets.0'''
 UpperCamelCase__					:    Tuple          =   '''middle_block.0'''
 UpperCamelCase__					:    List[Any]          =   convert_resnet(snake_case__    , snake_case__    , snake_case__    , snake_case__     )
 UpperCamelCase__					:    Optional[int]          =   '''mid_block.attentions.0'''
 UpperCamelCase__					:    str          =   '''middle_block.1'''
 UpperCamelCase__					:    str          =   convert_attention(snake_case__    , snake_case__    , snake_case__    , snake_case__    , snake_case__     )
 UpperCamelCase__					:    Dict          =   '''mid_block.resnets.1'''
 UpperCamelCase__					:    Dict          =   '''middle_block.2'''
 UpperCamelCase__					:    Optional[Any]          =   convert_resnet(snake_case__    , snake_case__    , snake_case__    , snake_case__     )

 UpperCamelCase__					:    List[str]          =   0
 UpperCamelCase__					:    Optional[int]          =   unet_config['''up_block_types''']

 for i, layer_type in enumerate(snake_case__     ):
  if layer_type == "ResnetUpsampleBlock2D":
   for j in range(layers_per_block + 1     ):
    UpperCamelCase__					:    int          =   F"up_blocks.{i}.resnets.{j}"
    UpperCamelCase__					:    Tuple          =   F"output_blocks.{current_layer}.0"
    UpperCamelCase__					:    Any          =   convert_resnet(snake_case__    , snake_case__    , snake_case__    , snake_case__    , has_skip=snake_case__     )
    current_layer += 1

   if i != len(snake_case__     ) - 1:
    UpperCamelCase__					:    int          =   F"up_blocks.{i}.upsamplers.0"
    UpperCamelCase__					:    Any          =   F"output_blocks.{current_layer-1}.1"
    UpperCamelCase__					:    Tuple          =   convert_resnet(snake_case__    , snake_case__    , snake_case__    , snake_case__     )
  elif layer_type == "AttnUpBlock2D":
   for j in range(layers_per_block + 1     ):
    UpperCamelCase__					:    Tuple          =   F"up_blocks.{i}.resnets.{j}"
    UpperCamelCase__					:    Dict          =   F"output_blocks.{current_layer}.0"
    UpperCamelCase__					:    Optional[Any]          =   convert_resnet(snake_case__    , snake_case__    , snake_case__    , snake_case__    , has_skip=snake_case__     )
    UpperCamelCase__					:    Dict          =   F"up_blocks.{i}.attentions.{j}"
    UpperCamelCase__					:    Optional[Any]          =   F"output_blocks.{current_layer}.1"
    UpperCamelCase__					:    List[Any]          =   convert_attention(
        snake_case__    , snake_case__    , snake_case__    , snake_case__    , snake_case__     )
    current_layer += 1

   if i != len(snake_case__     ) - 1:
    UpperCamelCase__					:    Optional[int]          =   F"up_blocks.{i}.upsamplers.0"
    UpperCamelCase__					:    List[str]          =   F"output_blocks.{current_layer-1}.2"
    UpperCamelCase__					:    Optional[int]          =   convert_resnet(snake_case__    , snake_case__    , snake_case__    , snake_case__     )

 UpperCamelCase__					:    str          =   checkpoint['''out.0.weight''']
 UpperCamelCase__					:    List[Any]          =   checkpoint['''out.0.bias''']
 UpperCamelCase__					:    List[Any]          =   checkpoint['''out.2.weight''']
 UpperCamelCase__					:    Optional[Any]          =   checkpoint['''out.2.bias''']

 return new_checkpoint


if __name__ == "__main__":
       __UpperCamelCase       :       str						       =						argparse.ArgumentParser()

       parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
       parser.add_argument(
           "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
       )
       parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

       __UpperCamelCase       :       int						       =						parser.parse_args()
       __UpperCamelCase       :       Optional[Any]						       =						strabool(args.class_cond)

       __UpperCamelCase       :       Optional[Any]						       =						os.path.basename(args.unet_path)
       print(f"Checkpoint: {ckpt_name}")

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              __UpperCamelCase       :       List[Any]						       =						IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __UpperCamelCase       :       List[Any]						       =						LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              __UpperCamelCase       :       List[str]						       =						TEST_UNET_CONFIG
       else:
              raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

       if not args.class_cond:
              __UpperCamelCase       :       Tuple						       =						None

       __UpperCamelCase       :       List[str]						       =						con_pt_to_diffuser(args.unet_path, unet_config)

       __UpperCamelCase       :       Union[str, Any]						       =						UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              __UpperCamelCase       :       List[Any]						       =						CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              __UpperCamelCase       :       Optional[Any]						       =						CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __UpperCamelCase       :       str						       =						CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

       __UpperCamelCase       :       Tuple						       =						CMStochasticIterativeScheduler(**scheduler_config)

       __UpperCamelCase       :       Optional[int]						       =						ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      "roberta-prelayernorm"


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     List[Any]=50265							,	lowerCamelCase__							:     Optional[Any]=768							,	lowerCamelCase__							:     str=12							,	lowerCamelCase__							:     Union[str, Any]=12							,	lowerCamelCase__							:     Dict=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     List[str]=512							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Tuple=0.02							,	lowerCamelCase__							:     List[Any]=1E-1_2							,	lowerCamelCase__							:     str=1							,	lowerCamelCase__							:     int=0							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Union[str, Any]="absolute"							,	lowerCamelCase__							:     Optional[Any]=True							,	lowerCamelCase__							:     Dict=None							,	**lowerCamelCase__							:     Any							,	)  ->      Optional[int]:





      '''simple docstring'''






      super().__init__(pad_token_id=lowerCamelCase__							,	bos_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	**lowerCamelCase__			)

      UpperCamelCase__					:    Optional[int]          =   vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   hidden_size
      UpperCamelCase__					:    List[str]          =   num_hidden_layers
      UpperCamelCase__					:    Optional[int]          =   num_attention_heads
      UpperCamelCase__					:    List[str]          =   hidden_act
      UpperCamelCase__					:    Optional[int]          =   intermediate_size
      UpperCamelCase__					:    Optional[int]          =   hidden_dropout_prob
      UpperCamelCase__					:    List[str]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Optional[int]          =   max_position_embeddings
      UpperCamelCase__					:    Optional[Any]          =   type_vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   initializer_range
      UpperCamelCase__					:    Dict          =   layer_norm_eps
      UpperCamelCase__					:    Union[str, Any]          =   position_embedding_type
      UpperCamelCase__					:    Optional[int]          =   use_cache
      UpperCamelCase__					:    int          =   classifier_dropout



class       __magic_name__	(     __lowerCAmelCase):


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





      '''simple docstring'''






      if self.task == "multiple-choice":
       UpperCamelCase__					:    Dict          =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       UpperCamelCase__					:    Any          =   {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]			)


