





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						{
    """sample_size""": 3_2,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [3_2, 6_4],
    """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""",
}

SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						{
    """sample_size""": 6_4,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    """attention_head_dim""": 6_4,
    """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""",
}

SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						{
    """sample_size""": 2_5_6,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    """attention_head_dim""": 6_4,
    """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""",
}

SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						{
    """num_train_timesteps""": 4_0,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """num_train_timesteps""": 2_0_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

SCREAMING_SNAKE_CASE__       :			Dict			      =						{
    """num_train_timesteps""": 1_5_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}






def       _A  (      lowerCamelCase   ):
   if isinstance(lowerCamelCase							, lowerCamelCase   ):
      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  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=False   ):
   a__	:						Tuple      		=  checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
   a__	:						int      		=  checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
   a__	:						str      		=  checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
   a__	:						Dict      		=  checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
   a__	:						Optional[int]      		=  checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
   a__	:						List[str]      		=  checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
   a__	:						str      		=  checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
   a__	:						Tuple      		=  checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
   a__	:						Any      		=  checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
   a__	:						Union[str, Any]      		=  checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

   if has_skip:
      a__	:						Union[str, Any]      		=  checkpoint[F"""{old_prefix}.skip_connection.weight"""]
      a__	:						Optional[int]      		=  checkpoint[F"""{old_prefix}.skip_connection.bias"""]

   return new_checkpoint






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None   ):
   a__		,     a__		,     a__	:						int      		=  checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3							, dim=0   )
   a__		,     a__		,     a__	:						int      		=  checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3							, dim=0   )

   a__	:						Union[str, Any]      		=  checkpoint[F"""{old_prefix}.norm.weight"""]
   a__	:						List[Any]      		=  checkpoint[F"""{old_prefix}.norm.bias"""]

   a__	:						Any      		=  weight_q.squeeze(-1   ).squeeze(-1   )
   a__	:						Optional[int]      		=  bias_q.squeeze(-1   ).squeeze(-1   )
   a__	:						Any      		=  weight_k.squeeze(-1   ).squeeze(-1   )
   a__	:						List[str]      		=  bias_k.squeeze(-1   ).squeeze(-1   )
   a__	:						str      		=  weight_v.squeeze(-1   ).squeeze(-1   )
   a__	:						int      		=  bias_v.squeeze(-1   ).squeeze(-1   )

   a__	:						Dict      		=  (
       checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1   ).squeeze(-1   )
   )
   a__	:						Union[str, Any]      		=  checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1   ).squeeze(-1   )

   return new_checkpoint






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Optional[Any]      		=  torch.load(lowerCamelCase							, map_location="cpu"   )
   a__	:						Any      		=  {}

   a__	:						int      		=  checkpoint["time_embed.0.weight"]
   a__	:						Tuple      		=  checkpoint["time_embed.0.bias"]
   a__	:						Optional[int]      		=  checkpoint["time_embed.2.weight"]
   a__	:						List[Any]      		=  checkpoint["time_embed.2.bias"]

   if unet_config["num_class_embeds"] is not None:
      a__	:						List[Any]      		=  checkpoint["label_emb.weight"]

   a__	:						Tuple      		=  checkpoint["input_blocks.0.0.weight"]
   a__	:						Any      		=  checkpoint["input_blocks.0.0.bias"]

   a__	:						Dict      		=  unet_config["down_block_types"]
   a__	:						Dict      		=  unet_config["layers_per_block"]
   a__	:						Any      		=  unet_config["attention_head_dim"]
   a__	:						Tuple      		=  unet_config["block_out_channels"]
   a__	:						Dict      		=  1
   a__	:						Tuple      		=  channels_list[0]

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

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

      if i != len(lowerCamelCase   ) - 1:
         a__	:						Dict      		=  F"""down_blocks.{i}.downsamplers.0"""
         a__	:						int      		=  F"""input_blocks.{current_layer}.0"""
         a__	:						int      		=  convert_resnet(lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   )
         current_layer += 1

      a__	:						Optional[Any]      		=  current_channels

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

   a__	:						List[str]      		=  0
   a__	:						List[Any]      		=  unet_config["up_block_types"]

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

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

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

   a__	:						int      		=  checkpoint["out.0.weight"]
   a__	:						List[str]      		=  checkpoint["out.0.bias"]
   a__	:						Optional[Any]      		=  checkpoint["out.2.weight"]
   a__	:						Tuple      		=  checkpoint["out.2.bias"]

   return new_checkpoint


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			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.""")

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						parser.parse_args()
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						strabool(args.class_cond)

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						os.path.basename(args.unet_path)
  print(f'Checkpoint: {ckpt_name}')

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

  if not args.class_cond:
    SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						None

  SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						con_pt_to_diffuser(args.unet_path, unet_config)

  SCREAMING_SNAKE_CASE__       :			Dict			      =						UNetaDModel(**unet_config)
  image_unet.load_state_dict(converted_unet_ckpt)

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    SCREAMING_SNAKE_CASE__       :			int			      =						CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    SCREAMING_SNAKE_CASE__       :			Tuple			      =						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)):
    SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

  SCREAMING_SNAKE_CASE__       :			Dict			      =						CMStochasticIterativeScheduler(**scheduler_config)

  SCREAMING_SNAKE_CASE__       :			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 packaging import version

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


SCREAMING_SNAKE_CASE__       :			Dict			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[int]     		=				"""mobilenet_v2"""





   def __init__( self					,			snake_case=3					,			snake_case=224					,			snake_case=1.0					,			snake_case=8					,			snake_case=8					,			snake_case=6					,			snake_case=32					,			snake_case=True					,			snake_case=True					,			snake_case="relu6"					,			snake_case=True					,			snake_case=0.8					,			snake_case=0.02					,			snake_case=0.001					,			snake_case=255					,			**snake_case					,			)				->  int:
      """simple docstring"""





      super().__init__(**snake_case      )

      if depth_multiplier <= 0:
         raise ValueError("depth_multiplier must be greater than zero."      )

      a__	:						str      		=  num_channels
      a__	:						Dict      		=  image_size
      a__	:						Any      		=  depth_multiplier
      a__	:						str      		=  depth_divisible_by
      a__	:						Optional[int]      		=  min_depth
      a__	:						Dict      		=  expand_ratio
      a__	:						str      		=  output_stride
      a__	:						Optional[int]      		=  first_layer_is_expansion
      a__	:						Union[str, Any]      		=  finegrained_output
      a__	:						Union[str, Any]      		=  hidden_act
      a__	:						str      		=  tf_padding
      a__	:						List[Any]      		=  classifier_dropout_prob
      a__	:						List[Any]      		=  initializer_range
      a__	:						Optional[Any]      		=  layer_norm_eps
      a__	:						str      		=  semantic_loss_ignore_index







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Any     		=				version.parse("""1.11"""  )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      return OrderedDict([("pixel_values", {0: "batch"})]      )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      if self.task == "image-classification":
         return OrderedDict([("logits", {0: "batch"})]      )
      else:
         return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]      )





   @property
   def 	_snake_case ( self      )				->  float:
      """simple docstring"""





      return 1E-4

