


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   {
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 10_00,
    "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",
}

_SCREAMING_SNAKE_CASE      :			int      				=   {
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 10_00,
    "block_out_channels": [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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",
}

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "sample_size": 2_56,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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",
}

_SCREAMING_SNAKE_CASE      :			Dict      				=   {
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_SCREAMING_SNAKE_CASE      :			Any      				=   {
    "num_train_timesteps": 2_01,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_SCREAMING_SNAKE_CASE      :			List[Any]      				=   {
    "num_train_timesteps": 1_51,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     if isinstance(UpperCamelCase_							,UpperCamelCase_    ):
          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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






     snake_case					=    checkpoint[F'''{old_prefix}.in_layers.0.weight''']
     snake_case					=    checkpoint[F'''{old_prefix}.in_layers.0.bias''']
     snake_case					=    checkpoint[F'''{old_prefix}.in_layers.2.weight''']
     snake_case					=    checkpoint[F'''{old_prefix}.in_layers.2.bias''']
     snake_case					=    checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
     snake_case					=    checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
     snake_case					=    checkpoint[F'''{old_prefix}.out_layers.0.weight''']
     snake_case					=    checkpoint[F'''{old_prefix}.out_layers.0.bias''']
     snake_case					=    checkpoint[F'''{old_prefix}.out_layers.3.weight''']
     snake_case					=    checkpoint[F'''{old_prefix}.out_layers.3.bias''']

     if has_skip:
          snake_case					=    checkpoint[F'''{old_prefix}.skip_connection.weight''']
          snake_case					=    checkpoint[F'''{old_prefix}.skip_connection.bias''']

     return new_checkpoint
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None    ):



     """simple docstring"""






     snake_case			,  snake_case			,  snake_case					=    checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3							,dim=0    )
     snake_case			,  snake_case			,  snake_case					=    checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3							,dim=0    )

     snake_case					=    checkpoint[F'''{old_prefix}.norm.weight''']
     snake_case					=    checkpoint[F'''{old_prefix}.norm.bias''']

     snake_case					=    weight_q.squeeze(-1    ).squeeze(-1    )
     snake_case					=    bias_q.squeeze(-1    ).squeeze(-1    )
     snake_case					=    weight_k.squeeze(-1    ).squeeze(-1    )
     snake_case					=    bias_k.squeeze(-1    ).squeeze(-1    )
     snake_case					=    weight_v.squeeze(-1    ).squeeze(-1    )
     snake_case					=    bias_v.squeeze(-1    ).squeeze(-1    )

     snake_case					=    (
         checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1    ).squeeze(-1    )
     )
     snake_case					=    checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1    ).squeeze(-1    )

     return new_checkpoint
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    torch.load(UpperCamelCase_							,map_location='''cpu'''    )
     snake_case					=    {}

     snake_case					=    checkpoint['''time_embed.0.weight''']
     snake_case					=    checkpoint['''time_embed.0.bias''']
     snake_case					=    checkpoint['''time_embed.2.weight''']
     snake_case					=    checkpoint['''time_embed.2.bias''']

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

     snake_case					=    checkpoint['''input_blocks.0.0.weight''']
     snake_case					=    checkpoint['''input_blocks.0.0.bias''']

     snake_case					=    unet_config['''down_block_types''']
     snake_case					=    unet_config['''layers_per_block''']
     snake_case					=    unet_config['''attention_head_dim''']
     snake_case					=    unet_config['''block_out_channels''']
     snake_case					=    1
     snake_case					=    channels_list[0]

     for i, layer_type in enumerate(UpperCamelCase_    ):
          snake_case					=    channels_list[i]
          snake_case					=    current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(UpperCamelCase_    ):
                    snake_case					=    F'''down_blocks.{i}.resnets.{j}'''
                    snake_case					=    F'''input_blocks.{current_layer}.0'''
                    snake_case					=    True if j == 0 and downsample_block_has_skip else False
                    snake_case					=    convert_resnet(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,has_skip=UpperCamelCase_    )
                    current_layer += 1

          elif layer_type == "AttnDownBlock2D":
               for j in range(UpperCamelCase_    ):
                    snake_case					=    F'''down_blocks.{i}.resnets.{j}'''
                    snake_case					=    F'''input_blocks.{current_layer}.0'''
                    snake_case					=    True if j == 0 and downsample_block_has_skip else False
                    snake_case					=    convert_resnet(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,has_skip=UpperCamelCase_    )
                    snake_case					=    F'''down_blocks.{i}.attentions.{j}'''
                    snake_case					=    F'''input_blocks.{current_layer}.1'''
                    snake_case					=    convert_attention(
                        UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
                    current_layer += 1

          if i != len(UpperCamelCase_    ) - 1:
               snake_case					=    F'''down_blocks.{i}.downsamplers.0'''
               snake_case					=    F'''input_blocks.{current_layer}.0'''
               snake_case					=    convert_resnet(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
               current_layer += 1

          snake_case					=    current_channels

     # hardcoded the mid-block for now
     snake_case					=    '''mid_block.resnets.0'''
     snake_case					=    '''middle_block.0'''
     snake_case					=    convert_resnet(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
     snake_case					=    '''mid_block.attentions.0'''
     snake_case					=    '''middle_block.1'''
     snake_case					=    convert_attention(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
     snake_case					=    '''mid_block.resnets.1'''
     snake_case					=    '''middle_block.2'''
     snake_case					=    convert_resnet(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

     snake_case					=    0
     snake_case					=    unet_config['''up_block_types''']

     for i, layer_type in enumerate(UpperCamelCase_    ):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1    ):
                    snake_case					=    F'''up_blocks.{i}.resnets.{j}'''
                    snake_case					=    F'''output_blocks.{current_layer}.0'''
                    snake_case					=    convert_resnet(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,has_skip=UpperCamelCase_    )
                    current_layer += 1

               if i != len(UpperCamelCase_    ) - 1:
                    snake_case					=    F'''up_blocks.{i}.upsamplers.0'''
                    snake_case					=    F'''output_blocks.{current_layer-1}.1'''
                    snake_case					=    convert_resnet(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1    ):
                    snake_case					=    F'''up_blocks.{i}.resnets.{j}'''
                    snake_case					=    F'''output_blocks.{current_layer}.0'''
                    snake_case					=    convert_resnet(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,has_skip=UpperCamelCase_    )
                    snake_case					=    F'''up_blocks.{i}.attentions.{j}'''
                    snake_case					=    F'''output_blocks.{current_layer}.1'''
                    snake_case					=    convert_attention(
                        UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
                    current_layer += 1

               if i != len(UpperCamelCase_    ) - 1:
                    snake_case					=    F'''up_blocks.{i}.upsamplers.0'''
                    snake_case					=    F'''output_blocks.{current_layer-1}.2'''
                    snake_case					=    convert_resnet(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

     snake_case					=    checkpoint['''out.0.weight''']
     snake_case					=    checkpoint['''out.0.bias''']
     snake_case					=    checkpoint['''out.2.weight''']
     snake_case					=    checkpoint['''out.2.bias''']

     return new_checkpoint


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   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      :			Dict      				=   parser.parse_args()
 _SCREAMING_SNAKE_CASE      :			str      				=   strabool(args.class_cond)

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

 # Get U-Net config
 if "imagenet64" in ckpt_name:
  _SCREAMING_SNAKE_CASE      :			List[str]      				=   IMAGENET_64_UNET_CONFIG
 elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  _SCREAMING_SNAKE_CASE      :			Dict      				=   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      :			Tuple      				=   con_pt_to_diffuser(args.unet_path, unet_config)

 _SCREAMING_SNAKE_CASE      :			int      				=   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      :			Any      				=   CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  _SCREAMING_SNAKE_CASE      :			str      				=   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      :			Optional[Any]      				=   CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

 _SCREAMING_SNAKE_CASE      :			int      				=   CMStochasticIterativeScheduler(**scheduler_config)

 _SCREAMING_SNAKE_CASE      :			str      				=   ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)








# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(UpperCamelCase_    )

def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_terminal_summary_main

     snake_case					=    terminalreporter.config.getoption('''--make-reports'''    )
     if make_reports:
          pytest_terminal_summary_main(UpperCamelCase_							,id=UpperCamelCase_    )




