import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowercase										= {
    '''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''',
}

__lowercase										= {
    '''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''',
}

__lowercase										= {
    '''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''',
}

__lowercase										= {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

__lowercase										= {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

__lowercase										= {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if isinstance(SCREAMING_SNAKE_CASE  ,      SCREAMING_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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=False     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
    __UpperCamelCase					:int        =		checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
    __UpperCamelCase					:Optional[Any]        =		checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
    __UpperCamelCase					:List[str]        =		checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
    __UpperCamelCase					:Optional[Any]        =		checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
    __UpperCamelCase					:Tuple        =		checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
    __UpperCamelCase					:Union[str, Any]        =		checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
    __UpperCamelCase					:Any        =		checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
    __UpperCamelCase					:Union[str, Any]        =		checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
    __UpperCamelCase					:Any        =		checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

    if has_skip:
        __UpperCamelCase					:List[str]        =		checkpoint[f"""{old_prefix}.skip_connection.weight"""]
        __UpperCamelCase					:Tuple        =		checkpoint[f"""{old_prefix}.skip_connection.bias"""]

    return new_checkpoint



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=None     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3  ,      dim=0     )
    __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3  ,      dim=0     )

    __UpperCamelCase					:Any        =		checkpoint[f"""{old_prefix}.norm.weight"""]
    __UpperCamelCase					:Optional[int]        =		checkpoint[f"""{old_prefix}.norm.bias"""]

    __UpperCamelCase					:Tuple        =		weight_q.squeeze(-1     ).squeeze(-1     )
    __UpperCamelCase					:Optional[int]        =		bias_q.squeeze(-1     ).squeeze(-1     )
    __UpperCamelCase					:Tuple        =		weight_k.squeeze(-1     ).squeeze(-1     )
    __UpperCamelCase					:Union[str, Any]        =		bias_k.squeeze(-1     ).squeeze(-1     )
    __UpperCamelCase					:Any        =		weight_v.squeeze(-1     ).squeeze(-1     )
    __UpperCamelCase					:Union[str, Any]        =		bias_v.squeeze(-1     ).squeeze(-1     )

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

    return new_checkpoint



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Optional[int]        =		{}

    __UpperCamelCase					:Union[str, Any]        =		checkpoint['''time_embed.0.weight''']
    __UpperCamelCase					:int        =		checkpoint['''time_embed.0.bias''']
    __UpperCamelCase					:Any        =		checkpoint['''time_embed.2.weight''']
    __UpperCamelCase					:Tuple        =		checkpoint['''time_embed.2.bias''']

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

    __UpperCamelCase					:Tuple        =		checkpoint['''input_blocks.0.0.weight''']
    __UpperCamelCase					:Optional[Any]        =		checkpoint['''input_blocks.0.0.bias''']

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

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

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

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

        __UpperCamelCase					:Optional[int]        =		current_channels

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

    __UpperCamelCase					:Optional[Any]        =		0
    __UpperCamelCase					:Optional[int]        =		unet_config['''up_block_types''']

    for i, layer_type in enumerate(SCREAMING_SNAKE_CASE     ):
        if layer_type == "ResnetUpsampleBlock2D":
            for j in range(layers_per_block + 1     ):
                __UpperCamelCase					:int        =		f"""up_blocks.{i}.resnets.{j}"""
                __UpperCamelCase					:List[Any]        =		f"""output_blocks.{current_layer}.0"""
                __UpperCamelCase					:Dict        =		convert_resnet(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      has_skip=SCREAMING_SNAKE_CASE     )
                current_layer += 1

            if i != len(SCREAMING_SNAKE_CASE     ) - 1:
                __UpperCamelCase					:str        =		f"""up_blocks.{i}.upsamplers.0"""
                __UpperCamelCase					:Union[str, Any]        =		f"""output_blocks.{current_layer-1}.1"""
                __UpperCamelCase					:Union[str, Any]        =		convert_resnet(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
        elif layer_type == "AttnUpBlock2D":
            for j in range(layers_per_block + 1     ):
                __UpperCamelCase					:Union[str, Any]        =		f"""up_blocks.{i}.resnets.{j}"""
                __UpperCamelCase					:Union[str, Any]        =		f"""output_blocks.{current_layer}.0"""
                __UpperCamelCase					:int        =		convert_resnet(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      has_skip=SCREAMING_SNAKE_CASE     )
                __UpperCamelCase					:Any        =		f"""up_blocks.{i}.attentions.{j}"""
                __UpperCamelCase					:int        =		f"""output_blocks.{current_layer}.1"""
                __UpperCamelCase					:List[Any]        =		convert_attention(
                    SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
                current_layer += 1

            if i != len(SCREAMING_SNAKE_CASE     ) - 1:
                __UpperCamelCase					:str        =		f"""up_blocks.{i}.upsamplers.0"""
                __UpperCamelCase					:Dict        =		f"""output_blocks.{current_layer-1}.2"""
                __UpperCamelCase					:Optional[int]        =		convert_resnet(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

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

    return new_checkpoint


if __name__ == "__main__":
    __lowercase										= 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.''')

    __lowercase										= parser.parse_args()
    __lowercase										= strabool(args.class_cond)

    __lowercase										= os.path.basename(args.unet_path)
    print(F'Checkpoint: {ckpt_name}')

    # Get U-Net config
    if "imagenet64" in ckpt_name:
        __lowercase										= IMAGENET_64_UNET_CONFIG
    elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
        __lowercase										= LSUN_256_UNET_CONFIG
    elif "test" in ckpt_name:
        __lowercase										= TEST_UNET_CONFIG
    else:
        raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

    if not args.class_cond:
        __lowercase										= None

    __lowercase										= con_pt_to_diffuser(args.unet_path, unet_config)

    __lowercase										= UNetaDModel(**unet_config)
    image_unet.load_state_dict(converted_unet_ckpt)

    # Get scheduler config
    if "cd" in ckpt_name or "test" in ckpt_name:
        __lowercase										= CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        __lowercase										= 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)):
        __lowercase										= CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

    __lowercase										= CMStochasticIterativeScheduler(**scheduler_config)

    __lowercase										= ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
    consistency_model.save_pretrained(args.dump_path)






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if (inductance, frequency, reactance).count(0     ) != 1:
        raise ValueError('''One and only one argument must be 0'''     )
    if inductance < 0:
        raise ValueError('''Inductance cannot be negative'''     )
    if frequency < 0:
        raise ValueError('''Frequency cannot be negative'''     )
    if reactance < 0:
        raise ValueError('''Inductive reactance cannot be negative'''     )
    if inductance == 0:
        return {"inductance": reactance / (2 * pi * frequency)}
    elif frequency == 0:
        return {"frequency": reactance / (2 * pi * inductance)}
    elif reactance == 0:
        return {"reactance": 2 * pi * frequency * inductance}
    else:
        raise ValueError('''Exactly one argument must be 0'''     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





