





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__A							:			Union[str, Any]         =			{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "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",
}

__A							:			Tuple         =			{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "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",
}

__A							:			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",
}

__A							:			int         =			{
    "num_train_timesteps": 40,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}

__A							:			Dict         =			{
    "num_train_timesteps": 201,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}

__A							:			Dict         =			{
    "num_train_timesteps": 151,
    "sigma_min": 0.0_0_2,
    "sigma_max": 8_0.0,
}





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """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				)							-> Optional[Any]:




    """simple docstring"""




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

    if has_skip:
        _A     =							checkpoint[F"{old_prefix}.skip_connection.weight"]
        _A     =							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				)							-> Any:




    """simple docstring"""




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

    _A     =							checkpoint[F"{old_prefix}.norm.weight"]
    _A     =							checkpoint[F"{old_prefix}.norm.bias"]

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

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

    return new_checkpoint





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							torch.load(_SCREAMING_SNAKE_CASE							,     map_location='cpu'				)
    _A     =							{}

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

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

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

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

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

        _A     =							current_channels

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

    _A     =							0
    _A     =							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				):
                _A     =							F"up_blocks.{i}.resnets.{j}"
                _A     =							F"output_blocks.{current_layer}.0"
                _A     =							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:
                _A     =							F"up_blocks.{i}.upsamplers.0"
                _A     =							F"output_blocks.{current_layer-1}.1"
                _A     =							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				):
                _A     =							F"up_blocks.{i}.resnets.{j}"
                _A     =							F"output_blocks.{current_layer}.0"
                _A     =							convert_resnet(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     has_skip=_SCREAMING_SNAKE_CASE				)
                _A     =							F"up_blocks.{i}.attentions.{j}"
                _A     =							F"output_blocks.{current_layer}.1"
                _A     =							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:
                _A     =							F"up_blocks.{i}.upsamplers.0"
                _A     =							F"output_blocks.{current_layer-1}.2"
                _A     =							convert_resnet(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

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

    return new_checkpoint


if __name__ == "__main__":
 __A							:			Optional[int]         =			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.")

 __A							:			Optional[Any]         =			parser.parse_args()
 __A							:			List[str]         =			strabool(args.class_cond)

 __A							:			List[str]         =			os.path.basename(args.unet_path)
 print(f"Checkpoint: {ckpt_name}")

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

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

 __A							:			str         =			con_pt_to_diffuser(args.unet_path, unet_config)

 __A							:			Tuple         =			UNetaDModel(**unet_config)
 image_unet.load_state_dict(converted_unet_ckpt)

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

 __A							:			Dict         =			CMStochasticIterativeScheduler(**scheduler_config)

 __A							:			List[Any]         =			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)












__A							:			Dict         =			"Alexander Joslin"

import operator as op

from .stack import Stack





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    _A     =							Stack()
    _A     =							Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(_SCREAMING_SNAKE_CASE				)				)
        elif i in operators:
            # RULE 2
            operator_stack.push(_SCREAMING_SNAKE_CASE				)
        elif i == ")":
            # RULE 4
            _A     =							operator_stack.peek()
            operator_stack.pop()
            _A     =							operand_stack.peek()
            operand_stack.pop()
            _A     =							operand_stack.peek()
            operand_stack.pop()

            _A     =							operators[opr](_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
            operand_stack.push(_SCREAMING_SNAKE_CASE				)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
 __A							:			Any         =			"(5 + ((4 * 2) * (2 + 3)))"
 # answer = 45
 print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")





