

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": 0.002,
    "sigma_max": 80.0,
}

lowercase_			=  {
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

lowercase_			=  {
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]=False				)							->				int:





    '''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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     Dict=None				)							->				int:





    '''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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Union[str, Any]:





    '''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__":
     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)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square(SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int				) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        A__   				=       update_area_of_max_square(SCREAMING_SNAKE_CASE__   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    update_area_of_max_square(0   ,      0				)
    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square_using_dp_array(
        SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        A__   				=       update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    A__   				=       [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__				)]
    update_area_of_max_square_using_dp_array(0   ,      0   ,      SCREAMING_SNAKE_CASE__				)

    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [[0] * (cols + 1) for _ in range(rows + 1				)]
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       dp_array[row][col + 1]
            A__   				=       dp_array[row + 1][col + 1]
            A__   				=       dp_array[row + 1][col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(dp_array[row][col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0

    return largest_square_area
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [0] * (cols + 1)
    A__   				=       [0] * (cols + 1)
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       current_row[col + 1]
            A__   				=       next_row[col + 1]
            A__   				=       next_row[col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(current_row[col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0
        A__   				=       current_row

    return largest_square_area


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

