
"""simple docstring"""




import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel



def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		Any=1    )    ->	List[Any]:



    if n_shave_prefix_segments >= 0:
        return ".".join(path.split('.'    )[n_shave_prefix_segments:]    )
    else:
        return ".".join(path.split('.'    )[:n_shave_prefix_segments]    )



def 						__magic_name__		(				UpperCamelCase    :		Dict       ,		UpperCamelCase    :		Dict=0    )    ->	List[str]:
    a__    = []
    for old_item in old_list:
        a__    = old_item
        a__    = new_item.replace('block.'       ,		'resnets.'    )
        a__    = new_item.replace('conv_shorcut'       ,		'conv1'    )
        a__    = new_item.replace('in_shortcut'       ,		'conv_shortcut'    )
        a__    = new_item.replace('temb_proj'       ,		'time_emb_proj'    )

        a__    = shave_segments(__A       ,		n_shave_prefix_segments=__A    )

        mapping.append({'old': old_item, 'new': new_item}    )

    return mapping



def 						__magic_name__		(				UpperCamelCase    :		Dict       ,		UpperCamelCase    :		Optional[int]=0       ,		UpperCamelCase    :		Dict=False    )    ->	int:
    a__    = []
    for old_item in old_list:
        a__    = old_item

        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
            a__    = new_item.replace('attn'       ,		'attentions'    )
        a__    = new_item.replace('.k.'       ,		'.key.'    )
        a__    = new_item.replace('.v.'       ,		'.value.'    )
        a__    = new_item.replace('.q.'       ,		'.query.'    )

        a__    = new_item.replace('proj_out'       ,		'proj_attn'    )
        a__    = new_item.replace('norm'       ,		'group_norm'    )

        a__    = shave_segments(__A       ,		n_shave_prefix_segments=__A    )
        mapping.append({'old': old_item, 'new': new_item}    )

    return mapping



def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		List[str]       ,		UpperCamelCase    :		Any       ,		UpperCamelCase    :		Optional[int]=None       ,		UpperCamelCase    :		Optional[Any]=None       ,		UpperCamelCase    :		Union[str, Any]=None    )    ->	List[Any]:
    assert isinstance(__A       ,		__A    ), "Paths should be a list of dicts containing 'old' and 'new' keys."

    if attention_paths_to_split is not None:
        if config is None:
            raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'    )

        for path, path_map in attention_paths_to_split.items():
            a__    = old_checkpoint[path]
            a__    = old_tensor.shape[0] // 3

            a__    = (-1, channels) if len(old_tensor.shape    ) == 3 else (-1)

            a__    = old_tensor.shape[0] // config.get('num_head_channels'       ,		1    ) // 3

            a__    = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
            a__  ,    a__  ,    a__    = old_tensor.split(channels // num_heads       ,		dim=1    )

            a__    = query.reshape(__A    ).squeeze()
            a__    = key.reshape(__A    ).squeeze()
            a__    = value.reshape(__A    ).squeeze()

    for path in paths:
        a__    = path['new']

        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        a__    = new_path.replace('down.'       ,		'down_blocks.'    )
        a__    = new_path.replace('up.'       ,		'up_blocks.'    )

        if additional_replacements is not None:
            for replacement in additional_replacements:
                a__    = new_path.replace(replacement['old']       ,		replacement['new']    )

        if "attentions" in new_path:
            a__    = old_checkpoint[path['old']].squeeze()
        else:
            a__    = old_checkpoint[path['old']]



def 						__magic_name__		(				UpperCamelCase    :		Union[str, Any]       ,		UpperCamelCase    :		Tuple    )    ->	Optional[int]:
    a__    = {}

    a__    = checkpoint['temb.dense.0.weight']
    a__    = checkpoint['temb.dense.0.bias']
    a__    = checkpoint['temb.dense.1.weight']
    a__    = checkpoint['temb.dense.1.bias']

    a__    = checkpoint['norm_out.weight']
    a__    = checkpoint['norm_out.bias']

    a__    = checkpoint['conv_in.weight']
    a__    = checkpoint['conv_in.bias']
    a__    = checkpoint['conv_out.weight']
    a__    = checkpoint['conv_out.bias']

    a__    = len({'.'.join(layer.split('.'    )[:2]    ) for layer in checkpoint if 'down' in layer}    )
    a__    = {
        layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(__A    )
    }

    a__    = len({'.'.join(layer.split('.'    )[:2]    ) for layer in checkpoint if 'up' in layer}    )
    a__    = {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(__A    )}

    for i in range(__A    ):
        a__    = (i - 1) // (config['layers_per_block'] + 1)

        if any('downsample' in layer for layer in down_blocks[i]    ):
            a__    = checkpoint[
                f'down.{i}.downsample.op.weight'
            ]
            a__    = checkpoint[f'down.{i}.downsample.op.bias']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

        if any('block' in layer for layer in down_blocks[i]    ):
            a__    = len(
                {'.'.join(shave_segments(__A       ,		2    ).split('.'    )[:2]    ) for layer in down_blocks[i] if 'block' in layer}    )
            a__    = {
                layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
                for layer_id in range(__A    )
            }

            if num_blocks > 0:
                for j in range(config['layers_per_block']    ):
                    a__    = renew_resnet_paths(blocks[j]    )
                    assign_to_checkpoint(__A       ,		__A       ,		__A    )

        if any('attn' in layer for layer in down_blocks[i]    ):
            a__    = len(
                {'.'.join(shave_segments(__A       ,		2    ).split('.'    )[:2]    ) for layer in down_blocks[i] if 'attn' in layer}    )
            a__    = {
                layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                for layer_id in range(__A    )
            }

            if num_attn > 0:
                for j in range(config['layers_per_block']    ):
                    a__    = renew_attention_paths(attns[j]    )
                    assign_to_checkpoint(__A       ,		__A       ,		__A       ,		config=__A    )

    a__    = [key for key in checkpoint if 'mid.block_1' in key]
    a__    = [key for key in checkpoint if 'mid.block_2' in key]
    a__    = [key for key in checkpoint if 'mid.attn_1' in key]

    # Mid new 2
    a__    = renew_resnet_paths(__A    )
    assign_to_checkpoint(
        __A       ,		__A       ,		__A       ,		additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,		)

    a__    = renew_resnet_paths(__A    )
    assign_to_checkpoint(
        __A       ,		__A       ,		__A       ,		additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,		)

    a__    = renew_attention_paths(__A       ,		in_mid=__A    )
    assign_to_checkpoint(
        __A       ,		__A       ,		__A       ,		additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,		)

    for i in range(__A    ):
        a__    = num_up_blocks - 1 - i

        if any('upsample' in layer for layer in up_blocks[i]    ):
            a__    = checkpoint[
                f'up.{i}.upsample.conv.weight'
            ]
            a__    = checkpoint[f'up.{i}.upsample.conv.bias']

        if any('block' in layer for layer in up_blocks[i]    ):
            a__    = len(
                {'.'.join(shave_segments(__A       ,		2    ).split('.'    )[:2]    ) for layer in up_blocks[i] if 'block' in layer}    )
            a__    = {
                layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(__A    )
            }

            if num_blocks > 0:
                for j in range(config['layers_per_block'] + 1    ):
                    a__    = {'old': f'up_blocks.{i}', 'new': f'up_blocks.{block_id}'}
                    a__    = renew_resnet_paths(blocks[j]    )
                    assign_to_checkpoint(__A       ,		__A       ,		__A       ,		additional_replacements=[replace_indices]    )

        if any('attn' in layer for layer in up_blocks[i]    ):
            a__    = len(
                {'.'.join(shave_segments(__A       ,		2    ).split('.'    )[:2]    ) for layer in up_blocks[i] if 'attn' in layer}    )
            a__    = {
                layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(__A    )
            }

            if num_attn > 0:
                for j in range(config['layers_per_block'] + 1    ):
                    a__    = {'old': f'up_blocks.{i}', 'new': f'up_blocks.{block_id}'}
                    a__    = renew_attention_paths(attns[j]    )
                    assign_to_checkpoint(__A       ,		__A       ,		__A       ,		additional_replacements=[replace_indices]    )

    a__    = {k.replace('mid_new_2'       ,		'mid_block'    ): v for k, v in new_checkpoint.items()}
    return new_checkpoint



def 						__magic_name__		(				UpperCamelCase    :		Any       ,		UpperCamelCase    :		int    )    ->	Tuple:
    a__    = {}

    a__    = checkpoint['encoder.norm_out.weight']
    a__    = checkpoint['encoder.norm_out.bias']

    a__    = checkpoint['encoder.conv_in.weight']
    a__    = checkpoint['encoder.conv_in.bias']
    a__    = checkpoint['encoder.conv_out.weight']
    a__    = checkpoint['encoder.conv_out.bias']

    a__    = checkpoint['decoder.norm_out.weight']
    a__    = checkpoint['decoder.norm_out.bias']

    a__    = checkpoint['decoder.conv_in.weight']
    a__    = checkpoint['decoder.conv_in.bias']
    a__    = checkpoint['decoder.conv_out.weight']
    a__    = checkpoint['decoder.conv_out.bias']

    a__    = len({'.'.join(layer.split('.'    )[:3]    ) for layer in checkpoint if 'down' in layer}    )
    a__    = {
        layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(__A    )
    }

    a__    = len({'.'.join(layer.split('.'    )[:3]    ) for layer in checkpoint if 'up' in layer}    )
    a__    = {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(__A    )}

    for i in range(__A    ):
        a__    = (i - 1) // (config['layers_per_block'] + 1)

        if any('downsample' in layer for layer in down_blocks[i]    ):
            a__    = checkpoint[
                f'encoder.down.{i}.downsample.conv.weight'
            ]
            a__    = checkpoint[
                f'encoder.down.{i}.downsample.conv.bias'
            ]

        if any('block' in layer for layer in down_blocks[i]    ):
            a__    = len(
                {'.'.join(shave_segments(__A       ,		3    ).split('.'    )[:3]    ) for layer in down_blocks[i] if 'block' in layer}    )
            a__    = {
                layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
                for layer_id in range(__A    )
            }

            if num_blocks > 0:
                for j in range(config['layers_per_block']    ):
                    a__    = renew_resnet_paths(blocks[j]    )
                    assign_to_checkpoint(__A       ,		__A       ,		__A    )

        if any('attn' in layer for layer in down_blocks[i]    ):
            a__    = len(
                {'.'.join(shave_segments(__A       ,		3    ).split('.'    )[:3]    ) for layer in down_blocks[i] if 'attn' in layer}    )
            a__    = {
                layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
                for layer_id in range(__A    )
            }

            if num_attn > 0:
                for j in range(config['layers_per_block']    ):
                    a__    = renew_attention_paths(attns[j]    )
                    assign_to_checkpoint(__A       ,		__A       ,		__A       ,		config=__A    )

    a__    = [key for key in checkpoint if 'mid.block_1' in key]
    a__    = [key for key in checkpoint if 'mid.block_2' in key]
    a__    = [key for key in checkpoint if 'mid.attn_1' in key]

    # Mid new 2
    a__    = renew_resnet_paths(__A    )
    assign_to_checkpoint(
        __A       ,		__A       ,		__A       ,		additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,		)

    a__    = renew_resnet_paths(__A    )
    assign_to_checkpoint(
        __A       ,		__A       ,		__A       ,		additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,		)

    a__    = renew_attention_paths(__A       ,		in_mid=__A    )
    assign_to_checkpoint(
        __A       ,		__A       ,		__A       ,		additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,		)

    for i in range(__A    ):
        a__    = num_up_blocks - 1 - i

        if any('upsample' in layer for layer in up_blocks[i]    ):
            a__    = checkpoint[
                f'decoder.up.{i}.upsample.conv.weight'
            ]
            a__    = checkpoint[
                f'decoder.up.{i}.upsample.conv.bias'
            ]

        if any('block' in layer for layer in up_blocks[i]    ):
            a__    = len(
                {'.'.join(shave_segments(__A       ,		3    ).split('.'    )[:3]    ) for layer in up_blocks[i] if 'block' in layer}    )
            a__    = {
                layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(__A    )
            }

            if num_blocks > 0:
                for j in range(config['layers_per_block'] + 1    ):
                    a__    = {'old': f'up_blocks.{i}', 'new': f'up_blocks.{block_id}'}
                    a__    = renew_resnet_paths(blocks[j]    )
                    assign_to_checkpoint(__A       ,		__A       ,		__A       ,		additional_replacements=[replace_indices]    )

        if any('attn' in layer for layer in up_blocks[i]    ):
            a__    = len(
                {'.'.join(shave_segments(__A       ,		3    ).split('.'    )[:3]    ) for layer in up_blocks[i] if 'attn' in layer}    )
            a__    = {
                layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(__A    )
            }

            if num_attn > 0:
                for j in range(config['layers_per_block'] + 1    ):
                    a__    = {'old': f'up_blocks.{i}', 'new': f'up_blocks.{block_id}'}
                    a__    = renew_attention_paths(attns[j]    )
                    assign_to_checkpoint(__A       ,		__A       ,		__A       ,		additional_replacements=[replace_indices]    )

    a__    = {k.replace('mid_new_2'       ,		'mid_block'    ): v for k, v in new_checkpoint.items()}
    a__    = checkpoint['quant_conv.weight']
    a__    = checkpoint['quant_conv.bias']
    if "quantize.embedding.weight" in checkpoint:
        a__    = checkpoint['quantize.embedding.weight']
    a__    = checkpoint['post_quant_conv.weight']
    a__    = checkpoint['post_quant_conv.bias']

    return new_checkpoint


if __name__ == "__main__":
    a		:					Dict    	=					argparse.ArgumentParser()

    parser.add_argument(
        '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
    )

    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The config json file corresponding to the architecture.',
    )

    parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

    a		:					Optional[int]    	=					parser.parse_args()
    a		:					Optional[int]    	=					torch.load(args.checkpoint_path)

    with open(args.config_file) as f:
        a		:					int    	=					json.loads(f.read())

    # unet case
    a		:					int    	=					{key.split('.')[0] for key in checkpoint.keys()}
    if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
        a		:					List[str]    	=					convert_vq_autoenc_checkpoint(checkpoint, config)
    else:
        a		:					List[str]    	=					convert_ddpm_checkpoint(checkpoint, config)

    if "ddpm" in config:
        del config["ddpm"]

    if config["_class_name"] == "VQModel":
        a		:					Tuple    	=					VQModel(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    elif config["_class_name"] == "AutoencoderKL":
        a		:					int    	=					AutoencoderKL(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    else:
        a		:					Tuple    	=					UNetaDModel(**config)
        model.load_state_dict(converted_checkpoint)

        a		:					Optional[int]    	=					DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

        a		:					str    	=					DDPMPipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)




"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a		:					str    	=					False







@nightly
@require_torch_gpu
class 						lowercase(unittest.TestCase					):



     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )

         a__    = torch.manual_seed(0      )
         a__    = pipe(
             prompt='first prompt'    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy'    ,							).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(__SCREAMING_SNAKE_CASE      )
             a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      )

         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = generator.manual_seed(0      )
         a__    = pipe(
             prompt='first prompt'    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy'    ,							).images

         assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"




     def  lowercase__     (       self      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
         pipe.remove_unused_weights()
         pipe.to(__SCREAMING_SNAKE_CASE      )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

         a__    = 'cyberpunk 2077'
         a__    = load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
         a__    = torch.manual_seed(0      )
         a__    = pipe(
             prompt=__SCREAMING_SNAKE_CASE    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=5_0    ,							output_type='numpy'    ,							).images

         a__    = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         a__    = np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2


