



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=1       ):
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split('''.'''       )[n_shave_prefix_segments:]       )
    else:
        return ".".join(path.split('''.'''       )[:n_shave_prefix_segments]       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=0       ):
    __a		 =       []
    for old_item in old_list:
        __a		 =       old_item.replace('''in_layers.0'''     ,				'''norm1'''       )
        __a		 =       new_item.replace('''in_layers.2'''     ,				'''conv1'''       )

        __a		 =       new_item.replace('''out_layers.0'''     ,				'''norm2'''       )
        __a		 =       new_item.replace('''out_layers.3'''     ,				'''conv2'''       )

        __a		 =       new_item.replace('''emb_layers.1'''     ,				'''time_emb_proj'''       )
        __a		 =       new_item.replace('''skip_connection'''     ,				'''conv_shortcut'''       )

        __a		 =       shave_segments(_UpperCAmelCase     ,				n_shave_prefix_segments=_UpperCAmelCase       )

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

    return mapping





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=0       ):
    __a		 =       []
    for old_item in old_list:
        __a		 =       old_item

        __a		 =       new_item.replace('''norm.weight'''     ,				'''group_norm.weight'''       )
        __a		 =       new_item.replace('''norm.bias'''     ,				'''group_norm.bias'''       )

        __a		 =       new_item.replace('''proj_out.weight'''     ,				'''proj_attn.weight'''       )
        __a		 =       new_item.replace('''proj_out.bias'''     ,				'''proj_attn.bias'''       )

        __a		 =       shave_segments(_UpperCAmelCase     ,				n_shave_prefix_segments=_UpperCAmelCase       )

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

    return mapping





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase=None       ):
    assert isinstance(_UpperCAmelCase     ,				_UpperCAmelCase       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        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['''num_head_channels'''] // 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(_UpperCAmelCase       )
            __a		 =       key.reshape(_UpperCAmelCase       )
            __a		 =       value.reshape(_UpperCAmelCase       )

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

        # These have already been assigned
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        # Global renaming happens here
        __a		 =       new_path.replace('''middle_block.0'''     ,				'''mid_block.resnets.0'''       )
        __a		 =       new_path.replace('''middle_block.1'''     ,				'''mid_block.attentions.0'''       )
        __a		 =       new_path.replace('''middle_block.2'''     ,				'''mid_block.resnets.1'''       )

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

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            __a		 =       old_checkpoint[path['''old''']][:, :, 0]
        else:
            __a		 =       old_checkpoint[path['''old''']]





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __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''']

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

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

    # Retrieves the keys for the input blocks only
    __a		 =       len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in checkpoint if '''input_blocks''' in layer}       )
    __a		 =       {
        layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
        for layer_id in range(_UpperCAmelCase       )
    }

    # Retrieves the keys for the middle blocks only
    __a		 =       len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in checkpoint if '''middle_block''' in layer}       )
    __a		 =       {
        layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
        for layer_id in range(_UpperCAmelCase       )
    }

    # Retrieves the keys for the output blocks only
    __a		 =       len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in checkpoint if '''output_blocks''' in layer}       )
    __a		 =       {
        layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
        for layer_id in range(_UpperCAmelCase       )
    }

    for i in range(1     ,				_UpperCAmelCase       ):
        __a		 =       (i - 1) // (config['''num_res_blocks'''] + 1)
        __a		 =       (i - 1) % (config['''num_res_blocks'''] + 1)

        __a		 =       [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
        __a		 =       [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

        if f'input_blocks.{i}.0.op.weight' in checkpoint:
            __a		 =       checkpoint[
                f'input_blocks.{i}.0.op.weight'
            ]
            __a		 =       checkpoint[
                f'input_blocks.{i}.0.op.bias'
            ]
            continue

        __a		 =       renew_resnet_paths(_UpperCAmelCase       )
        __a		 =       {'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
        __a		 =       {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
        assign_to_checkpoint(
            _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				additional_replacements=[meta_path, resnet_op]     ,				config=_UpperCAmelCase       )

        if len(_UpperCAmelCase       ):
            __a		 =       renew_attention_paths(_UpperCAmelCase       )
            __a		 =       {
                '''old''': f'input_blocks.{i}.1',
                '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
            }
            __a		 =       {
                f'input_blocks.{i}.1.qkv.bias': {
                    '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                    '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                    '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                },
                f'input_blocks.{i}.1.qkv.weight': {
                    '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                    '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                    '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                },
            }
            assign_to_checkpoint(
                _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				additional_replacements=[meta_path]     ,				attention_paths_to_split=_UpperCAmelCase     ,				config=_UpperCAmelCase     ,				)

    __a		 =       middle_blocks[0]
    __a		 =       middle_blocks[1]
    __a		 =       middle_blocks[2]

    __a		 =       renew_resnet_paths(_UpperCAmelCase       )
    assign_to_checkpoint(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				config=_UpperCAmelCase       )

    __a		 =       renew_resnet_paths(_UpperCAmelCase       )
    assign_to_checkpoint(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				config=_UpperCAmelCase       )

    __a		 =       renew_attention_paths(_UpperCAmelCase       )
    __a		 =       {
        '''middle_block.1.qkv.bias''': {
            '''key''': '''mid_block.attentions.0.key.bias''',
            '''query''': '''mid_block.attentions.0.query.bias''',
            '''value''': '''mid_block.attentions.0.value.bias''',
        },
        '''middle_block.1.qkv.weight''': {
            '''key''': '''mid_block.attentions.0.key.weight''',
            '''query''': '''mid_block.attentions.0.query.weight''',
            '''value''': '''mid_block.attentions.0.value.weight''',
        },
    }
    assign_to_checkpoint(
        _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				attention_paths_to_split=_UpperCAmelCase     ,				config=_UpperCAmelCase       )

    for i in range(_UpperCAmelCase       ):
        __a		 =       i // (config['''num_res_blocks'''] + 1)
        __a		 =       i % (config['''num_res_blocks'''] + 1)
        __a		 =       [shave_segments(_UpperCAmelCase     ,				2       ) for name in output_blocks[i]]
        __a		 =       {}

        for layer in output_block_layers:
            __a ,     __a		 =       layer.split('''.'''       )[0], shave_segments(_UpperCAmelCase     ,				1       )
            if layer_id in output_block_list:
                output_block_list[layer_id].append(_UpperCAmelCase       )
            else:
                __a		 =       [layer_name]

        if len(_UpperCAmelCase       ) > 1:
            __a		 =       [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
            __a		 =       [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

            __a		 =       renew_resnet_paths(_UpperCAmelCase       )
            __a		 =       renew_resnet_paths(_UpperCAmelCase       )

            __a		 =       {'''old''': f'output_blocks.{i}.0', '''new''': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
            assign_to_checkpoint(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				additional_replacements=[meta_path]     ,				config=_UpperCAmelCase       )

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                __a		 =       list(output_block_list.values()       ).index(['''conv.weight''', '''conv.bias''']       )
                __a		 =       checkpoint[
                    f'output_blocks.{i}.{index}.conv.weight'
                ]
                __a		 =       checkpoint[
                    f'output_blocks.{i}.{index}.conv.bias'
                ]

                # Clear attentions as they have been attributed above.
                if len(_UpperCAmelCase       ) == 2:
                    __a		 =       []

            if len(_UpperCAmelCase       ):
                __a		 =       renew_attention_paths(_UpperCAmelCase       )
                __a		 =       {
                    '''old''': f'output_blocks.{i}.1',
                    '''new''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                }
                __a		 =       {
                    f'output_blocks.{i}.1.qkv.bias': {
                        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                    },
                    f'output_blocks.{i}.1.qkv.weight': {
                        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                    },
                }
                assign_to_checkpoint(
                    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				additional_replacements=[meta_path]     ,				attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions       ) else None     ,				config=_UpperCAmelCase     ,				)
        else:
            __a		 =       renew_resnet_paths(_UpperCAmelCase     ,				n_shave_prefix_segments=1       )
            for path in resnet_0_paths:
                __a		 =       '''.'''.join(['''output_blocks''', str(_UpperCAmelCase       ), path['''old''']]       )
                __a		 =       '''.'''.join(['''up_blocks''', str(_UpperCAmelCase       ), '''resnets''', str(_UpperCAmelCase       ), path['''new''']]       )

                __a		 =       checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
 __snake_case		:int   	=  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.''')

 __snake_case		:Union[str, Any]   	=  parser.parse_args()

 __snake_case		:List[Any]   	=  torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  __snake_case		:Union[str, Any]   	=  json.loads(f.read())

 __snake_case		:List[str]   	=  convert_ldm_checkpoint(checkpoint, config)

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

 __snake_case		:Dict   	=  UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  __snake_case		:Optional[int]   	=  DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  __snake_case		:Tuple   	=  VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  __snake_case		:Optional[int]   	=  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)










import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def   __snake_case			(    _UpperCAmelCase       ):
    __a ,     __a		 =       image.size
    __a ,     __a		 =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    __a		 =       image.resize((w, h)     ,				resample=PIL_INTERPOLATION['''lanczos''']       )
    __a		 =       np.array(_UpperCAmelCase       ).astype(np.floataa       ) / 2_55.0
    __a		 =       image[None].transpose(0     ,				3     ,				1     ,				2       )
    __a		 =       torch.from_numpy(_UpperCAmelCase       )
    return 2.0 * image - 1.0

class 					_A (			__UpperCAmelCase					):



       def __init__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  VQModel  ,	__SCREAMING_SNAKE_CASE							:  UNetaDModel  ,	__SCREAMING_SNAKE_CASE							:  Union[
               DDIMScheduler,
               PNDMScheduler,
               LMSDiscreteScheduler,
               EulerDiscreteScheduler,
               EulerAncestralDiscreteScheduler,
               DPMSolverMultistepScheduler,
           ]  ,	):


           '''simple docstring'''

           super().__init__()
           self.register_modules(vqvae=__SCREAMING_SNAKE_CASE  ,	unet=__SCREAMING_SNAKE_CASE  ,	scheduler=__SCREAMING_SNAKE_CASE)





       @torch.no_grad()
       def __call__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[torch.Tensor, PIL.Image.Image] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 1  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 100  ,	__SCREAMING_SNAKE_CASE							:  Optional[float] = 0.0  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = "pil"  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       1
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor):
               __a		 =       image.shape[0]
           else:
               raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE)}')

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       preprocess(__SCREAMING_SNAKE_CASE)

           __a ,     __a		 =       image.shape[-2:]

           # in_channels should be 6: 3 for latents, 3 for low resolution image
           __a		 =       (batch_size, self.unet.config.in_channels // 2, height, width)
           __a		 =       next(self.unet.parameters()).dtype

           __a		 =       randn_tensor(__SCREAMING_SNAKE_CASE  ,	generator=__SCREAMING_SNAKE_CASE  ,	device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           __a		 =       image.to(device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           # set timesteps and move to the correct device
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,	device=self.device)
           __a		 =       self.scheduler.timesteps

           # scale the initial noise by the standard deviation required by the scheduler
           __a		 =       latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           __a		 =       '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
           __a		 =       {}
           if accepts_eta:
               __a		 =       eta

           for t in self.progress_bar(__SCREAMING_SNAKE_CASE):
               # concat latents and low resolution image in the channel dimension.
               __a		 =       torch.cat([latents, image]  ,	dim=1)
               __a		 =       self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               # predict the noise residual
               __a		 =       self.unet(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE).sample
               # compute the previous noisy sample x_t -> x_t-1
               __a		 =       self.scheduler.step(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE).prev_sample

           # decode the image latents with the VQVAE
           __a		 =       self.vqvae.decode(__SCREAMING_SNAKE_CASE).sample
           __a		 =       torch.clamp(__SCREAMING_SNAKE_CASE  ,	-1.0  ,	1.0)
           __a		 =       image / 2 + 0.5
           __a		 =       image.cpu().permute(0  ,	2  ,	3  ,	1).numpy()

           if output_type == "pil":
               __a		 =       self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)





