






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def      lowercase__      (     __snake_case    :							Optional[Any]     ,       __snake_case    :							List[str]=0.999     ,       __snake_case    :							str="cosine"     ,       ):


					'''simple docstring'''


					if alpha_transform_type == "cosine":

										def alpha_bar_fn(__snake_case    :							Union[str, Any]  ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(__snake_case    :							Optional[int]  ):
															return math.exp(t * -12.0  )

					else:
										raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"  )

					UpperCAmelCase_    :      Dict            =						[]
					for i in range(__snake_case  ):
										UpperCAmelCase_    :      Dict            =						i / num_diffusion_timesteps
										UpperCAmelCase_    :      List[str]            =						(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(__snake_case  ) / alpha_bar_fn(__snake_case  )     ,       __snake_case  )  )
					return torch.tensor(__snake_case     ,       dtype=torch.floataa  )






class   lowerCamelCase      (_snake_case ,  _snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  [e.name for e in KarrasDiffusionSchedulers]
		_snake_case    :    int       						=  2



		@register_to_config
		def __init__(							self				,		_UpperCamelCase = 1_0_0_0				,		_UpperCamelCase = 0.0_00_85				,		_UpperCamelCase = 0.0_12				,		_UpperCamelCase = "linear"				,		_UpperCamelCase = None				,		_UpperCamelCase = "epsilon"				,		_UpperCamelCase = "linspace"				,		_UpperCamelCase = 0				,		)	->							Optional[Any]:
							if trained_betas is not None:
												UpperCAmelCase_    :      Any            =						torch.tensor(_UpperCamelCase				,		dtype=torch.floataa   )
							elif beta_schedule == "linear":
												UpperCAmelCase_    :      Optional[int]            =						torch.linspace(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		dtype=torch.floataa   )
							elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												UpperCAmelCase_    :      str            =						(
												    torch.linspace(beta_start**0.5				,		beta_end**0.5				,		_UpperCamelCase				,		dtype=torch.floataa   ) ** 2
												)
							elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												UpperCAmelCase_    :      List[str]            =						betas_for_alpha_bar(_UpperCamelCase   )
							else:
												raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"   )

							UpperCAmelCase_    :      Dict            =						1.0 - self.betas
							UpperCAmelCase_    :      Tuple            =						torch.cumprod(self.alphas				,		dim=0   )

							#  set all values
							self.set_timesteps(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							List[Any]:
							if schedule_timesteps is None:
												UpperCAmelCase_    :      Optional[Any]            =						self.timesteps

							UpperCAmelCase_    :      Any            =						(schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter   ) == 0:
												UpperCAmelCase_    :      int            =						1 if len(_UpperCamelCase   ) > 1 else 0
							else:
												UpperCAmelCase_    :      Union[str, Any]            =						timestep.cpu().item() if torch.is_tensor(_UpperCamelCase   ) else timestep
												UpperCAmelCase_    :      Optional[int]            =						self._index_counter[timestep_int]

							return indices[pos].item()



		@property
		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							torch.FloatTensor:
							UpperCAmelCase_    :      List[Any]            =						self.index_for_timestep(_UpperCamelCase   )

							if self.state_in_first_order:
												UpperCAmelCase_    :      Dict            =						self.sigmas[step_index]
							else:
												UpperCAmelCase_    :      Union[str, Any]            =						self.sigmas_interpol[step_index]

							UpperCAmelCase_    :      Optional[int]            =						sample / ((sigma**2 + 1) ** 0.5)
							return sample



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		)	->							List[str]:
							UpperCAmelCase_    :      Dict            =						num_inference_steps

							UpperCAmelCase_    :      str            =						num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
												UpperCAmelCase_    :      List[Any]            =						np.linspace(0				,		num_train_timesteps - 1				,		_UpperCamelCase				,		dtype=_UpperCamelCase   )[::-1].copy()
							elif self.config.timestep_spacing == "leading":
												UpperCAmelCase_    :      str            =						num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												UpperCAmelCase_    :      List[Any]            =						(np.arange(0				,		_UpperCamelCase   ) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase   )
												timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
												UpperCAmelCase_    :      Optional[Any]            =						num_train_timesteps / self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												UpperCAmelCase_    :      List[str]            =						(np.arange(_UpperCamelCase				,		0				,		-step_ratio   )).round().copy().astype(_UpperCamelCase   )
												timesteps -= 1
							else:
												raise ValueError(
												    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."   )

							UpperCAmelCase_    :      Union[str, Any]            =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
							UpperCAmelCase_    :      List[str]            =						torch.from_numpy(np.log(_UpperCamelCase   )   ).to(_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						np.interp(_UpperCamelCase				,		np.arange(0				,		len(_UpperCamelCase   )   )				,		_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
							UpperCAmelCase_    :      str            =						torch.from_numpy(_UpperCamelCase   ).to(device=_UpperCamelCase   )

							# interpolate sigmas
							UpperCAmelCase_    :      Optional[int]            =						sigmas.log().lerp(sigmas.roll(1   ).log()				,		0.5   ).exp()

							UpperCAmelCase_    :      Optional[Any]            =						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
							UpperCAmelCase_    :      Optional[int]            =						torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )

							if str(_UpperCamelCase   ).startswith('mps'   ):
												# mps does not support float64
												UpperCAmelCase_    :      Optional[Any]            =						torch.from_numpy(_UpperCamelCase   ).to(_UpperCamelCase				,		dtype=torch.floataa   )
							else:
												UpperCAmelCase_    :      Any            =						torch.from_numpy(_UpperCamelCase   ).to(_UpperCamelCase   )

							# interpolate timesteps
							UpperCAmelCase_    :      Optional[int]            =						self.sigma_to_t(_UpperCamelCase   ).to(_UpperCamelCase				,		dtype=timesteps.dtype   )
							UpperCAmelCase_    :      Optional[Any]            =						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])				,		dim=-1   ).flatten()

							UpperCAmelCase_    :      Tuple            =						torch.cat([timesteps[:1], interleaved_timesteps]   )

							UpperCAmelCase_    :      Dict            =						None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							UpperCAmelCase_    :      Dict            =						defaultdict(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							str:
							# get log sigma
							UpperCAmelCase_    :      List[str]            =						sigma.log()

							# get distribution
							UpperCAmelCase_    :      Any            =						log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							UpperCAmelCase_    :      int            =						dists.ge(0   ).cumsum(dim=0   ).argmax(dim=0   ).clamp(max=self.log_sigmas.shape[0] - 2   )
							UpperCAmelCase_    :      List[str]            =						low_idx + 1

							UpperCAmelCase_    :      List[str]            =						self.log_sigmas[low_idx]
							UpperCAmelCase_    :      int            =						self.log_sigmas[high_idx]

							# interpolate sigmas
							UpperCAmelCase_    :      Optional[Any]            =						(low - log_sigma) / (low - high)
							UpperCAmelCase_    :      Any            =						w.clamp(0				,		1   )

							# transform interpolation to time range
							UpperCAmelCase_    :      Union[str, Any]            =						(1 - w) * low_idx + w * high_idx
							UpperCAmelCase_    :      Optional[Any]            =						t.view(sigma.shape   )
							return t



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							return self.sample is None



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = True				,		)	->							Union[SchedulerOutput, Tuple]:
							UpperCAmelCase_    :      List[Any]            =						self.index_for_timestep(_UpperCamelCase   )

							# advance index counter by 1
							UpperCAmelCase_    :      Union[str, Any]            =						timestep.cpu().item() if torch.is_tensor(_UpperCamelCase   ) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
												UpperCAmelCase_    :      List[str]            =						self.sigmas[step_index]
												UpperCAmelCase_    :      List[str]            =						self.sigmas_interpol[step_index + 1]
												UpperCAmelCase_    :      Optional[Any]            =						self.sigmas[step_index + 1]
							else:
												# 2nd order / KDPM2's method
												UpperCAmelCase_    :      Union[str, Any]            =						self.sigmas[step_index - 1]
												UpperCAmelCase_    :      Any            =						self.sigmas_interpol[step_index]
												UpperCAmelCase_    :      List[Any]            =						self.sigmas[step_index]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							UpperCAmelCase_    :      str            =						0
							UpperCAmelCase_    :      int            =						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
												UpperCAmelCase_    :      Dict            =						sigma_hat if self.state_in_first_order else sigma_interpol
												UpperCAmelCase_    :      Optional[int]            =						sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
												UpperCAmelCase_    :      Any            =						sigma_hat if self.state_in_first_order else sigma_interpol
												UpperCAmelCase_    :      Optional[int]            =						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
												    sample / (sigma_input**2 + 1)
												)
							elif self.config.prediction_type == "sample":
												raise NotImplementedError('prediction_type not implemented yet: sample'   )
							else:
												raise ValueError(
												    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"   )

							if self.state_in_first_order:
												# 2. Convert to an ODE derivative for 1st order
												UpperCAmelCase_    :      List[str]            =						(sample - pred_original_sample) / sigma_hat
												# 3. delta timestep
												UpperCAmelCase_    :      Dict            =						sigma_interpol - sigma_hat

												# store for 2nd order step
												UpperCAmelCase_    :      Any            =						sample
							else:
												# DPM-Solver-2
												# 2. Convert to an ODE derivative for 2nd order
												UpperCAmelCase_    :      Union[str, Any]            =						(sample - pred_original_sample) / sigma_interpol

												# 3. delta timestep
												UpperCAmelCase_    :      Any            =						sigma_next - sigma_hat

												UpperCAmelCase_    :      Dict            =						self.sample
												UpperCAmelCase_    :      str            =						None

							UpperCAmelCase_    :      List[str]            =						sample + derivative * dt

							if not return_dict:
												return (prev_sample,)

							return SchedulerOutput(prev_sample=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							UpperCAmelCase_    :      Tuple            =						self.sigmas.to(device=original_samples.device				,		dtype=original_samples.dtype   )
							if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase   ):
												# mps does not support float64
												UpperCAmelCase_    :      Optional[Any]            =						self.timesteps.to(original_samples.device				,		dtype=torch.floataa   )
												UpperCAmelCase_    :      Union[str, Any]            =						timesteps.to(original_samples.device				,		dtype=torch.floataa   )
							else:
												UpperCAmelCase_    :      Any            =						self.timesteps.to(original_samples.device   )
												UpperCAmelCase_    :      Optional[Any]            =						timesteps.to(original_samples.device   )

							UpperCAmelCase_    :      Any            =						[self.index_for_timestep(_UpperCamelCase				,		_UpperCamelCase   ) for t in timesteps]

							UpperCAmelCase_    :      int            =						sigmas[step_indices].flatten()
							while len(sigma.shape   ) < len(original_samples.shape   ):
												UpperCAmelCase_    :      Optional[Any]            =						sigma.unsqueeze(-1   )

							UpperCAmelCase_    :      int            =						original_samples + noise * sigma
							return noisy_samples



		def __len__(							self   )	->							Union[str, Any]:
							return self.config.num_train_timesteps











'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=1			):



    """simple docstring"""





    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("""."""			)[n_shave_prefix_segments:]			)
    else:
        return ".".join(path.split("""."""			)[:n_shave_prefix_segments]			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  Optional[Any]							      =					old_item.replace("""in_layers.0"""							,    """norm1"""			)
        lowerCAmelCase__ :  Optional[int]							      =					new_item.replace("""in_layers.2"""							,    """conv1"""			)

        lowerCAmelCase__ :  Dict							      =					new_item.replace("""out_layers.0"""							,    """norm2"""			)
        lowerCAmelCase__ :  str							      =					new_item.replace("""out_layers.3"""							,    """conv2"""			)

        lowerCAmelCase__ :  str							      =					new_item.replace("""emb_layers.1"""							,    """time_emb_proj"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""skip_connection"""							,    """conv_shortcut"""			)

        lowerCAmelCase__ :  Union[str, Any]							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

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

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  List[str]							      =					old_item

        lowerCAmelCase__ :  int							      =					new_item.replace("""norm.weight"""							,    """group_norm.weight"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""norm.bias"""							,    """group_norm.bias"""			)

        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""proj_out.weight"""							,    """proj_attn.weight"""			)
        lowerCAmelCase__ :  int							      =					new_item.replace("""proj_out.bias"""							,    """proj_attn.bias"""			)

        lowerCAmelCase__ :  str							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

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

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=None							,    UpperCamelCase=None			):



    """simple docstring"""





    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():
            lowerCAmelCase__ :  Any							      =					old_checkpoint[path]
            lowerCAmelCase__ :  int							      =					old_tensor.shape[0] // 3

            lowerCAmelCase__ :  int							      =					(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

            lowerCAmelCase__ :  Tuple							      =					old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

            lowerCAmelCase__ :  int							      =					query.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Union[str, Any]							      =					key.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Optional[int]							      =					value.reshape(UpperCamelCase			)

    for path in paths:
        lowerCAmelCase__ :  Any							      =					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
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.0"""							,    """mid_block.resnets.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.1"""							,    """mid_block.attentions.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.2"""							,    """mid_block.resnets.1"""			)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                lowerCAmelCase__ :  Any							      =					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:
            lowerCAmelCase__ :  List[Any]							      =					old_checkpoint[path["""old"""]][:, :, 0]
        else:
            lowerCAmelCase__ :  Dict							      =					old_checkpoint[path["""old"""]]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					{}

    lowerCAmelCase__ :  str							      =					checkpoint["""time_embed.0.weight"""]
    lowerCAmelCase__ :  List[Any]							      =					checkpoint["""time_embed.0.bias"""]
    lowerCAmelCase__ :  int							      =					checkpoint["""time_embed.2.weight"""]
    lowerCAmelCase__ :  List[str]							      =					checkpoint["""time_embed.2.bias"""]

    lowerCAmelCase__ :  str							      =					checkpoint["""input_blocks.0.0.weight"""]
    lowerCAmelCase__ :  Any							      =					checkpoint["""input_blocks.0.0.bias"""]

    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.weight"""]
    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.bias"""]
    lowerCAmelCase__ :  str							      =					checkpoint["""out.2.weight"""]
    lowerCAmelCase__ :  Tuple							      =					checkpoint["""out.2.bias"""]

    # Retrieves the keys for the input blocks only
    lowerCAmelCase__ :  Optional[Any]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """input_blocks""" in layer}			)
    lowerCAmelCase__ :  Optional[Any]							      =					{
        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
    lowerCAmelCase__ :  Union[str, Any]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """middle_block""" in layer}			)
    lowerCAmelCase__ :  Union[str, Any]							      =					{
        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
    lowerCAmelCase__ :  List[str]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """output_blocks""" in layer}			)
    lowerCAmelCase__ :  List[Any]							      =					{
        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			):
        lowerCAmelCase__ :  Dict							      =					(i - 1) // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  Tuple							      =					(i - 1) % (config["""num_res_blocks"""] + 1)

        lowerCAmelCase__ :  Optional[int]							      =					[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
        lowerCAmelCase__ :  Optional[Any]							      =					[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:
            lowerCAmelCase__ :  Optional[int]							      =					checkpoint[
                f"""input_blocks.{i}.0.op.weight"""
            ]
            lowerCAmelCase__ :  Tuple							      =					checkpoint[
                f"""input_blocks.{i}.0.op.bias"""
            ]
            continue

        lowerCAmelCase__ :  Optional[Any]							      =					renew_resnet_paths(UpperCamelCase			)
        lowerCAmelCase__ :  Dict							      =					{"""old""": f"""input_blocks.{i}.0""", """new""": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
        lowerCAmelCase__ :  Optional[Any]							      =					{"""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			):
            lowerCAmelCase__ :  Optional[Any]							      =					renew_attention_paths(UpperCamelCase			)
            lowerCAmelCase__ :  Tuple							      =					{
                """old""": f"""input_blocks.{i}.1""",
                """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            lowerCAmelCase__ :  List[str]							      =					{
                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							,    )

    lowerCAmelCase__ :  Dict							      =					middle_blocks[0]
    lowerCAmelCase__ :  Union[str, Any]							      =					middle_blocks[1]
    lowerCAmelCase__ :  Dict							      =					middle_blocks[2]

    lowerCAmelCase__ :  Any							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Dict							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					renew_attention_paths(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					{
        """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			):
        lowerCAmelCase__ :  Tuple							      =					i // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  List[str]							      =					i % (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  int							      =					[shave_segments(UpperCamelCase							,    2			) for name in output_blocks[i]]
        lowerCAmelCase__ :  Union[str, Any]							      =					{}

        for layer in output_block_layers:
            lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					layer.split("""."""			)[0], shave_segments(UpperCamelCase							,    1			)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(UpperCamelCase			)
            else:
                lowerCAmelCase__ :  str							      =					[layer_name]

        if len(UpperCamelCase			) > 1:
            lowerCAmelCase__ :  str							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
            lowerCAmelCase__ :  Dict							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

            lowerCAmelCase__ :  Optional[int]							      =					renew_resnet_paths(UpperCamelCase			)
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase			)

            lowerCAmelCase__ :  Optional[int]							      =					{"""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():
                lowerCAmelCase__ :  List[Any]							      =					list(output_block_list.values()			).index(["""conv.weight""", """conv.bias"""]			)
                lowerCAmelCase__ :  int							      =					checkpoint[
                    f"""output_blocks.{i}.{index}.conv.weight"""
                ]
                lowerCAmelCase__ :  int							      =					checkpoint[
                    f"""output_blocks.{i}.{index}.conv.bias"""
                ]

                # Clear attentions as they have been attributed above.
                if len(UpperCamelCase			) == 2:
                    lowerCAmelCase__ :  Tuple							      =					[]

            if len(UpperCamelCase			):
                lowerCAmelCase__ :  Dict							      =					renew_attention_paths(UpperCamelCase			)
                lowerCAmelCase__ :  Tuple							      =					{
                    """old""": f"""output_blocks.{i}.1""",
                    """new""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                }
                lowerCAmelCase__ :  Tuple							      =					{
                    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:
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase							,    n_shave_prefix_segments=1			)
            for path in resnet_0_paths:
                lowerCAmelCase__ :  Tuple							      =					""".""".join(["""output_blocks""", str(UpperCamelCase			), path["""old"""]]			)
                lowerCAmelCase__ :  List[Any]							      =					""".""".join(["""up_blocks""", str(UpperCamelCase			), """resnets""", str(UpperCamelCase			), path["""new"""]]			)

                lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint[old_path]

    return new_checkpoint


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

 _lowerCAmelCase      						=	parser.parse_args()

 _lowerCAmelCase      						=	torch.load(args.checkpoint_path)

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

 _lowerCAmelCase      						=	convert_ldm_checkpoint(checkpoint, config)

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

 _lowerCAmelCase      						=	UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

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

  _lowerCAmelCase      						=	LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)


