



'''simple docstring'''

from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


__lowerCAmelCase									=   logging.get_logger(__name__)

# General docstring
__lowerCAmelCase									=   '''RegNetConfig'''

# Base docstring
__lowerCAmelCase									=   '''facebook/regnet-y-040'''
__lowerCAmelCase									=   [1, 1_088, 7, 7]

# Image classification docstring
__lowerCAmelCase									=   '''facebook/regnet-y-040'''
__lowerCAmelCase									=   '''tabby, tabby cat'''

__lowerCAmelCase									=   [
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class 	__magic_name__						(    nn.Module   ):







							def __init__(							self					:       Optional[Any]	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int = 3	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       Optional[str] = "relu"	,):
									super().__init__()
									_a     :      Optional[int]        =							nn.Convad(
									    _UpperCAmelCase	,_UpperCAmelCase	,kernel_size=_UpperCAmelCase	,stride=_UpperCAmelCase	,padding=kernel_size // 2	,groups=_UpperCAmelCase	,bias=_UpperCAmelCase	,)
									_a     :      Any        =							nn.BatchNormad(_UpperCAmelCase		)
									_a     :      Optional[int]        =							ACTaFN[activation] if activation is not None else nn.Identity()







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       List[str]		):
									_a     :      Dict        =							self.convolution(_UpperCAmelCase		)
									_a     :      List[str]        =							self.normalization(_UpperCAmelCase		)
									_a     :      List[Any]        =							self.activation(_UpperCAmelCase		)
									return hidden_state

class 	__magic_name__						(    nn.Module   ):







							def __init__(							self					:       Any	,_UpperCAmelCase					:       RegNetConfig		):
									super().__init__()
									_a     :      List[Any]        =							RegNetConvLayer(
									    config.num_channels	,config.embedding_size	,kernel_size=3	,stride=2	,activation=config.hidden_act		)
									_a     :      List[str]        =							config.num_channels







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Tuple		):
									_a     :      Tuple        =							pixel_values.shape[1]
									if num_channels != self.num_channels:
											raise ValueError(
											    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'		)
									_a     :      Optional[int]        =							self.embedder(_UpperCAmelCase		)
									return hidden_state

class 	__magic_name__						(    nn.Module   ):







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int = 2		):
									super().__init__()
									_a     :      str        =							nn.Convad(_UpperCAmelCase	,_UpperCAmelCase	,kernel_size=1	,stride=_UpperCAmelCase	,bias=_UpperCAmelCase		)
									_a     :      Optional[Any]        =							nn.BatchNormad(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       Tensor		):
									_a     :      int        =							self.convolution(_UpperCAmelCase		)
									_a     :      Any        =							self.normalization(_UpperCAmelCase		)
									return hidden_state

class 	__magic_name__						(    nn.Module   ):







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int		):
									super().__init__()

									_a     :      Optional[int]        =							nn.AdaptiveAvgPoolad((1, 1)		)
									_a     :      int        =							nn.Sequential(
									    nn.Convad(_UpperCAmelCase	,_UpperCAmelCase	,kernel_size=1		)	,nn.ReLU()	,nn.Convad(_UpperCAmelCase	,_UpperCAmelCase	,kernel_size=1		)	,nn.Sigmoid()	,)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Optional[int]		):
									# b c h w -> b c 1 1
									_a     :      int        =							self.pooler(_UpperCAmelCase		)
									_a     :      Dict        =							self.attention(_UpperCAmelCase		)
									_a     :      List[str]        =							hidden_state * attention
									return hidden_state

class 	__magic_name__						(    nn.Module   ):







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       RegNetConfig	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int = 1		):
									super().__init__()
									_a     :      str        =							in_channels != out_channels or stride != 1
									_a     :      int        =							max(1	,out_channels // config.groups_width		)
									_a     :      Tuple        =							(
									    RegNetShortCut(_UpperCAmelCase	,_UpperCAmelCase	,stride=_UpperCAmelCase		) if should_apply_shortcut else nn.Identity()
									)
									_a     :      Any        =							nn.Sequential(
									    RegNetConvLayer(_UpperCAmelCase	,_UpperCAmelCase	,kernel_size=1	,activation=config.hidden_act		)	,RegNetConvLayer(_UpperCAmelCase	,_UpperCAmelCase	,stride=_UpperCAmelCase	,groups=_UpperCAmelCase	,activation=config.hidden_act		)	,RegNetConvLayer(_UpperCAmelCase	,_UpperCAmelCase	,kernel_size=1	,activation=_UpperCAmelCase		)	,)
									_a     :      Any        =							ACTaFN[config.hidden_act]







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       str		):
									_a     :      str        =							hidden_state
									_a     :      str        =							self.layer(_UpperCAmelCase		)
									_a     :      Tuple        =							self.shortcut(_UpperCAmelCase		)
									hidden_state += residual
									_a     :      Optional[Any]        =							self.activation(_UpperCAmelCase		)
									return hidden_state

class 	__magic_name__						(    nn.Module   ):







							def __init__(							self					:       Any	,_UpperCAmelCase					:       RegNetConfig	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int = 1		):
									super().__init__()
									_a     :      Optional[int]        =							in_channels != out_channels or stride != 1
									_a     :      str        =							max(1	,out_channels // config.groups_width		)
									_a     :      Dict        =							(
									    RegNetShortCut(_UpperCAmelCase	,_UpperCAmelCase	,stride=_UpperCAmelCase		) if should_apply_shortcut else nn.Identity()
									)
									_a     :      int        =							nn.Sequential(
									    RegNetConvLayer(_UpperCAmelCase	,_UpperCAmelCase	,kernel_size=1	,activation=config.hidden_act		)	,RegNetConvLayer(_UpperCAmelCase	,_UpperCAmelCase	,stride=_UpperCAmelCase	,groups=_UpperCAmelCase	,activation=config.hidden_act		)	,RegNetSELayer(_UpperCAmelCase	,reduced_channels=int(round(in_channels / 4		)		)		)	,RegNetConvLayer(_UpperCAmelCase	,_UpperCAmelCase	,kernel_size=1	,activation=_UpperCAmelCase		)	,)
									_a     :      Optional[int]        =							ACTaFN[config.hidden_act]







							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       Any		):
									_a     :      List[str]        =							hidden_state
									_a     :      Tuple        =							self.layer(_UpperCAmelCase		)
									_a     :      Dict        =							self.shortcut(_UpperCAmelCase		)
									hidden_state += residual
									_a     :      List[str]        =							self.activation(_UpperCAmelCase		)
									return hidden_state

class 	__magic_name__						(    nn.Module   ):







							def __init__(							self					:       Any	,_UpperCAmelCase					:       RegNetConfig	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       int = 2	,_UpperCAmelCase					:       int = 2	,):
									super().__init__()

									_a     :      Dict        =							RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

									_a     :      Any        =							nn.Sequential(
									    # downsampling is done in the first layer with stride of 2
									    layer(
									        _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,stride=_UpperCAmelCase	,)	,*[layer(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		) for _ in range(depth - 1		)]	,)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Union[str, Any]        =							self.layers(_UpperCAmelCase		)
									return hidden_state

class 	__magic_name__						(    nn.Module   ):







							def __init__(							self					:       str	,_UpperCAmelCase					:       RegNetConfig		):
									super().__init__()
									_a     :      Dict        =							nn.ModuleList([]		)
									# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
									self.stages.append(
									    RegNetStage(
									        _UpperCAmelCase	,config.embedding_size	,config.hidden_sizes[0]	,stride=2 if config.downsample_in_first_stage else 1	,depth=config.depths[0]	,)		)
									_a     :      int        =							zip(config.hidden_sizes	,config.hidden_sizes[1:]		)
									for (in_channels, out_channels), depth in zip(_UpperCAmelCase	,config.depths[1:]		):
											self.stages.append(RegNetStage(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,depth=_UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Tensor	,_UpperCAmelCase					:       bool = False	,_UpperCAmelCase					:       bool = True		):
									_a     :      Optional[int]        =							() if output_hidden_states else None

									for stage_module in self.stages:
											if output_hidden_states:
													_a     :      str        =							hidden_states + (hidden_state,)

											_a     :      Any        =							stage_module(_UpperCAmelCase		)

									if output_hidden_states:
											_a     :      str        =							hidden_states + (hidden_state,)

									if not return_dict:
											return tuple(v for v in [hidden_state, hidden_states] if v is not None		)

									return BaseModelOutputWithNoAttention(last_hidden_state=_UpperCAmelCase	,hidden_states=_UpperCAmelCase		)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    int   				=					RegNetConfig
							lowerCAmelCase       :    int   				=					'regnet'
							lowerCAmelCase       :    Optional[int]   				=					'pixel_values'
							lowerCAmelCase       :    List[Any]   				=					True







							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       Dict		):
									if isinstance(_UpperCAmelCase	,nn.Convad		):
											nn.init.kaiming_normal_(module.weight	,mode='fan_out'	,nonlinearity='relu'		)
									elif isinstance(_UpperCAmelCase	,(nn.BatchNormad, nn.GroupNorm)		):
											nn.init.constant_(module.weight	,1		)
											nn.init.constant_(module.bias	,0		)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Dict=False		):
									if isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											_a     :      Union[str, Any]        =							value
__lowerCAmelCase									=   r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__lowerCAmelCase									=   r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''

@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'			,						_UpperCamelCase			,						)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Optional[Any]	,_UpperCAmelCase					:       Any		):
									super().__init__(_UpperCAmelCase		)
									_a     :      List[Any]        =							config
									_a     :      int        =							RegNetEmbeddings(_UpperCAmelCase		)
									_a     :      str        =							RegNetEncoder(_UpperCAmelCase		)
									_a     :      Optional[int]        =							nn.AdaptiveAvgPoolad((1, 1)		)
									# Initialize weights and apply final processing
									self.post_init()







							@add_start_docstrings_to_model_forward(_UpperCAmelCase		)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC	,output_type=_UpperCAmelCase	,config_class=_CONFIG_FOR_DOC	,modality='vision'	,expected_output=_EXPECTED_OUTPUT_SHAPE	,)
							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       Tensor	,_UpperCAmelCase					:       Optional[bool] = None	,_UpperCAmelCase					:       Optional[bool] = None		):
									_a     :      Any        =							(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									_a     :      Optional[int]        =							return_dict if return_dict is not None else self.config.use_return_dict

									_a     :      List[str]        =							self.embedder(_UpperCAmelCase		)

									_a     :      Tuple        =							self.encoder(
									    _UpperCAmelCase	,output_hidden_states=_UpperCAmelCase	,return_dict=_UpperCAmelCase		)

									_a     :      List[Any]        =							encoder_outputs[0]

									_a     :      int        =							self.pooler(_UpperCAmelCase		)

									if not return_dict:
											return (last_hidden_state, pooled_output) + encoder_outputs[1:]

									return BaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=_UpperCAmelCase	,pooler_output=_UpperCAmelCase	,hidden_states=encoder_outputs.hidden_states	,)

@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '			,						_UpperCamelCase			,						)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Dict		):
									super().__init__(_UpperCAmelCase		)
									_a     :      List[Any]        =							config.num_labels
									_a     :      Any        =							RegNetModel(_UpperCAmelCase		)
									# classification head
									_a     :      Union[str, Any]        =							nn.Sequential(
									    nn.Flatten()	,nn.Linear(config.hidden_sizes[-1]	,config.num_labels		) if config.num_labels > 0 else nn.Identity()	,)
									# initialize weights and apply final processing
									self.post_init()







							@add_start_docstrings_to_model_forward(_UpperCAmelCase		)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT	,output_type=_UpperCAmelCase	,config_class=_CONFIG_FOR_DOC	,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,)
							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       Optional[torch.FloatTensor] = None	,_UpperCAmelCase					:       Optional[torch.LongTensor] = None	,_UpperCAmelCase					:       Optional[bool] = None	,_UpperCAmelCase					:       Optional[bool] = None	,):
									_a     :      Any        =							return_dict if return_dict is not None else self.config.use_return_dict

									_a     :      Optional[int]        =							self.regnet(_UpperCAmelCase	,output_hidden_states=_UpperCAmelCase	,return_dict=_UpperCAmelCase		)

									_a     :      Optional[int]        =							outputs.pooler_output if return_dict else outputs[1]

									_a     :      List[Any]        =							self.classifier(_UpperCAmelCase		)

									_a     :      Dict        =							None

									if labels is not None:
											if self.config.problem_type is None:
													if self.num_labels == 1:
															_a     :      Tuple        =							'regression'
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
															_a     :      Any        =							'single_label_classification'
													else:
															_a     :      Union[str, Any]        =							'multi_label_classification'
											if self.config.problem_type == "regression":
													_a     :      List[str]        =							MSELoss()
													if self.num_labels == 1:
															_a     :      int        =							loss_fct(logits.squeeze()	,labels.squeeze()		)
													else:
															_a     :      Union[str, Any]        =							loss_fct(_UpperCAmelCase	,_UpperCAmelCase		)
											elif self.config.problem_type == "single_label_classification":
													_a     :      List[Any]        =							CrossEntropyLoss()
													_a     :      Tuple        =							loss_fct(logits.view(-1	,self.num_labels		)	,labels.view(-1		)		)
											elif self.config.problem_type == "multi_label_classification":
													_a     :      List[Any]        =							BCEWithLogitsLoss()
													_a     :      int        =							loss_fct(_UpperCAmelCase	,_UpperCAmelCase		)

									if not return_dict:
											_a     :      int        =							(logits,) + outputs[2:]
											return (loss,) + output if loss is not None else output

									return ImageClassifierOutputWithNoAttention(loss=_UpperCAmelCase	,logits=_UpperCAmelCase	,hidden_states=outputs.hidden_states		)









'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=1  )			->      Dict:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('.'  )[n_shave_prefix_segments:]  )
		else:
				return ".".join(path.split('.'  )[:n_shave_prefix_segments]  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Tuple:
		_a     :      Any        =							[]
		for old_item in old_list:
				_a     :      Union[str, Any]        =							old_item.replace('in_layers.0'						,			'norm1'  )
				_a     :      Optional[int]        =							new_item.replace('in_layers.2'						,			'conv1'  )

				_a     :      str        =							new_item.replace('out_layers.0'						,			'norm2'  )
				_a     :      List[str]        =							new_item.replace('out_layers.3'						,			'conv2'  )

				_a     :      str        =							new_item.replace('emb_layers.1'						,			'time_emb_proj'  )
				_a     :      Tuple        =							new_item.replace('skip_connection'						,			'conv_shortcut'  )

				_a     :      Any        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

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

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Any:
		_a     :      List[str]        =							[]
		for old_item in old_list:
				_a     :      List[Any]        =							old_item

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

				_a     :      Any        =							new_item.replace('proj_out.weight'						,			'proj_attn.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('proj_out.bias'						,			'proj_attn.bias'  )

				_a     :      Optional[int]        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

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

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None  )			->      Any:
		assert isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ), "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     :      Optional[Any]        =							old_checkpoint[path]
						_a     :      Optional[Any]        =							old_tensor.shape[0] // 3

						_a     :      Any        =							(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

						_a     :      int        =							old_tensor.shape[0] // config['num_head_channels'] // 3

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

						_a     :      Dict        =							query.reshape(lowerCAmelCase_  )
						_a     :      str        =							key.reshape(lowerCAmelCase_  )
						_a     :      Optional[int]        =							value.reshape(lowerCAmelCase_  )

		for path in paths:
				_a     :      Dict        =							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     :      Any        =							new_path.replace('middle_block.0'						,			'mid_block.resnets.0'  )
				_a     :      str        =							new_path.replace('middle_block.1'						,			'mid_block.attentions.0'  )
				_a     :      Union[str, Any]        =							new_path.replace('middle_block.2'						,			'mid_block.resnets.1'  )

				if additional_replacements is not None:
						for replacement in additional_replacements:
								_a     :      int        =							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     :      List[str]        =							old_checkpoint[path['old']][:, :, 0]
				else:
						_a     :      Dict        =							old_checkpoint[path['old']]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		_a     :      Optional[int]        =							{}

		_a     :      Dict        =							checkpoint['time_embed.0.weight']
		_a     :      Tuple        =							checkpoint['time_embed.0.bias']
		_a     :      Union[str, Any]        =							checkpoint['time_embed.2.weight']
		_a     :      List[str]        =							checkpoint['time_embed.2.bias']

		_a     :      List[str]        =							checkpoint['input_blocks.0.0.weight']
		_a     :      Union[str, Any]        =							checkpoint['input_blocks.0.0.bias']

		_a     :      Optional[int]        =							checkpoint['out.0.weight']
		_a     :      int        =							checkpoint['out.0.bias']
		_a     :      List[str]        =							checkpoint['out.2.weight']
		_a     :      Optional[int]        =							checkpoint['out.2.bias']

		# Retrieves the keys for the input blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'input_blocks' in layer}  )
		_a     :      Dict        =							{
		    layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the middle blocks only
		_a     :      List[Any]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'middle_block' in layer}  )
		_a     :      Union[str, Any]        =							{
		    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the output blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'output_blocks' in layer}  )
		_a     :      str        =							{
		    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		for i in range(1						,			lowerCAmelCase_  ):
				_a     :      List[Any]        =							(i - 1) // (config['num_res_blocks'] + 1)
				_a     :      Optional[int]        =							(i - 1) % (config['num_res_blocks'] + 1)

				_a     :      Optional[int]        =							[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
				_a     :      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:
						_a     :      List[Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.weight"""
						]
						_a     :      Union[str, Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.bias"""
						]
						continue

				_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
				_a     :      List[str]        =							{'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
				_a     :      Optional[Any]        =							{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
				assign_to_checkpoint(
				    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path, resnet_op]						,			config=lowerCAmelCase_  )

				if len(lowerCAmelCase_  ):
						_a     :      List[str]        =							renew_attention_paths(lowerCAmelCase_  )
						_a     :      List[Any]        =							{
						    'old': f"""input_blocks.{i}.1""",
						    'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
						}
						_a     :      Optional[Any]        =							{
						    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(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_						,			)

		_a     :      str        =							middle_blocks[0]
		_a     :      Tuple        =							middle_blocks[1]
		_a     :      Any        =							middle_blocks[2]

		_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      int        =							renew_attention_paths(lowerCAmelCase_  )
		_a     :      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(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_  )

		for i in range(lowerCAmelCase_  ):
				_a     :      List[str]        =							i // (config['num_res_blocks'] + 1)
				_a     :      Any        =							i % (config['num_res_blocks'] + 1)
				_a     :      Union[str, Any]        =							[shave_segments(lowerCAmelCase_						,			2  ) for name in output_blocks[i]]
				_a     :      Optional[Any]        =							{}

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

				if len(lowerCAmelCase_  ) > 1:
						_a     :      str        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
						_a     :      Optional[Any]        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

						_a     :      Dict        =							renew_resnet_paths(lowerCAmelCase_  )
						_a     :      str        =							renew_resnet_paths(lowerCAmelCase_  )

						_a     :      Optional[int]        =							{'old': f"""output_blocks.{i}.0""", 'new': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
						assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			config=lowerCAmelCase_  )

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

								# Clear attentions as they have been attributed above.
								if len(lowerCAmelCase_  ) == 2:
										_a     :      Union[str, Any]        =							[]

						if len(lowerCAmelCase_  ):
								_a     :      Tuple        =							renew_attention_paths(lowerCAmelCase_  )
								_a     :      str        =							{
								    'old': f"""output_blocks.{i}.1""",
								    'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
								}
								_a     :      List[Any]        =							{
								    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(
								    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=to_split if any('qkv' in key for key in attentions  ) else None						,			config=lowerCAmelCase_						,			)
				else:
						_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_						,			n_shave_prefix_segments=1  )
						for path in resnet_0_paths:
								_a     :      int        =							'.'.join(['output_blocks', str(lowerCAmelCase_  ), path['old']]  )
								_a     :      Union[str, Any]        =							'.'.join(['up_blocks', str(lowerCAmelCase_  ), 'resnets', str(lowerCAmelCase_  ), path['new']]  )

								_a     :      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)




