



'''simple docstring'''
import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_UpperCamelCase									=      False
_UpperCamelCase									=      Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
_UpperCamelCase									=      datasets.Features(
    OrderedDict(
        {
            '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
            '''img_id''': datasets.Value('''int32'''),
            '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)



class _A	:





		def __init__(  self			,							__UpperCAmelCase=sys.argv[1:]					)							->  Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  None
					__UpperCAmelCase :   List[Any]    					=  None
					__UpperCAmelCase :   Any    					=  None
					__UpperCAmelCase :   Optional[int]    					=  1
					__UpperCAmelCase					,	__UpperCAmelCase :   List[str]    					=  getopt.getopt(__UpperCAmelCase			,							"""i:o:b:s"""			,							["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]					)
					for opt, arg in opts:
								if opt in ("-i", "--inputdir"):
											__UpperCAmelCase :   Union[str, Any]    					=  arg
								elif opt in ("-o", "--outfile"):
											__UpperCAmelCase :   str    					=  arg
								elif opt in ("-b", "--batch_size"):
											__UpperCAmelCase :   Optional[Any]    					=  int(__UpperCAmelCase					)
								elif opt in ("-s", "--subset_list"):
											__UpperCAmelCase :   int    					=  arg

					assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
					assert outputfile is not None and not os.path.isfile(__UpperCAmelCase					), f'{outputfile}'
					if subset_list is not None:
								with open(os.path.realpath(__UpperCAmelCase					)					) as f:
											__UpperCAmelCase :   Optional[int]    					=  {self._vqa_file_split()[0] for x in tryload(__UpperCAmelCase					)}
					else:
								__UpperCAmelCase :   Tuple    					=  None

					__UpperCAmelCase :   Optional[int]    					=  CONFIG
					if torch.cuda.is_available():
								__UpperCAmelCase :   List[str]    					=  """cuda"""
					__UpperCAmelCase :   str    					=  os.path.realpath(__UpperCAmelCase					)
					__UpperCAmelCase :   Union[str, Any]    					=  os.path.realpath(__UpperCAmelCase					)
					__UpperCAmelCase :   Optional[int]    					=  Preprocess(self.config					)
					__UpperCAmelCase :   Any    					=  GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""			,							config=self.config					)
					__UpperCAmelCase :   Tuple    					=  batch_size if batch_size != 0 else 1
					__UpperCAmelCase :   Tuple    					=  DEFAULT_SCHEMA





		def 				__A							(  self			,							__UpperCAmelCase					)							->  str:

					'''simple docstring'''
					__UpperCAmelCase :   List[Any]    					=  int(file.split("""."""					)[0].split("""_"""					)[-1]					)
					__UpperCAmelCase :   Dict    					=  os.path.join(self.inputdir			,							__UpperCAmelCase					)
					return (img_id, filepath)





		@property
		def 				__A							(  self					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[int]    					=  []
					for i, file in enumerate(os.listdir(self.inputdir					)					):
								if self.subset_list is not None and i not in self.subset_list:
											continue
								batch.append(self._vqa_file_split(__UpperCAmelCase					)					)
								if len(__UpperCAmelCase					) == self.batch:
											__UpperCAmelCase :   List[str]    					=  batch
											__UpperCAmelCase :   int    					=  []
											yield list(map(__UpperCAmelCase			,							zip(*__UpperCAmelCase					)					)					)

					for i in range(1					):
								yield list(map(__UpperCAmelCase			,							zip(*__UpperCAmelCase					)					)					)





		def __call__(  self					)							->  Any:

					'''simple docstring'''
					# make writer
					if not TEST:
								__UpperCAmelCase :   str    					=  datasets.ArrowWriter(features=self.schema			,							path=self.outputfile					)
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator					):
								__UpperCAmelCase					,	__UpperCAmelCase					,	__UpperCAmelCase :   List[Any]    					=  self.preprocess(__UpperCAmelCase					)
								__UpperCAmelCase :   List[str]    					=  self.model(
								    __UpperCAmelCase			,							__UpperCAmelCase			,							scales_yx=__UpperCAmelCase			,							padding="""max_detections"""			,							max_detections=self.config.MAX_DETECTIONS			,							pad_value=0			,							return_tensors="""np"""			,							location="""cpu"""			,							)
								__UpperCAmelCase :   int    					=  output_dict.pop("""normalized_boxes"""					)
								if not TEST:
											__UpperCAmelCase :   List[Any]    					=  np.array(__UpperCAmelCase					)
											__UpperCAmelCase :   Any    					=  self.schema.encode_batch(__UpperCAmelCase					)
											writer.write_batch(__UpperCAmelCase					)
								if TEST:
											break
								# finalizer the writer
					if not TEST:
								__UpperCAmelCase					,	__UpperCAmelCase :   Dict    					=  writer.finalize()
								print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'					)


def 	lowercase_						( lowerCAmelCase__						:	Any			):

			"""simple docstring"""





			try:
						__UpperCAmelCase :   int    					=  json.load(lowerCAmelCase__			)
						try:
									__UpperCAmelCase :   str    					=  list(data.keys()			)
						except Exception:
									__UpperCAmelCase :   Dict    					=  [d["""img_id"""] for d in data]
			except Exception:
						try:
									__UpperCAmelCase :   Optional[int]    					=  eval(stream.read()			)
						except Exception:
									__UpperCAmelCase :   List[str]    					=  stream.read().split("""\n"""			)
			return data


if __name__ == "__main__":
					_UpperCamelCase									=      Extract(sys.argv[1:])
					extract()
					if not TEST:
										_UpperCamelCase									=      datasets.Dataset.from_file(extract.outputfile)
										# wala!
										# print(np.array(dataset[0:2]["roi_features"]).shape)









'''simple docstring'''
import argparse
import json

import torch

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


def 	lowercase_						( lowerCAmelCase__						:	Tuple      ,				lowerCAmelCase__						:	Optional[Any]=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 	lowercase_						( lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	str=0			):

			"""simple docstring"""





			__UpperCAmelCase :   int    					=  []
			for old_item in old_list:
						__UpperCAmelCase :   str    					=  old_item
						__UpperCAmelCase :   Dict    					=  new_item.replace("""block."""      ,				"""resnets."""			)
						__UpperCAmelCase :   Union[str, Any]    					=  new_item.replace("""conv_shorcut"""      ,				"""conv1"""			)
						__UpperCAmelCase :   List[str]    					=  new_item.replace("""in_shortcut"""      ,				"""conv_shortcut"""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace("""temb_proj"""      ,				"""time_emb_proj"""			)

						__UpperCAmelCase :   List[str]    					=  shave_segments(lowerCAmelCase__      ,				n_shave_prefix_segments=lowerCAmelCase__			)

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

			return mapping


def 	lowercase_						( lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	Any=0      ,				lowerCAmelCase__						:	Optional[Any]=False			):

			"""simple docstring"""





			__UpperCAmelCase :   Any    					=  []
			for old_item in old_list:
						__UpperCAmelCase :   List[Any]    					=  old_item

						# In `model.mid`, the layer is called `attn`.
						if not in_mid:
									__UpperCAmelCase :   List[Any]    					=  new_item.replace("""attn"""      ,				"""attentions"""			)
						__UpperCAmelCase :   str    					=  new_item.replace(""".k."""      ,				""".key."""			)
						__UpperCAmelCase :   Dict    					=  new_item.replace(""".v."""      ,				""".value."""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace(""".q."""      ,				""".query."""			)

						__UpperCAmelCase :   str    					=  new_item.replace("""proj_out"""      ,				"""proj_attn"""			)
						__UpperCAmelCase :   Optional[int]    					=  new_item.replace("""norm"""      ,				"""group_norm"""			)

						__UpperCAmelCase :   List[str]    					=  shave_segments(lowerCAmelCase__      ,				n_shave_prefix_segments=lowerCAmelCase__			)
						mapping.append({"""old""": old_item, """new""": new_item}			)

			return mapping


def 	lowercase_						( lowerCAmelCase__						:	Dict      ,				lowerCAmelCase__						:	Optional[Any]      ,				lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	int=None      ,				lowerCAmelCase__						:	List[str]=None      ,				lowerCAmelCase__						:	int=None			):

			"""simple docstring"""





			assert isinstance(lowerCAmelCase__      ,				lowerCAmelCase__			), "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():
									__UpperCAmelCase :   Optional[int]    					=  old_checkpoint[path]
									__UpperCAmelCase :   Union[str, Any]    					=  old_tensor.shape[0] // 3

									__UpperCAmelCase :   Optional[int]    					=  (-1, channels) if len(old_tensor.shape			) == 3 else (-1)

									__UpperCAmelCase :   Optional[int]    					=  old_tensor.shape[0] // config.get("""num_head_channels"""      ,				1			) // 3

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

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

			for path in paths:
						__UpperCAmelCase :   Optional[Any]    					=  path["""new"""]

						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						__UpperCAmelCase :   List[str]    					=  new_path.replace("""down."""      ,				"""down_blocks."""			)
						__UpperCAmelCase :   List[str]    					=  new_path.replace("""up."""      ,				"""up_blocks."""			)

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

						if "attentions" in new_path:
									__UpperCAmelCase :   int    					=  old_checkpoint[path["""old"""]].squeeze()
						else:
									__UpperCAmelCase :   str    					=  old_checkpoint[path["""old"""]]


def 	lowercase_						( lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	Tuple			):

			"""simple docstring"""





			__UpperCAmelCase :   Optional[int]    					=  {}

			__UpperCAmelCase :   Dict    					=  checkpoint["""temb.dense.0.weight"""]
			__UpperCAmelCase :   Tuple    					=  checkpoint["""temb.dense.0.bias"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""temb.dense.1.weight"""]
			__UpperCAmelCase :   Any    					=  checkpoint["""temb.dense.1.bias"""]

			__UpperCAmelCase :   Any    					=  checkpoint["""norm_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""norm_out.bias"""]

			__UpperCAmelCase :   Optional[int]    					=  checkpoint["""conv_in.weight"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""conv_in.bias"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""conv_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""conv_out.bias"""]

			__UpperCAmelCase :   Tuple    					=  len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """down""" in layer}			)
			__UpperCAmelCase :   str    					=  {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
			}

			__UpperCAmelCase :   str    					=  len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """up""" in layer}			)
			__UpperCAmelCase :   Union[str, Any]    					=  {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)}

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Any    					=  (i - 1) // (config["""layers_per_block"""] + 1)

						if any("""downsample""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Dict    					=  checkpoint[
									    f'down.{i}.downsample.op.weight'
									]
									__UpperCAmelCase :   int    					=  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]			):
									__UpperCAmelCase :   str    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				2			).split("""."""			)[:2]			) for layer in down_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Tuple    					=  {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   List[str]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__			)

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

									if num_attn > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   Dict    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				config=lowerCAmelCase__			)

			__UpperCAmelCase :   List[Any]    					=  [key for key in checkpoint if """mid.block_1""" in key]
			__UpperCAmelCase :   Union[str, Any]    					=  [key for key in checkpoint if """mid.block_2""" in key]
			__UpperCAmelCase :   Optional[Any]    					=  [key for key in checkpoint if """mid.attn_1""" in key]

			# Mid new 2
			__UpperCAmelCase :   Optional[Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]      ,				)

			__UpperCAmelCase :   Union[str, Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,				)

			__UpperCAmelCase :   Dict    					=  renew_attention_paths(lowerCAmelCase__      ,				in_mid=lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]      ,				)

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   List[Any]    					=  num_up_blocks - 1 - i

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

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

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

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

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

			__UpperCAmelCase :   Optional[int]    					=  {k.replace("""mid_new_2"""      ,				"""mid_block"""			): v for k, v in new_checkpoint.items()}
			return new_checkpoint


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	Optional[Any]			):

			"""simple docstring"""





			__UpperCAmelCase :   Tuple    					=  {}

			__UpperCAmelCase :   str    					=  checkpoint["""encoder.norm_out.weight"""]
			__UpperCAmelCase :   List[Any]    					=  checkpoint["""encoder.norm_out.bias"""]

			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_in.weight"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_in.bias"""]
			__UpperCAmelCase :   Union[str, Any]    					=  checkpoint["""encoder.conv_out.weight"""]
			__UpperCAmelCase :   int    					=  checkpoint["""encoder.conv_out.bias"""]

			__UpperCAmelCase :   str    					=  checkpoint["""decoder.norm_out.weight"""]
			__UpperCAmelCase :   Optional[int]    					=  checkpoint["""decoder.norm_out.bias"""]

			__UpperCAmelCase :   Dict    					=  checkpoint["""decoder.conv_in.weight"""]
			__UpperCAmelCase :   Dict    					=  checkpoint["""decoder.conv_in.bias"""]
			__UpperCAmelCase :   Tuple    					=  checkpoint["""decoder.conv_out.weight"""]
			__UpperCAmelCase :   str    					=  checkpoint["""decoder.conv_out.bias"""]

			__UpperCAmelCase :   str    					=  len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """down""" in layer}			)
			__UpperCAmelCase :   Tuple    					=  {
			    layer_id: [key for key in checkpoint if f'down.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
			}

			__UpperCAmelCase :   Tuple    					=  len({""".""".join(layer.split("""."""			)[:3]			) for layer in checkpoint if """up""" in layer}			)
			__UpperCAmelCase :   List[Any]    					=  {layer_id: [key for key in checkpoint if f'up.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)}

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Any    					=  (i - 1) // (config["""layers_per_block"""] + 1)

						if any("""downsample""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   Optional[Any]    					=  checkpoint[
									    f'encoder.down.{i}.downsample.conv.weight'
									]
									__UpperCAmelCase :   Union[str, Any]    					=  checkpoint[
									    f'encoder.down.{i}.downsample.conv.bias'
									]

						if any("""block""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   List[Any]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   str    					=  {
									    layer_id: [key for key in down_blocks[i] if f'block.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   List[Any]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__			)

						if any("""attn""" in layer for layer in down_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in down_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   Dict    					=  {
									    layer_id: [key for key in down_blocks[i] if f'attn.{layer_id}' in key]
									    for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""]			):
															__UpperCAmelCase :   Optional[int]    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				config=lowerCAmelCase__			)

			__UpperCAmelCase :   Optional[Any]    					=  [key for key in checkpoint if """mid.block_1""" in key]
			__UpperCAmelCase :   List[Any]    					=  [key for key in checkpoint if """mid.block_2""" in key]
			__UpperCAmelCase :   Dict    					=  [key for key in checkpoint if """mid.attn_1""" in key]

			# Mid new 2
			__UpperCAmelCase :   Any    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]      ,				)

			__UpperCAmelCase :   Optional[Any]    					=  renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]      ,				)

			__UpperCAmelCase :   Tuple    					=  renew_attention_paths(lowerCAmelCase__      ,				in_mid=lowerCAmelCase__			)
			assign_to_checkpoint(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]      ,				)

			for i in range(lowerCAmelCase__			):
						__UpperCAmelCase :   Union[str, Any]    					=  num_up_blocks - 1 - i

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

						if any("""block""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   List[str]    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """block""" in layer}			)
									__UpperCAmelCase :   Union[str, Any]    					=  {
									    layer_id: [key for key in up_blocks[i] if f'block.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_blocks > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Optional[int]    					=  renew_resnet_paths(blocks[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

						if any("""attn""" in layer for layer in up_blocks[i]			):
									__UpperCAmelCase :   int    					=  len(
									    {""".""".join(shave_segments(lowerCAmelCase__      ,				3			).split("""."""			)[:3]			) for layer in up_blocks[i] if """attn""" in layer}			)
									__UpperCAmelCase :   List[str]    					=  {
									    layer_id: [key for key in up_blocks[i] if f'attn.{layer_id}' in key] for layer_id in range(lowerCAmelCase__			)
									}

									if num_attn > 0:
												for j in range(config["""layers_per_block"""] + 1			):
															__UpperCAmelCase :   List[str]    					=  {"""old""": f'up_blocks.{i}', """new""": f'up_blocks.{block_id}'}
															__UpperCAmelCase :   Union[str, Any]    					=  renew_attention_paths(attns[j]			)
															assign_to_checkpoint(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				additional_replacements=[replace_indices]			)

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

			return new_checkpoint


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

					_UpperCamelCase									=      parser.parse_args()
					_UpperCamelCase									=      torch.load(args.checkpoint_path)

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

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

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

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

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

										_UpperCamelCase									=      DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)




