



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase									=      {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					_UpperCamelCase									=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''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)




