
import argparse

import torch

from diffusers import UNetaDConditionModel




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE=None      ,	_SCREAMING_SNAKE_CASE=None      ,	_SCREAMING_SNAKE_CASE=None  )   ->      Union[str, Any]:

							"""simple docstring"""






							assert isinstance(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  ), "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():
																					_UpperCAmelCase  :			List[Any]	       =		old_checkpoint[path]
																					_UpperCAmelCase  :			Any	       =		old_tensor.shape[0] // 3

																					_UpperCAmelCase  :			Tuple	       =		(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

																					_UpperCAmelCase  :			Optional[Any]	       =		old_tensor.shape[0] // config["num_head_channels"] // 3

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

																					_UpperCAmelCase  :			Tuple	       =		query.reshape(_SCREAMING_SNAKE_CASE  )
																					_UpperCAmelCase  :			Union[str, Any]	       =		key.reshape(_SCREAMING_SNAKE_CASE  )
																					_UpperCAmelCase  :			Dict	       =		value.reshape(_SCREAMING_SNAKE_CASE  )

							for path in paths:
														_UpperCAmelCase  :			Tuple	       =		path["new"]

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
														_UpperCAmelCase  :			Union[str, Any]	       =		old_checkpoint[path["old"]]
														_UpperCAmelCase  :			Optional[int]	       =		["proj_attn.weight"]
														_UpperCAmelCase  :			Any	       =		["proj_out.weight", "proj_in.weight"]
														if any(k in new_path for k in names  ):
																					_UpperCAmelCase  :			Optional[Any]	       =		weight[:, :, 0]
														elif any(k in new_path for k in names_a  ) and len(weight.shape  ) > 2 and ".attentions." not in new_path:
																					_UpperCAmelCase  :			int	       =		weight[:, :, 0]
														else:
																					_UpperCAmelCase  :			int	       =		weight




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE=0  )   ->      str:

							"""simple docstring"""






							_UpperCAmelCase  :			Tuple	       =		[]
							for old_item in old_list:
														_UpperCAmelCase  :			List[Any]	       =		old_item

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

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

														#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

							return mapping




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE=1  )   ->      Dict:

							"""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 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE=0  )   ->      List[str]:

							"""simple docstring"""






							_UpperCAmelCase  :			int	       =		[]
							for old_item in old_list:
														mapping.append({"old": old_item, "new": old_item}  )

							return mapping




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE=0  )   ->      Optional[int]:

							"""simple docstring"""






							_UpperCAmelCase  :			Dict	       =		[]
							for old_item in old_list:
														_UpperCAmelCase  :			str	       =		old_item.replace("in_layers.0"      ,	"norm1"  )
														_UpperCAmelCase  :			Any	       =		new_item.replace("in_layers.2"      ,	"conv1"  )

														_UpperCAmelCase  :			Dict	       =		new_item.replace("out_layers.0"      ,	"norm2"  )
														_UpperCAmelCase  :			List[str]	       =		new_item.replace("out_layers.3"      ,	"conv2"  )

														_UpperCAmelCase  :			int	       =		new_item.replace("emb_layers.1"      ,	"time_emb_proj"  )
														_UpperCAmelCase  :			Optional[int]	       =		new_item.replace("skip_connection"      ,	"conv_shortcut"  )

														_UpperCAmelCase  :			Optional[Any]	       =		shave_segments(_SCREAMING_SNAKE_CASE      ,	n_shave_prefix_segments=_SCREAMING_SNAKE_CASE  )

														if "temopral_conv" not in old_item:
																					mapping.append({"old": old_item, "new": new_item}  )

							return mapping




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE=None      ,	_SCREAMING_SNAKE_CASE=False  )   ->      Optional[Any]:

							"""simple docstring"""






							_UpperCAmelCase  :			List[Any]	       =		{}
							_UpperCAmelCase  :			Optional[int]	       =		list(checkpoint.keys()  )

							_UpperCAmelCase  :			Tuple	       =		"model.diffusion_model."

							# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
							if sum(k.startswith("model_ema"  ) for k in keys  ) > 1_0_0 and extract_ema:
														print(F"""Checkpoint {path} has both EMA and non-EMA weights."""  )
														print(
														    "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
														    " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."  )
														for key in keys:
																					if key.startswith("model.diffusion_model"  ):
																												_UpperCAmelCase  :			Dict	       =		"model_ema." + "".join(key.split("."  )[1:]  )
																												_UpperCAmelCase  :			List[Any]	       =		checkpoint.pop(_SCREAMING_SNAKE_CASE  )
							else:
														if sum(k.startswith("model_ema"  ) for k in keys  ) > 1_0_0:
																					print(
																					    "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
																					    " weights (usually better for inference), please make sure to add the `--extract_ema` flag."  )

														for key in keys:
																					_UpperCAmelCase  :			str	       =		checkpoint.pop(_SCREAMING_SNAKE_CASE  )

							_UpperCAmelCase  :			List[str]	       =		{}

							_UpperCAmelCase  :			Optional[Any]	       =		unet_state_dict["time_embed.0.weight"]
							_UpperCAmelCase  :			str	       =		unet_state_dict["time_embed.0.bias"]
							_UpperCAmelCase  :			Any	       =		unet_state_dict["time_embed.2.weight"]
							_UpperCAmelCase  :			Union[str, Any]	       =		unet_state_dict["time_embed.2.bias"]

							if config["class_embed_type"] is None:
														# No parameters to port
														...
							elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
														_UpperCAmelCase  :			List[Any]	       =		unet_state_dict["label_emb.0.0.weight"]
														_UpperCAmelCase  :			Optional[int]	       =		unet_state_dict["label_emb.0.0.bias"]
														_UpperCAmelCase  :			Union[str, Any]	       =		unet_state_dict["label_emb.0.2.weight"]
														_UpperCAmelCase  :			List[Any]	       =		unet_state_dict["label_emb.0.2.bias"]
							else:
														raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}"""  )

							_UpperCAmelCase  :			str	       =		unet_state_dict["input_blocks.0.0.weight"]
							_UpperCAmelCase  :			Optional[int]	       =		unet_state_dict["input_blocks.0.0.bias"]

							_UpperCAmelCase  :			Tuple	       =		[v for v in unet_state_dict if v.startswith("input_blocks.0.1"  )]
							_UpperCAmelCase  :			Any	       =		renew_attention_paths(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			Tuple	       =		{"old": "input_blocks.0.1", "new": "transformer_in"}
							assign_to_checkpoint(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

							_UpperCAmelCase  :			Union[str, Any]	       =		unet_state_dict["out.0.weight"]
							_UpperCAmelCase  :			Dict	       =		unet_state_dict["out.0.bias"]
							_UpperCAmelCase  :			int	       =		unet_state_dict["out.2.weight"]
							_UpperCAmelCase  :			str	       =		unet_state_dict["out.2.bias"]

							# Retrieves the keys for the input blocks only
							_UpperCAmelCase  :			Tuple	       =		len({".".join(layer.split("."  )[:2]  ) for layer in unet_state_dict if "input_blocks" in layer}  )
							_UpperCAmelCase  :			Optional[Any]	       =		{
							    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
							    for layer_id in range(_SCREAMING_SNAKE_CASE  )
							}

							# Retrieves the keys for the middle blocks only
							_UpperCAmelCase  :			List[Any]	       =		len({".".join(layer.split("."  )[:2]  ) for layer in unet_state_dict if "middle_block" in layer}  )
							_UpperCAmelCase  :			Optional[Any]	       =		{
							    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
							    for layer_id in range(_SCREAMING_SNAKE_CASE  )
							}

							# Retrieves the keys for the output blocks only
							_UpperCAmelCase  :			Optional[int]	       =		len({".".join(layer.split("."  )[:2]  ) for layer in unet_state_dict if "output_blocks" in layer}  )
							_UpperCAmelCase  :			Dict	       =		{
							    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
							    for layer_id in range(_SCREAMING_SNAKE_CASE  )
							}

							for i in range(1      ,	_SCREAMING_SNAKE_CASE  ):
														_UpperCAmelCase  :			List[str]	       =		(i - 1) // (config["layers_per_block"] + 1)
														_UpperCAmelCase  :			Any	       =		(i - 1) % (config["layers_per_block"] + 1)

														_UpperCAmelCase  :			Union[str, Any]	       =		[
														    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
														]
														_UpperCAmelCase  :			Any	       =		[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
														_UpperCAmelCase  :			List[Any]	       =		[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

														if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
																					_UpperCAmelCase  :			Dict	       =		unet_state_dict.pop(
																					    F"""input_blocks.{i}.op.weight"""  )
																					_UpperCAmelCase  :			Tuple	       =		unet_state_dict.pop(
																					    F"""input_blocks.{i}.op.bias"""  )

														_UpperCAmelCase  :			str	       =		renew_resnet_paths(_SCREAMING_SNAKE_CASE  )
														_UpperCAmelCase  :			Optional[Any]	       =		{"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
														assign_to_checkpoint(
														    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

														_UpperCAmelCase  :			List[str]	       =		[key for key in resnets if "temopral_conv" in key]
														_UpperCAmelCase  :			Union[str, Any]	       =		renew_temp_conv_paths(_SCREAMING_SNAKE_CASE  )
														_UpperCAmelCase  :			str	       =		{
														    "old": F"""input_blocks.{i}.0.temopral_conv""",
														    "new": F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
														}
														assign_to_checkpoint(
														    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

														if len(_SCREAMING_SNAKE_CASE  ):
																					_UpperCAmelCase  :			Union[str, Any]	       =		renew_attention_paths(_SCREAMING_SNAKE_CASE  )
																					_UpperCAmelCase  :			int	       =		{"old": F"""input_blocks.{i}.1""", "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
																					assign_to_checkpoint(
																					    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

														if len(_SCREAMING_SNAKE_CASE  ):
																					_UpperCAmelCase  :			Optional[int]	       =		renew_attention_paths(_SCREAMING_SNAKE_CASE  )
																					_UpperCAmelCase  :			Tuple	       =		{
																					    "old": F"""input_blocks.{i}.2""",
																					    "new": F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																					}
																					assign_to_checkpoint(
																					    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

							_UpperCAmelCase  :			int	       =		middle_blocks[0]
							_UpperCAmelCase  :			Optional[int]	       =		[key for key in resnet_a if "temopral_conv" in key]
							_UpperCAmelCase  :			Optional[Any]	       =		middle_blocks[1]
							_UpperCAmelCase  :			List[Any]	       =		middle_blocks[2]
							_UpperCAmelCase  :			Optional[int]	       =		middle_blocks[3]
							_UpperCAmelCase  :			Union[str, Any]	       =		[key for key in resnet_a if "temopral_conv" in key]

							_UpperCAmelCase  :			Tuple	       =		renew_resnet_paths(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			List[str]	       =		{"old": "middle_block.0", "new": "mid_block.resnets.0"}
							assign_to_checkpoint(
							    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	config=_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]  )

							_UpperCAmelCase  :			str	       =		renew_temp_conv_paths(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			Optional[int]	       =		{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
							assign_to_checkpoint(
							    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	config=_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]  )

							_UpperCAmelCase  :			Optional[int]	       =		renew_resnet_paths(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			Optional[Any]	       =		{"old": "middle_block.3", "new": "mid_block.resnets.1"}
							assign_to_checkpoint(
							    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	config=_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]  )

							_UpperCAmelCase  :			List[str]	       =		renew_temp_conv_paths(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			Optional[int]	       =		{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
							assign_to_checkpoint(
							    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	config=_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]  )

							_UpperCAmelCase  :			int	       =		renew_attention_paths(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			List[str]	       =		{"old": "middle_block.1", "new": "mid_block.attentions.0"}
							assign_to_checkpoint(
							    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

							_UpperCAmelCase  :			Optional[Any]	       =		renew_attention_paths(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			str	       =		{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
							assign_to_checkpoint(
							    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

							for i in range(_SCREAMING_SNAKE_CASE  ):
														_UpperCAmelCase  :			List[Any]	       =		i // (config["layers_per_block"] + 1)
														_UpperCAmelCase  :			str	       =		i % (config["layers_per_block"] + 1)
														_UpperCAmelCase  :			Dict	       =		[shave_segments(_SCREAMING_SNAKE_CASE      ,	2  ) for name in output_blocks[i]]
														_UpperCAmelCase  :			Tuple	       =		{}

														for layer in output_block_layers:
																					_UpperCAmelCase       ,_UpperCAmelCase  :			List[str]	       =		layer.split("."  )[0], shave_segments(_SCREAMING_SNAKE_CASE      ,	1  )
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE  )
																					else:
																												_UpperCAmelCase  :			Dict	       =		[layer_name]

														if len(_SCREAMING_SNAKE_CASE  ) > 1:
																					_UpperCAmelCase  :			Union[str, Any]	       =		[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
																					_UpperCAmelCase  :			Optional[Any]	       =		[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
																					_UpperCAmelCase  :			Any	       =		[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

																					_UpperCAmelCase  :			int	       =		renew_resnet_paths(_SCREAMING_SNAKE_CASE  )
																					_UpperCAmelCase  :			int	       =		renew_resnet_paths(_SCREAMING_SNAKE_CASE  )

																					_UpperCAmelCase  :			str	       =		{"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																					assign_to_checkpoint(
																					    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

																					_UpperCAmelCase  :			Optional[Any]	       =		[key for key in resnets if "temopral_conv" in key]
																					_UpperCAmelCase  :			Optional[int]	       =		renew_temp_conv_paths(_SCREAMING_SNAKE_CASE  )
																					_UpperCAmelCase  :			str	       =		{
																					    "old": F"""output_blocks.{i}.0.temopral_conv""",
																					    "new": F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
																					}
																					assign_to_checkpoint(
																					    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

																					_UpperCAmelCase  :			Tuple	       =		{k: sorted(_SCREAMING_SNAKE_CASE  ) for k, v in output_block_list.items()}
																					if ["conv.bias", "conv.weight"] in output_block_list.values():
																												_UpperCAmelCase  :			Optional[Any]	       =		list(output_block_list.values()  ).index(["conv.bias", "conv.weight"]  )
																												_UpperCAmelCase  :			Optional[Any]	       =		unet_state_dict[
																												    F"""output_blocks.{i}.{index}.conv.weight"""
																												]
																												_UpperCAmelCase  :			Optional[Any]	       =		unet_state_dict[
																												    F"""output_blocks.{i}.{index}.conv.bias"""
																												]

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

																					if len(_SCREAMING_SNAKE_CASE  ):
																												_UpperCAmelCase  :			int	       =		renew_attention_paths(_SCREAMING_SNAKE_CASE  )
																												_UpperCAmelCase  :			int	       =		{
																												    "old": F"""output_blocks.{i}.1""",
																												    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																												}
																												assign_to_checkpoint(
																												    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )

																					if len(_SCREAMING_SNAKE_CASE  ):
																												_UpperCAmelCase  :			Optional[int]	       =		renew_attention_paths(_SCREAMING_SNAKE_CASE  )
																												_UpperCAmelCase  :			Optional[Any]	       =		{
																												    "old": F"""output_blocks.{i}.2""",
																												    "new": F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																												}
																												assign_to_checkpoint(
																												    _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	additional_replacements=[meta_path]      ,	config=_SCREAMING_SNAKE_CASE  )
														else:
																					_UpperCAmelCase  :			Optional[Any]	       =		renew_resnet_paths(_SCREAMING_SNAKE_CASE      ,	n_shave_prefix_segments=1  )
																					for path in resnet_0_paths:
																												_UpperCAmelCase  :			Any	       =		".".join(["output_blocks", str(_SCREAMING_SNAKE_CASE  ), path["old"]]  )
																												_UpperCAmelCase  :			Union[str, Any]	       =		".".join(["up_blocks", str(_SCREAMING_SNAKE_CASE  ), "resnets", str(_SCREAMING_SNAKE_CASE  ), path["new"]]  )
																												_UpperCAmelCase  :			str	       =		unet_state_dict[old_path]

																					_UpperCAmelCase  :			List[str]	       =		[l for l in output_block_layers if "temopral_conv" in l]
																					for path in temopral_conv_paths:
																												_UpperCAmelCase  :			int	       =		path.split("temopral_conv."  )[-1]
																												_UpperCAmelCase  :			int	       =		".".join(["output_blocks", str(_SCREAMING_SNAKE_CASE  ), str(_SCREAMING_SNAKE_CASE  ), "temopral_conv", pruned_path]  )
																												_UpperCAmelCase  :			Tuple	       =		".".join(["up_blocks", str(_SCREAMING_SNAKE_CASE  ), "temp_convs", str(_SCREAMING_SNAKE_CASE  ), pruned_path]  )
																												_UpperCAmelCase  :			Dict	       =		unet_state_dict[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('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
				__lowerCamelCase								=					parser.parse_args()

				__lowerCamelCase								=					torch.load(args.checkpoint_path, map_location='cpu')
				__lowerCamelCase								=					UNetaDConditionModel()

				__lowerCamelCase								=					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

				__lowerCamelCase								=					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
				__lowerCamelCase								=					set(converted_ckpt.keys()) - set(unet.state_dict().keys())

				assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

				# load state_dict
				unet.load_state_dict(converted_ckpt)

				unet.save_pretrained(args.dump_path)

				# -- finish converting the unet --






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						dtype=torch.float	,						)
													# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
													# camembert.eval()
													# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

													self.assertTrue(torch.allclose(output[:, :3, :3]	,						_lowerCamelCase	,						atol=1E-4			)			)




