
'''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,
)


__SCREAMING_SNAKE_CASE  =			{"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE  =			[
	    "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:
	__SCREAMING_SNAKE_CASE  =			[
	    "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFT5EncoderModel",
	    "TFT5ForConditionalGeneration",
	    "TFT5Model",
	    "TFT5PreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE  =			[
	    "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

	__SCREAMING_SNAKE_CASE  =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






'''simple docstring'''


import argparse

import torch

from diffusers import UNetaDConditionModel
def 				__magic_name__(					UpperCAmelCase__   :					Optional[int]       ,							UpperCAmelCase__   :					Union[str, Any]       ,							UpperCAmelCase__   :					Optional[int]       ,							UpperCAmelCase__   :					Optional[int]=None       ,							UpperCAmelCase__   :					Any=None       ,							UpperCAmelCase__   :					Optional[Any]=None  ):
						assert isinstance(UpperCAmelCase__       ,							UpperCAmelCase__  ), "Paths should be a list of dicts containing 'old' and 'new' keys."

						# Splits the attention layers into three variables.
						if attention_paths_to_split is not None:
												for path, path_map in attention_paths_to_split.items():
																		UpperCamelCase_    :  Union[str, Any]				    =    old_checkpoint[path]
																		UpperCamelCase_    :  Any				    =    old_tensor.shape[0] // 3

																		UpperCamelCase_    :  Union[str, Any]				    =    (-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

																		UpperCamelCase_    :  List[Any]				    =    old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

																		UpperCamelCase_    :  Tuple				    =    query.reshape(UpperCAmelCase__  )
																		UpperCamelCase_    :  List[str]				    =    key.reshape(UpperCAmelCase__  )
																		UpperCamelCase_    :  List[str]				    =    value.reshape(UpperCAmelCase__  )

						for path in paths:
												UpperCamelCase_    :  Union[str, Any]				    =    path["""new"""]

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
												UpperCamelCase_    :  List[str]				    =    old_checkpoint[path["""old"""]]
												UpperCamelCase_    :  List[str]				    =    ["""proj_attn.weight"""]
												UpperCamelCase_    :  int				    =    ["""proj_out.weight""", """proj_in.weight"""]
												if any(k in new_path for k in names  ):
																		UpperCamelCase_    :  Dict				    =    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_    :  Any				    =    weight[:, :, 0]
												else:
																		UpperCamelCase_    :  int				    =    weight
def 				__magic_name__(					UpperCAmelCase__   :					Tuple       ,							UpperCAmelCase__   :					Any=0  ):
						UpperCamelCase_    :  Any				    =    []
						for old_item in old_list:
												UpperCamelCase_    :  Tuple				    =    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 				__magic_name__(					UpperCAmelCase__   :					Any       ,							UpperCAmelCase__   :					int=1  ):
						if n_shave_prefix_segments >= 0:
												return ".".join(path.split("""."""  )[n_shave_prefix_segments:]  )
						else:
												return ".".join(path.split("""."""  )[:n_shave_prefix_segments]  )
def 				__magic_name__(					UpperCAmelCase__   :					str       ,							UpperCAmelCase__   :					Dict=0  ):
						UpperCamelCase_    :  str				    =    []
						for old_item in old_list:
												mapping.append({"""old""": old_item, """new""": old_item}  )

						return mapping
def 				__magic_name__(					UpperCAmelCase__   :					List[Any]       ,							UpperCAmelCase__   :					Any=0  ):
						UpperCamelCase_    :  List[str]				    =    []
						for old_item in old_list:
												UpperCamelCase_    :  Tuple				    =    old_item.replace("""in_layers.0"""       ,							"""norm1"""  )
												UpperCamelCase_    :  List[str]				    =    new_item.replace("""in_layers.2"""       ,							"""conv1"""  )

												UpperCamelCase_    :  Any				    =    new_item.replace("""out_layers.0"""       ,							"""norm2"""  )
												UpperCamelCase_    :  Optional[Any]				    =    new_item.replace("""out_layers.3"""       ,							"""conv2"""  )

												UpperCamelCase_    :  int				    =    new_item.replace("""emb_layers.1"""       ,							"""time_emb_proj"""  )
												UpperCamelCase_    :  int				    =    new_item.replace("""skip_connection"""       ,							"""conv_shortcut"""  )

												UpperCamelCase_    :  Union[str, Any]				    =    shave_segments(UpperCAmelCase__       ,							n_shave_prefix_segments=UpperCAmelCase__  )

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

						return mapping
def 				__magic_name__(					UpperCAmelCase__   :					List[str]       ,							UpperCAmelCase__   :					Union[str, Any]       ,							UpperCAmelCase__   :					Union[str, Any]=None       ,							UpperCAmelCase__   :					int=False  ):
						UpperCamelCase_    :  List[str]				    =    {}
						UpperCamelCase_    :  Dict				    =    list(checkpoint.keys()  )

						UpperCamelCase_    :  int				    =    """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  ) > 100 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_    :  Optional[int]				    =    """model_ema.""" + """""".join(key.split("""."""  )[1:]  )
																								UpperCamelCase_    :  Dict				    =    checkpoint.pop(UpperCAmelCase__  )
						else:
												if sum(k.startswith("""model_ema"""  ) for k in keys  ) > 100:
																		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_    :  Optional[Any]				    =    checkpoint.pop(UpperCAmelCase__  )

						UpperCamelCase_    :  str				    =    {}

						UpperCamelCase_    :  Union[str, Any]				    =    unet_state_dict["""time_embed.0.weight"""]
						UpperCamelCase_    :  Optional[int]				    =    unet_state_dict["""time_embed.0.bias"""]
						UpperCamelCase_    :  List[Any]				    =    unet_state_dict["""time_embed.2.weight"""]
						UpperCamelCase_    :  Dict				    =    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_    :  Optional[Any]				    =    unet_state_dict["""label_emb.0.0.weight"""]
												UpperCamelCase_    :  Any				    =    unet_state_dict["""label_emb.0.0.bias"""]
												UpperCamelCase_    :  Union[str, Any]				    =    unet_state_dict["""label_emb.0.2.weight"""]
												UpperCamelCase_    :  Any				    =    unet_state_dict["""label_emb.0.2.bias"""]
						else:
												raise NotImplementedError(f"""Not implemented `class_embed_type`: {config['class_embed_type']}"""  )

						UpperCamelCase_    :  Optional[Any]				    =    unet_state_dict["""input_blocks.0.0.weight"""]
						UpperCamelCase_    :  Dict				    =    unet_state_dict["""input_blocks.0.0.bias"""]

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

						UpperCamelCase_    :  List[Any]				    =    unet_state_dict["""out.0.weight"""]
						UpperCamelCase_    :  Tuple				    =    unet_state_dict["""out.0.bias"""]
						UpperCamelCase_    :  Any				    =    unet_state_dict["""out.2.weight"""]
						UpperCamelCase_    :  str				    =    unet_state_dict["""out.2.bias"""]

						# Retrieves the keys for the input blocks only
						UpperCamelCase_    :  str				    =    len({""".""".join(layer.split("""."""  )[:2]  ) for layer in unet_state_dict if """input_blocks""" in layer}  )
						UpperCamelCase_    :  str				    =    {
						    layer_id: [key for key in unet_state_dict if f"""input_blocks.{layer_id}""" in key]
						    for layer_id in range(UpperCAmelCase__  )
						}

						# Retrieves the keys for the middle blocks only
						UpperCamelCase_    :  Optional[int]				    =    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(UpperCAmelCase__  )
						}

						# Retrieves the keys for the output blocks only
						UpperCamelCase_    :  Dict				    =    len({""".""".join(layer.split("""."""  )[:2]  ) for layer in unet_state_dict if """output_blocks""" in layer}  )
						UpperCamelCase_    :  List[Any]				    =    {
						    layer_id: [key for key in unet_state_dict if f"""output_blocks.{layer_id}""" in key]
						    for layer_id in range(UpperCAmelCase__  )
						}

						for i in range(1       ,							UpperCAmelCase__  ):
												UpperCamelCase_    :  Tuple				    =    (i - 1) // (config["""layers_per_block"""] + 1)
												UpperCamelCase_    :  List[Any]				    =    (i - 1) % (config["""layers_per_block"""] + 1)

												UpperCamelCase_    :  List[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_    :  int				    =    [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]
												UpperCamelCase_    :  int				    =    [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_    :  int				    =    unet_state_dict.pop(
																		    f"""input_blocks.{i}.op.weight"""  )
																		UpperCamelCase_    :  Union[str, Any]				    =    unet_state_dict.pop(
																		    f"""input_blocks.{i}.op.bias"""  )

												UpperCamelCase_    :  Optional[int]				    =    renew_resnet_paths(UpperCAmelCase__  )
												UpperCamelCase_    :  Any				    =    {"""old""": f"""input_blocks.{i}.0""", """new""": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(
												    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

												UpperCamelCase_    :  Tuple				    =    [key for key in resnets if """temopral_conv""" in key]
												UpperCamelCase_    :  List[Any]				    =    renew_temp_conv_paths(UpperCAmelCase__  )
												UpperCamelCase_    :  Dict				    =    {
												    """old""": f"""input_blocks.{i}.0.temopral_conv""",
												    """new""": f"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
												}
												assign_to_checkpoint(
												    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

												if len(UpperCAmelCase__  ):
																		UpperCamelCase_    :  Any				    =    renew_attention_paths(UpperCAmelCase__  )
																		UpperCamelCase_    :  Optional[int]				    =    {"""old""": f"""input_blocks.{i}.1""", """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
																		assign_to_checkpoint(
																		    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

												if len(UpperCAmelCase__  ):
																		UpperCamelCase_    :  List[str]				    =    renew_attention_paths(UpperCAmelCase__  )
																		UpperCamelCase_    :  Optional[Any]				    =    {
																		    """old""": f"""input_blocks.{i}.2""",
																		    """new""": f"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																		}
																		assign_to_checkpoint(
																		    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

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

						UpperCamelCase_    :  Any				    =    renew_resnet_paths(UpperCAmelCase__  )
						UpperCamelCase_    :  Union[str, Any]				    =    {"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
						assign_to_checkpoint(
						    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							config=UpperCAmelCase__       ,							additional_replacements=[meta_path]  )

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

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

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

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

						UpperCamelCase_    :  Optional[int]				    =    renew_attention_paths(UpperCAmelCase__  )
						UpperCamelCase_    :  Tuple				    =    {"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
						assign_to_checkpoint(
						    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

						for i in range(UpperCAmelCase__  ):
												UpperCamelCase_    :  Optional[Any]				    =    i // (config["""layers_per_block"""] + 1)
												UpperCamelCase_    :  List[str]				    =    i % (config["""layers_per_block"""] + 1)
												UpperCamelCase_    :  List[str]				    =    [shave_segments(UpperCAmelCase__       ,							2  ) for name in output_blocks[i]]
												UpperCamelCase_    :  List[Any]				    =    {}

												for layer in output_block_layers:
																		UpperCamelCase_    ,UpperCamelCase_    :  List[Any]				    =    layer.split("""."""  )[0], shave_segments(UpperCAmelCase__       ,							1  )
																		if layer_id in output_block_list:
																								output_block_list[layer_id].append(UpperCAmelCase__  )
																		else:
																								UpperCamelCase_    :  Optional[Any]				    =    [layer_name]

												if len(UpperCAmelCase__  ) > 1:
																		UpperCamelCase_    :  Union[str, Any]				    =    [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
																		UpperCamelCase_    :  Optional[int]				    =    [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]
																		UpperCamelCase_    :  Dict				    =    [key for key in output_blocks[i] if f"""output_blocks.{i}.2""" in key]

																		UpperCamelCase_    :  str				    =    renew_resnet_paths(UpperCAmelCase__  )
																		UpperCamelCase_    :  Dict				    =    renew_resnet_paths(UpperCAmelCase__  )

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

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

																		UpperCamelCase_    :  str				    =    {k: sorted(UpperCAmelCase__  ) for k, v in output_block_list.items()}
																		if ["conv.bias", "conv.weight"] in output_block_list.values():
																								UpperCamelCase_    :  Union[str, Any]				    =    list(output_block_list.values()  ).index(["""conv.bias""", """conv.weight"""]  )
																								UpperCamelCase_    :  Optional[int]				    =    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(UpperCAmelCase__  ) == 2:
																														UpperCamelCase_    :  Tuple				    =    []

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

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

																		UpperCamelCase_    :  Optional[Any]				    =    [l for l in output_block_layers if """temopral_conv""" in l]
																		for path in temopral_conv_paths:
																								UpperCamelCase_    :  Any				    =    path.split("""temopral_conv."""  )[-1]
																								UpperCamelCase_    :  Union[str, Any]				    =    """.""".join(["""output_blocks""", str(UpperCAmelCase__  ), str(UpperCAmelCase__  ), """temopral_conv""", pruned_path]  )
																								UpperCamelCase_    :  Dict				    =    """.""".join(["""up_blocks""", str(UpperCAmelCase__  ), """temp_convs""", str(UpperCAmelCase__  ), pruned_path]  )
																								UpperCamelCase_    :  int				    =    unet_state_dict[old_path]

						return new_checkpoint


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE  =			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.")
	__SCREAMING_SNAKE_CASE  =			parser.parse_args()

	__SCREAMING_SNAKE_CASE  =			torch.load(args.checkpoint_path, map_location="cpu")
	__SCREAMING_SNAKE_CASE  =			UNetaDConditionModel()

	__SCREAMING_SNAKE_CASE  =			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

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




