import argparse

import torch

from diffusers import UNetaDConditionModel







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Dict		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]		,							__SCREAMING_SNAKE_CASE       :					Tuple		,							__SCREAMING_SNAKE_CASE       :					Optional[int]=None		,							__SCREAMING_SNAKE_CASE       :					Any=None		,							__SCREAMING_SNAKE_CASE       :					Dict=None   ):







    '''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():
            __snake_case       :				Optional[Any] 			=    old_checkpoint[path]
            __snake_case       :				Optional[Any] 			=    old_tensor.shape[0] // 3

            __snake_case       :				str 			=    (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

            __snake_case       :				Tuple 			=    old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

            __snake_case       :				Union[str, Any] 			=    query.reshape(__SCREAMING_SNAKE_CASE   )
            __snake_case       :				Dict 			=    key.reshape(__SCREAMING_SNAKE_CASE   )
            __snake_case       :				List[str] 			=    value.reshape(__SCREAMING_SNAKE_CASE   )

    for path in paths:
        __snake_case       :				Optional[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:
                __snake_case       :				int 			=    new_path.replace(replacement["""old"""]		,							replacement["""new"""]   )

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







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					Dict=0   ):







    '''simple docstring'''
    __snake_case       :				Tuple 			=    []
    for old_item in old_list:
        __snake_case       :				Optional[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       :					str		,							__SCREAMING_SNAKE_CASE       :					List[str]=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 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					Tuple=0   ):







    '''simple docstring'''
    __snake_case       :				Union[str, Any] 			=    []
    for old_item in old_list:
        mapping.append({"""old""": old_item, """new""": old_item}   )

    return mapping







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]=0   ):







    '''simple docstring'''
    __snake_case       :				Any 			=    []
    for old_item in old_list:
        __snake_case       :				Any 			=    old_item.replace("""in_layers.0"""		,							"""norm1"""   )
        __snake_case       :				List[str] 			=    new_item.replace("""in_layers.2"""		,							"""conv1"""   )

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

        __snake_case       :				Tuple 			=    new_item.replace("""emb_layers.1"""		,							"""time_emb_proj"""   )
        __snake_case       :				Dict 			=    new_item.replace("""skip_connection"""		,							"""conv_shortcut"""   )

        __snake_case       :				int 			=    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       :					Optional[Any]		,							__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					Any=None		,							__SCREAMING_SNAKE_CASE       :					List[Any]=False   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    {}
    __snake_case       :				str 			=    list(checkpoint.keys()   )

    __snake_case       :				str 			=    """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"""   ):
                __snake_case       :				Optional[Any] 			=    """model_ema.""" + """""".join(key.split("""."""   )[1:]   )
                __snake_case       :				Tuple 			=    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:
            __snake_case       :				Optional[int] 			=    checkpoint.pop(__SCREAMING_SNAKE_CASE   )

    __snake_case       :				Union[str, Any] 			=    {}

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

    __snake_case       :				Optional[int] 			=    unet_state_dict["""input_blocks.0.0.weight"""]
    __snake_case       :				List[Any] 			=    unet_state_dict["""input_blocks.0.0.bias"""]

    __snake_case       :				Any 			=    [v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""   )]
    __snake_case       :				str 			=    renew_attention_paths(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Union[str, Any] 			=    {"""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   )

    __snake_case       :				Optional[Any] 			=    unet_state_dict["""out.0.weight"""]
    __snake_case       :				List[Any] 			=    unet_state_dict["""out.0.bias"""]
    __snake_case       :				Any 			=    unet_state_dict["""out.2.weight"""]
    __snake_case       :				int 			=    unet_state_dict["""out.2.bias"""]

    # Retrieves the keys for the input blocks only
    __snake_case       :				int 			=    len({""".""".join(layer.split("""."""   )[:2]   ) for layer in unet_state_dict if """input_blocks""" in layer}   )
    __snake_case       :				Dict 			=    {
        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
    __snake_case       :				Any 			=    len({""".""".join(layer.split("""."""   )[:2]   ) for layer in unet_state_dict if """middle_block""" in layer}   )
    __snake_case       :				Optional[int] 			=    {
        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
    __snake_case       :				Dict 			=    len({""".""".join(layer.split("""."""   )[:2]   ) for layer in unet_state_dict if """output_blocks""" in layer}   )
    __snake_case       :				int 			=    {
        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   ):
        __snake_case       :				int 			=    (i - 1) // (config["""layers_per_block"""] + 1)
        __snake_case       :				int 			=    (i - 1) % (config["""layers_per_block"""] + 1)

        __snake_case       :				Optional[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
        ]
        __snake_case       :				Tuple 			=    [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
        __snake_case       :				Dict 			=    [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:
            __snake_case       :				Any 			=    unet_state_dict.pop(
                F'''input_blocks.{i}.op.weight'''   )
            __snake_case       :				Any 			=    unet_state_dict.pop(
                F'''input_blocks.{i}.op.bias'''   )

        __snake_case       :				Tuple 			=    renew_resnet_paths(__SCREAMING_SNAKE_CASE   )
        __snake_case       :				List[str] 			=    {"""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   )

        __snake_case       :				str 			=    [key for key in resnets if """temopral_conv""" in key]
        __snake_case       :				int 			=    renew_temp_conv_paths(__SCREAMING_SNAKE_CASE   )
        __snake_case       :				Any 			=    {
            """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   ):
            __snake_case       :				Dict 			=    renew_attention_paths(__SCREAMING_SNAKE_CASE   )
            __snake_case       :				Any 			=    {"""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   ):
            __snake_case       :				Optional[Any] 			=    renew_attention_paths(__SCREAMING_SNAKE_CASE   )
            __snake_case       :				List[str] 			=    {
                """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   )

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

    __snake_case       :				List[Any] 			=    renew_resnet_paths(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Union[str, Any] 			=    {"""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]   )

    __snake_case       :				Dict 			=    renew_temp_conv_paths(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				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]   )

    __snake_case       :				Tuple 			=    renew_resnet_paths(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				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]   )

    __snake_case       :				Optional[int] 			=    renew_temp_conv_paths(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				List[Any] 			=    {"""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]   )

    __snake_case       :				Optional[int] 			=    renew_attention_paths(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Optional[Any] 			=    {"""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   )

    __snake_case       :				Tuple 			=    renew_attention_paths(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Optional[int] 			=    {"""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   ):
        __snake_case       :				int 			=    i // (config["""layers_per_block"""] + 1)
        __snake_case       :				int 			=    i % (config["""layers_per_block"""] + 1)
        __snake_case       :				Union[str, Any] 			=    [shave_segments(__SCREAMING_SNAKE_CASE		,							2   ) for name in output_blocks[i]]
        __snake_case       :				str 			=    {}

        for layer in output_block_layers:
            __snake_case			, __snake_case       :				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:
                __snake_case       :				Optional[Any] 			=    [layer_name]

        if len(__SCREAMING_SNAKE_CASE   ) > 1:
            __snake_case       :				Any 			=    [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
            __snake_case       :				Tuple 			=    [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
            __snake_case       :				Any 			=    [key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

            __snake_case       :				str 			=    renew_resnet_paths(__SCREAMING_SNAKE_CASE   )
            __snake_case       :				Union[str, Any] 			=    renew_resnet_paths(__SCREAMING_SNAKE_CASE   )

            __snake_case       :				Optional[Any] 			=    {"""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   )

            __snake_case       :				List[Any] 			=    [key for key in resnets if """temopral_conv""" in key]
            __snake_case       :				List[Any] 			=    renew_temp_conv_paths(__SCREAMING_SNAKE_CASE   )
            __snake_case       :				int 			=    {
                """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   )

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

                # Clear attentions as they have been attributed above.
                if len(__SCREAMING_SNAKE_CASE   ) == 2:
                    __snake_case       :				int 			=    []

            if len(__SCREAMING_SNAKE_CASE   ):
                __snake_case       :				Tuple 			=    renew_attention_paths(__SCREAMING_SNAKE_CASE   )
                __snake_case       :				Any 			=    {
                    """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   ):
                __snake_case       :				Tuple 			=    renew_attention_paths(__SCREAMING_SNAKE_CASE   )
                __snake_case       :				Union[str, 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:
            __snake_case       :				int 			=    renew_resnet_paths(__SCREAMING_SNAKE_CASE		,							n_shave_prefix_segments=1   )
            for path in resnet_0_paths:
                __snake_case       :				List[str] 			=    """.""".join(["""output_blocks""", str(__SCREAMING_SNAKE_CASE   ), path["""old"""]]   )
                __snake_case       :				Union[str, Any] 			=    """.""".join(["""up_blocks""", str(__SCREAMING_SNAKE_CASE   ), """resnets""", str(__SCREAMING_SNAKE_CASE   ), path["""new"""]]   )
                __snake_case       :				Any 			=    unet_state_dict[old_path]

            __snake_case       :				Optional[Any] 			=    [l for l in output_block_layers if """temopral_conv""" in l]
            for path in temopral_conv_paths:
                __snake_case       :				Tuple 			=    path.split("""temopral_conv."""   )[-1]
                __snake_case       :				Dict 			=    """.""".join(["""output_blocks""", str(__SCREAMING_SNAKE_CASE   ), str(__SCREAMING_SNAKE_CASE   ), """temopral_conv""", pruned_path]   )
                __snake_case       :				Any 			=    """.""".join(["""up_blocks""", str(__SCREAMING_SNAKE_CASE   ), """temp_convs""", str(__SCREAMING_SNAKE_CASE   ), pruned_path]   )
                __snake_case       :				List[Any] 			=    unet_state_dict[old_path]

    return new_checkpoint


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

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

     lowercase_  							=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

     lowercase_  							=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
     lowercase_  							=			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 torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				Any 			=    (inputs > threshold).type(inputs.type()			)
           if mask.sum() < nb_min:
               # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				Optional[int] 			=    mask.flatten()
           __snake_case       :				Dict 			=    0
           __snake_case       :				Optional[Any] 			=    1
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				List[Any] 			=    mask.flatten()
           __snake_case       :				int 			=    0
           __snake_case       :				Optional[int] 			=    1
           return mask



