

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		List[str]=0       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for old_item in old_list:
        SCREAMING_SNAKE_CASE_									=						old_item.replace('in_layers.0' ,   'norm1'       )
        SCREAMING_SNAKE_CASE_									=						new_item.replace('in_layers.2' ,   'conv1'       )

        SCREAMING_SNAKE_CASE_									=						new_item.replace('out_layers.0' ,   'norm2'       )
        SCREAMING_SNAKE_CASE_									=						new_item.replace('out_layers.3' ,   'conv2'       )

        SCREAMING_SNAKE_CASE_									=						new_item.replace('emb_layers.1' ,   'time_emb_proj'       )
        SCREAMING_SNAKE_CASE_									=						new_item.replace('skip_connection' ,   'conv_shortcut'       )

        SCREAMING_SNAKE_CASE_									=						shave_segments(_SCREAMING_SNAKE_CASE ,   n_shave_prefix_segments=_SCREAMING_SNAKE_CASE       )

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

    return mapping


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Tuple=0       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for old_item in old_list:
        SCREAMING_SNAKE_CASE_									=						old_item

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

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

        SCREAMING_SNAKE_CASE_									=						shave_segments(_SCREAMING_SNAKE_CASE ,   n_shave_prefix_segments=_SCREAMING_SNAKE_CASE       )

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

    return mapping


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]=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():
            SCREAMING_SNAKE_CASE_									=						old_checkpoint[path]
            SCREAMING_SNAKE_CASE_									=						old_tensor.shape[0] // 3

            SCREAMING_SNAKE_CASE_									=						(-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

            SCREAMING_SNAKE_CASE_									=						old_tensor.shape[0] // config['num_head_channels'] // 3

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

            SCREAMING_SNAKE_CASE_									=						query.reshape(_SCREAMING_SNAKE_CASE       )
            SCREAMING_SNAKE_CASE_									=						key.reshape(_SCREAMING_SNAKE_CASE       )
            SCREAMING_SNAKE_CASE_									=						value.reshape(_SCREAMING_SNAKE_CASE       )

    for path in paths:
        SCREAMING_SNAKE_CASE_									=						path['new']

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

        # Global renaming happens here
        SCREAMING_SNAKE_CASE_									=						new_path.replace('middle_block.0' ,   'mid_block.resnets.0'       )
        SCREAMING_SNAKE_CASE_									=						new_path.replace('middle_block.1' ,   'mid_block.attentions.0'       )
        SCREAMING_SNAKE_CASE_									=						new_path.replace('middle_block.2' ,   'mid_block.resnets.1'       )

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

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            SCREAMING_SNAKE_CASE_									=						old_checkpoint[path['old']][:, :, 0]
        else:
            SCREAMING_SNAKE_CASE_									=						old_checkpoint[path['old']]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{}

    SCREAMING_SNAKE_CASE_									=						checkpoint['time_embed.0.weight']
    SCREAMING_SNAKE_CASE_									=						checkpoint['time_embed.0.bias']
    SCREAMING_SNAKE_CASE_									=						checkpoint['time_embed.2.weight']
    SCREAMING_SNAKE_CASE_									=						checkpoint['time_embed.2.bias']

    SCREAMING_SNAKE_CASE_									=						checkpoint['input_blocks.0.0.weight']
    SCREAMING_SNAKE_CASE_									=						checkpoint['input_blocks.0.0.bias']

    SCREAMING_SNAKE_CASE_									=						checkpoint['out.0.weight']
    SCREAMING_SNAKE_CASE_									=						checkpoint['out.0.bias']
    SCREAMING_SNAKE_CASE_									=						checkpoint['out.2.weight']
    SCREAMING_SNAKE_CASE_									=						checkpoint['out.2.bias']

    # Retrieves the keys for the input blocks only
    SCREAMING_SNAKE_CASE_									=						len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'input_blocks' in layer}       )
    SCREAMING_SNAKE_CASE_									=						{
        layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
        for layer_id in range(_SCREAMING_SNAKE_CASE       )
    }

    # Retrieves the keys for the middle blocks only
    SCREAMING_SNAKE_CASE_									=						len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'middle_block' in layer}       )
    SCREAMING_SNAKE_CASE_									=						{
        layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
        for layer_id in range(_SCREAMING_SNAKE_CASE       )
    }

    # Retrieves the keys for the output blocks only
    SCREAMING_SNAKE_CASE_									=						len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'output_blocks' in layer}       )
    SCREAMING_SNAKE_CASE_									=						{
        layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
        for layer_id in range(_SCREAMING_SNAKE_CASE       )
    }

    for i in range(1 ,   _SCREAMING_SNAKE_CASE       ):
        SCREAMING_SNAKE_CASE_									=						(i - 1) // (config['num_res_blocks'] + 1)
        SCREAMING_SNAKE_CASE_									=						(i - 1) % (config['num_res_blocks'] + 1)

        SCREAMING_SNAKE_CASE_									=						[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
        SCREAMING_SNAKE_CASE_									=						[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

        if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
            SCREAMING_SNAKE_CASE_									=						checkpoint[
                f"""input_blocks.{i}.0.op.weight"""
            ]
            SCREAMING_SNAKE_CASE_									=						checkpoint[
                f"""input_blocks.{i}.0.op.bias"""
            ]
            continue

        SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_									=						{'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
        SCREAMING_SNAKE_CASE_									=						{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
        assign_to_checkpoint(
            _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   additional_replacements=[meta_path, resnet_op] ,   config=_SCREAMING_SNAKE_CASE       )

        if len(_SCREAMING_SNAKE_CASE       ):
            SCREAMING_SNAKE_CASE_									=						renew_attention_paths(_SCREAMING_SNAKE_CASE       )
            SCREAMING_SNAKE_CASE_									=						{
                'old': f"""input_blocks.{i}.1""",
                'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            SCREAMING_SNAKE_CASE_									=						{
                f"""input_blocks.{i}.1.qkv.bias""": {
                    'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                    'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                    'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                },
                f"""input_blocks.{i}.1.qkv.weight""": {
                    'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                    'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                    'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                },
            }
            assign_to_checkpoint(
                _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   additional_replacements=[meta_path] ,   attention_paths_to_split=_SCREAMING_SNAKE_CASE ,   config=_SCREAMING_SNAKE_CASE ,   )

    SCREAMING_SNAKE_CASE_									=						middle_blocks[0]
    SCREAMING_SNAKE_CASE_									=						middle_blocks[1]
    SCREAMING_SNAKE_CASE_									=						middle_blocks[2]

    SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
    assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   config=_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
    assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   config=_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						renew_attention_paths(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						{
        'middle_block.1.qkv.bias': {
            'key': 'mid_block.attentions.0.key.bias',
            'query': 'mid_block.attentions.0.query.bias',
            'value': 'mid_block.attentions.0.value.bias',
        },
        'middle_block.1.qkv.weight': {
            'key': 'mid_block.attentions.0.key.weight',
            'query': 'mid_block.attentions.0.query.weight',
            'value': 'mid_block.attentions.0.value.weight',
        },
    }
    assign_to_checkpoint(
        _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   attention_paths_to_split=_SCREAMING_SNAKE_CASE ,   config=_SCREAMING_SNAKE_CASE       )

    for i in range(_SCREAMING_SNAKE_CASE       ):
        SCREAMING_SNAKE_CASE_									=						i // (config['num_res_blocks'] + 1)
        SCREAMING_SNAKE_CASE_									=						i % (config['num_res_blocks'] + 1)
        SCREAMING_SNAKE_CASE_									=						[shave_segments(_SCREAMING_SNAKE_CASE ,   2       ) for name in output_blocks[i]]
        SCREAMING_SNAKE_CASE_									=						{}

        for layer in output_block_layers:
            SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						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:
                SCREAMING_SNAKE_CASE_									=						[layer_name]

        if len(_SCREAMING_SNAKE_CASE       ) > 1:
            SCREAMING_SNAKE_CASE_									=						[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
            SCREAMING_SNAKE_CASE_									=						[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

            SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
            SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE       )

            SCREAMING_SNAKE_CASE_									=						{'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       )

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                SCREAMING_SNAKE_CASE_									=						list(output_block_list.values()       ).index(['conv.weight', 'conv.bias']       )
                SCREAMING_SNAKE_CASE_									=						checkpoint[
                    f"""output_blocks.{i}.{index}.conv.weight"""
                ]
                SCREAMING_SNAKE_CASE_									=						checkpoint[
                    f"""output_blocks.{i}.{index}.conv.bias"""
                ]

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

            if len(_SCREAMING_SNAKE_CASE       ):
                SCREAMING_SNAKE_CASE_									=						renew_attention_paths(_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						{
                    'old': f"""output_blocks.{i}.1""",
                    'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                }
                SCREAMING_SNAKE_CASE_									=						{
                    f"""output_blocks.{i}.1.qkv.bias""": {
                        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                    },
                    f"""output_blocks.{i}.1.qkv.weight""": {
                        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                    },
                }
                assign_to_checkpoint(
                    _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   additional_replacements=[meta_path] ,   attention_paths_to_split=to_split if any('qkv' in key for key in attentions       ) else None ,   config=_SCREAMING_SNAKE_CASE ,   )
        else:
            SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE ,   n_shave_prefix_segments=1       )
            for path in resnet_0_paths:
                SCREAMING_SNAKE_CASE_									=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE       ), path['old']]       )
                SCREAMING_SNAKE_CASE_									=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE       ), 'resnets', str(_SCREAMING_SNAKE_CASE       ), path['new']]       )

                SCREAMING_SNAKE_CASE_									=						checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
       UpperCamelCase__  :      Union[str, Any]			=    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__  :      List[Any]			=    parser.parse_args()

       UpperCamelCase__  :      Tuple			=    torch.load(args.checkpoint_path)

       with open(args.config_file) as f:
              UpperCamelCase__  :      Optional[Any]			=    json.loads(f.read())

       UpperCamelCase__  :      List[str]			=    convert_ldm_checkpoint(checkpoint, config)

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

       UpperCamelCase__  :      Tuple			=    UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

       try:
              UpperCamelCase__  :      List[str]			=    DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
              UpperCamelCase__  :      int			=    VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

              UpperCamelCase__  :      str			=    LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
              pipe.save_pretrained(args.dump_path)
       except:  # noqa: E722
              model.save_pretrained(args.dump_path)





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase__  :      int			=    Lock()


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Any       ):





    """simple docstring"""


    global process_lock

    # we perform n swaps since after n swaps we know we are sorted
    # we *could* stop early if we are sorted already, but it takes as long to
    # find out we are sorted as it does to sort the list with this algorithm
    for i in range(0 ,   10       ):
        if (i + position) % 2 == 0 and r_send is not None:
            # send your value to your right neighbor
            process_lock.acquire()
            r_send[1].send(_SCREAMING_SNAKE_CASE       )
            process_lock.release()

            # receive your right neighbor's value
            process_lock.acquire()
            SCREAMING_SNAKE_CASE_									=						rr_cv[0].recv()
            process_lock.release()

            # take the lower value since you are on the left
            SCREAMING_SNAKE_CASE_									=						min(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
        elif (i + position) % 2 != 0 and l_send is not None:
            # send your value to your left neighbor
            process_lock.acquire()
            l_send[1].send(_SCREAMING_SNAKE_CASE       )
            process_lock.release()

            # receive your left neighbor's value
            process_lock.acquire()
            SCREAMING_SNAKE_CASE_									=						lr_cv[0].recv()
            process_lock.release()

            # take the higher value since you are on the right
            SCREAMING_SNAKE_CASE_									=						max(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    # after all swaps are performed, send the values back to main
    result_pipe[1].send(_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    # initialize the list of pipes where the values will be retrieved
    for _ in arr:
        result_pipe.append(Pipe()       )
    # creates the processes
    # the first and last process only have one neighbor so they are made outside
    # of the loop
    SCREAMING_SNAKE_CASE_									=						Pipe()
    SCREAMING_SNAKE_CASE_									=						Pipe()
    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE ,   args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,   )       )
    SCREAMING_SNAKE_CASE_									=						temp_rs
    SCREAMING_SNAKE_CASE_									=						temp_rr

    for i in range(1 ,   len(_SCREAMING_SNAKE_CASE       ) - 1       ):
        SCREAMING_SNAKE_CASE_									=						Pipe()
        SCREAMING_SNAKE_CASE_									=						Pipe()
        process_array_.append(
            Process(
                target=_SCREAMING_SNAKE_CASE ,   args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) ,   )       )
        SCREAMING_SNAKE_CASE_									=						temp_rs
        SCREAMING_SNAKE_CASE_									=						temp_rr

    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE ,   args=(
                len(_SCREAMING_SNAKE_CASE       ) - 1,
                arr[len(_SCREAMING_SNAKE_CASE       ) - 1],
                temp_ls,
                None,
                temp_lr,
                None,
                result_pipe[len(_SCREAMING_SNAKE_CASE       ) - 1],
            ) ,   )       )

    # start the processes
    for p in process_array_:
        p.start()

    # wait for the processes to end and write their values to the list
    for p in range(0 ,   len(_SCREAMING_SNAKE_CASE       )       ):
        SCREAMING_SNAKE_CASE_									=						result_pipe[p][0].recv()
        process_array_[p].join()
    return arr


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						list(range(10 ,   0 ,   -1       )       )
    print('Initial List'       )
    print(*_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						odd_even_transposition(_SCREAMING_SNAKE_CASE       )
    print('Sorted List\n'       )
    print(*_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       main()


