



import argparse

import torch

from diffusers import UNetaDConditionModel





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase=None  ,					_UpperCAmelCase=None  ,					_UpperCAmelCase=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():
            __a					=							old_checkpoint[path]
            __a					=							old_tensor.shape[0] // 3

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

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

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

            __a					=							query.reshape(_UpperCAmelCase							)
            __a					=							key.reshape(_UpperCAmelCase							)
            __a					=							value.reshape(_UpperCAmelCase							)

    for path in paths:
        __a					=							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:
                __a					=							new_path.replace(replacement['''old''']  ,					replacement['''new''']							)

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





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=0							):
    __a					=							[]
    for old_item in old_list:
        __a					=							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 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=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 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=0							):
    __a					=							[]
    for old_item in old_list:
        mapping.append({'''old''': old_item, '''new''': old_item}							)

    return mapping





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

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

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

        __a					=							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 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase=None  ,					_UpperCAmelCase=False							):
    __a					=							{}
    __a					=							list(checkpoint.keys()							)

    __a					=							'''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'''							):
                __a					=							'''model_ema.''' + ''''''.join(key.split('''.'''							)[1:]							)
                __a					=							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:
            __a					=							checkpoint.pop(_UpperCAmelCase							)

    __a					=							{}

    __a					=							unet_state_dict['''time_embed.0.weight''']
    __a					=							unet_state_dict['''time_embed.0.bias''']
    __a					=							unet_state_dict['''time_embed.2.weight''']
    __a					=							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":
        __a					=							unet_state_dict['''label_emb.0.0.weight''']
        __a					=							unet_state_dict['''label_emb.0.0.bias''']
        __a					=							unet_state_dict['''label_emb.0.2.weight''']
        __a					=							unet_state_dict['''label_emb.0.2.bias''']
    else:
        raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'							)

    __a					=							unet_state_dict['''input_blocks.0.0.weight''']
    __a					=							unet_state_dict['''input_blocks.0.0.bias''']

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

    __a					=							unet_state_dict['''out.0.weight''']
    __a					=							unet_state_dict['''out.0.bias''']
    __a					=							unet_state_dict['''out.2.weight''']
    __a					=							unet_state_dict['''out.2.bias''']

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

        __a					=							[
            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
        ]
        __a					=							[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
        __a					=							[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:
            __a					=							unet_state_dict.pop(
                f'input_blocks.{i}.op.weight'							)
            __a					=							unet_state_dict.pop(
                f'input_blocks.{i}.op.bias'							)

        __a					=							renew_resnet_paths(_UpperCAmelCase							)
        __a					=							{'''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							)

        __a					=							[key for key in resnets if '''temopral_conv''' in key]
        __a					=							renew_temp_conv_paths(_UpperCAmelCase							)
        __a					=							{
            '''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							):
            __a					=							renew_attention_paths(_UpperCAmelCase							)
            __a					=							{'''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							):
            __a					=							renew_attention_paths(_UpperCAmelCase							)
            __a					=							{
                '''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							)

    __a					=							middle_blocks[0]
    __a					=							[key for key in resnet_a if '''temopral_conv''' in key]
    __a					=							middle_blocks[1]
    __a					=							middle_blocks[2]
    __a					=							middle_blocks[3]
    __a					=							[key for key in resnet_a if '''temopral_conv''' in key]

    __a					=							renew_resnet_paths(_UpperCAmelCase							)
    __a					=							{'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
    assign_to_checkpoint(
        _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					config=_UpperCAmelCase  ,					additional_replacements=[meta_path]							)

    __a					=							renew_temp_conv_paths(_UpperCAmelCase							)
    __a					=							{'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
    assign_to_checkpoint(
        _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					config=_UpperCAmelCase  ,					additional_replacements=[meta_path]							)

    __a					=							renew_resnet_paths(_UpperCAmelCase							)
    __a					=							{'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
    assign_to_checkpoint(
        _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					config=_UpperCAmelCase  ,					additional_replacements=[meta_path]							)

    __a					=							renew_temp_conv_paths(_UpperCAmelCase							)
    __a					=							{'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
    assign_to_checkpoint(
        _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					config=_UpperCAmelCase  ,					additional_replacements=[meta_path]							)

    __a					=							renew_attention_paths(_UpperCAmelCase							)
    __a					=							{'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
    assign_to_checkpoint(
        _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					additional_replacements=[meta_path]  ,					config=_UpperCAmelCase							)

    __a					=							renew_attention_paths(_UpperCAmelCase							)
    __a					=							{'''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							):
        __a					=							i // (config['''layers_per_block'''] + 1)
        __a					=							i % (config['''layers_per_block'''] + 1)
        __a					=							[shave_segments(_UpperCAmelCase  ,					2							) for name in output_blocks[i]]
        __a					=							{}

        for layer in output_block_layers:
            __a			,  __a					=							layer.split('''.'''							)[0], shave_segments(_UpperCAmelCase  ,					1							)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(_UpperCAmelCase							)
            else:
                __a					=							[layer_name]

        if len(_UpperCAmelCase							) > 1:
            __a					=							[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
            __a					=							[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
            __a					=							[key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

            __a					=							renew_resnet_paths(_UpperCAmelCase							)
            __a					=							renew_resnet_paths(_UpperCAmelCase							)

            __a					=							{'''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							)

            __a					=							[key for key in resnets if '''temopral_conv''' in key]
            __a					=							renew_temp_conv_paths(_UpperCAmelCase							)
            __a					=							{
                '''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							)

            __a					=							{k: sorted(_UpperCAmelCase							) for k, v in output_block_list.items()}
            if ["conv.bias", "conv.weight"] in output_block_list.values():
                __a					=							list(output_block_list.values()							).index(['''conv.bias''', '''conv.weight''']							)
                __a					=							unet_state_dict[
                    f'output_blocks.{i}.{index}.conv.weight'
                ]
                __a					=							unet_state_dict[
                    f'output_blocks.{i}.{index}.conv.bias'
                ]

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

            if len(_UpperCAmelCase							):
                __a					=							renew_attention_paths(_UpperCAmelCase							)
                __a					=							{
                    '''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							):
                __a					=							renew_attention_paths(_UpperCAmelCase							)
                __a					=							{
                    '''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:
            __a					=							renew_resnet_paths(_UpperCAmelCase  ,					n_shave_prefix_segments=1							)
            for path in resnet_0_paths:
                __a					=							'''.'''.join(['''output_blocks''', str(_UpperCAmelCase							), path['''old''']]							)
                __a					=							'''.'''.join(['''up_blocks''', str(_UpperCAmelCase							), '''resnets''', str(_UpperCAmelCase							), path['''new''']]							)
                __a					=							unet_state_dict[old_path]

            __a					=							[l for l in output_block_layers if '''temopral_conv''' in l]
            for path in temopral_conv_paths:
                __a					=							path.split('''temopral_conv.'''							)[-1]
                __a					=							'''.'''.join(['''output_blocks''', str(_UpperCAmelCase							), str(_UpperCAmelCase							), '''temopral_conv''', pruned_path]							)
                __a					=							'''.'''.join(['''up_blocks''', str(_UpperCAmelCase							), '''temp_convs''', str(_UpperCAmelCase							), pruned_path]							)
                __a					=							unet_state_dict[old_path]

    return new_checkpoint


if __name__ == "__main__":
 __snake_case		:str 						=	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.''')
 __snake_case		:Optional[int] 						=	parser.parse_args()

 __snake_case		:Any 						=	torch.load(args.checkpoint_path, map_location='''cpu''')
 __snake_case		:Optional[Any] 						=	UNetaDConditionModel()

 __snake_case		:Dict 						=	convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

 __snake_case		:Dict 						=	set(unet.state_dict().keys()) - set(converted_ckpt.keys())
 __snake_case		:Any 						=	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 --










def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							[1 / first_term]
    __a					=							1 / first_term
    for _ in range(number_of_terms - 1							):
        first_term += common_difference
        arithmetic_progression.append(_UpperCAmelCase							)
    __a					=							[1 / step for step in arithmetic_progression]
    return sum(_UpperCAmelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))





