




'''simple docstring'''







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 						snake_case_							(_a	:							Optional[int]    ,   _a	:							Optional[Any]=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_							(_a	:							Dict    ,   _a	:							Dict=0     ):
    UpperCAmelCase			       =		[]
    for old_item in old_list:
        UpperCAmelCase			       =		old_item.replace('''in_layers.0'''    ,   '''norm1'''     )
        UpperCAmelCase			       =		new_item.replace('''in_layers.2'''    ,   '''conv1'''     )

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

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

        UpperCAmelCase			       =		shave_segments(_lowerCamelCase    ,   n_shave_prefix_segments=_lowerCamelCase     )

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

    return mapping





def 						snake_case_							(_a	:							str    ,   _a	:							Optional[int]=0     ):
    UpperCAmelCase			       =		[]
    for old_item in old_list:
        UpperCAmelCase			       =		old_item

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

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

        UpperCAmelCase			       =		shave_segments(_lowerCamelCase    ,   n_shave_prefix_segments=_lowerCamelCase     )

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

    return mapping





def 						snake_case_							(_a	:							Dict    ,   _a	:							int    ,   _a	:							Optional[Any]    ,   _a	:							Union[str, Any]=None    ,   _a	:							Any=None    ,   _a	:							Dict=None     ):
    assert isinstance(_lowerCamelCase    ,   _lowerCamelCase     ), "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			       =		old_checkpoint[path]
            UpperCAmelCase			       =		old_tensor.shape[0] // 3

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

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

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

            UpperCAmelCase			       =		query.reshape(_lowerCamelCase     )
            UpperCAmelCase			       =		key.reshape(_lowerCamelCase     )
            UpperCAmelCase			       =		value.reshape(_lowerCamelCase     )

    for path in paths:
        UpperCAmelCase			       =		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
        UpperCAmelCase			       =		new_path.replace('''middle_block.0'''    ,   '''mid_block.resnets.0'''     )
        UpperCAmelCase			       =		new_path.replace('''middle_block.1'''    ,   '''mid_block.attentions.0'''     )
        UpperCAmelCase			       =		new_path.replace('''middle_block.2'''    ,   '''mid_block.resnets.1'''     )

        if additional_replacements is not None:
            for replacement in additional_replacements:
                UpperCAmelCase			       =		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:
            UpperCAmelCase			       =		old_checkpoint[path['''old''']][:, :, 0]
        else:
            UpperCAmelCase			       =		old_checkpoint[path['''old''']]





def 						snake_case_							(_a	:							Dict    ,   _a	:							int     ):
    UpperCAmelCase			       =		{}

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

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

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

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

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

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

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

        UpperCAmelCase			       =		[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
        UpperCAmelCase			       =		[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:
            UpperCAmelCase			       =		checkpoint[
                F"input_blocks.{i}.0.op.weight"
            ]
            UpperCAmelCase			       =		checkpoint[
                F"input_blocks.{i}.0.op.bias"
            ]
            continue

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

        if len(_lowerCamelCase     ):
            UpperCAmelCase			       =		renew_attention_paths(_lowerCamelCase     )
            UpperCAmelCase			       =		{
                '''old''': F"input_blocks.{i}.1",
                '''new''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
            }
            UpperCAmelCase			       =		{
                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(
                _lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   additional_replacements=[meta_path]    ,   attention_paths_to_split=_lowerCamelCase    ,   config=_lowerCamelCase    ,   )

    UpperCAmelCase			       =		middle_blocks[0]
    UpperCAmelCase			       =		middle_blocks[1]
    UpperCAmelCase			       =		middle_blocks[2]

    UpperCAmelCase			       =		renew_resnet_paths(_lowerCamelCase     )
    assign_to_checkpoint(_lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   config=_lowerCamelCase     )

    UpperCAmelCase			       =		renew_resnet_paths(_lowerCamelCase     )
    assign_to_checkpoint(_lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   config=_lowerCamelCase     )

    UpperCAmelCase			       =		renew_attention_paths(_lowerCamelCase     )
    UpperCAmelCase			       =		{
        '''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(
        _lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   attention_paths_to_split=_lowerCamelCase    ,   config=_lowerCamelCase     )

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

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

        if len(_lowerCamelCase     ) > 1:
            UpperCAmelCase			       =		[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
            UpperCAmelCase			       =		[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

            UpperCAmelCase			       =		renew_resnet_paths(_lowerCamelCase     )
            UpperCAmelCase			       =		renew_resnet_paths(_lowerCamelCase     )

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

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

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

            if len(_lowerCamelCase     ):
                UpperCAmelCase			       =		renew_attention_paths(_lowerCamelCase     )
                UpperCAmelCase			       =		{
                    '''old''': F"output_blocks.{i}.1",
                    '''new''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                }
                UpperCAmelCase			       =		{
                    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(
                    _lowerCamelCase    ,   _lowerCamelCase    ,   _lowerCamelCase    ,   additional_replacements=[meta_path]    ,   attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions     ) else None    ,   config=_lowerCamelCase    ,   )
        else:
            UpperCAmelCase			       =		renew_resnet_paths(_lowerCamelCase    ,   n_shave_prefix_segments=1     )
            for path in resnet_0_paths:
                UpperCAmelCase			       =		'''.'''.join(['''output_blocks''', str(_lowerCamelCase     ), path['''old''']]     )
                UpperCAmelCase			       =		'''.'''.join(['''up_blocks''', str(_lowerCamelCase     ), '''resnets''', str(_lowerCamelCase     ), path['''new''']]     )

                UpperCAmelCase			       =		checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
   A     =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.')

   A     =parser.parse_args()

   A     =torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      A     =json.loads(f.read())

   A     =convert_ldm_checkpoint(checkpoint, config)

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

   A     =UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      A     =DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
      A     =VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

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











'''simple docstring'''

def 						UpperCamelCase				(							_lowerCamelCase					:					int | float | str       ):
    try:
        A__				=			float(_lowerCamelCase       )
    except ValueError:
        raise ValueError("Please enter a valid number"       )
    A__				=			decimal - int(_lowerCamelCase       )
    if fractional_part == 0:
        return int(_lowerCamelCase       ), 1
    else:
        A__				=			len(str(_lowerCamelCase       ).split("."       )[1]       )
        A__				=			int(decimal * (10**number_of_frac_digits)       )
        A__				=			10**number_of_frac_digits
        A__,	A__				=			denominator, numerator
        while True:
            A__				=			dividend % divisor
            if remainder == 0:
                break
            A__,	A__				=			divisor, remainder
        A__,	A__				=			numerator / divisor, denominator / divisor
        return int(_lowerCamelCase       ), int(_lowerCamelCase       )


if __name__ == "__main__":
  print(f"""{decimal_to_fraction(2) = }""")
  print(f"""{decimal_to_fraction(89.0) = }""")
  print(f"""{decimal_to_fraction("67") = }""")
  print(f"""{decimal_to_fraction("45.0") = }""")
  print(f"""{decimal_to_fraction(1.5) = }""")
  print(f"""{decimal_to_fraction("6.25") = }""")
  print(f"""{decimal_to_fraction("78td") = }""")



