




"""simple docstring"""
import argparse

import torch

from diffusers import UNetaDConditionModel






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		Tuple  ,	__lowerCAmelCase			:		str=None  ,	__lowerCAmelCase			:		Optional[Any]=None  ,	__lowerCAmelCase			:		Optional[Any]=None						)    ->       List[str]:
 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      :	Dict					      =  old_checkpoint[path]
   _UpperCamelCase      :	Dict					      =  old_tensor.shape[0] // 3

   _UpperCamelCase      :	Dict					      =  (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

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

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

   _UpperCamelCase      :	int					      =  query.reshape(__lowerCAmelCase						)
   _UpperCamelCase      :	str					      =  key.reshape(__lowerCAmelCase						)
   _UpperCamelCase      :	Any					      =  value.reshape(__lowerCAmelCase						)

 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      :	Optional[int]					      =  ["proj_attn.weight"]
  _UpperCamelCase      :	List[str]					      =  ["proj_out.weight", "proj_in.weight"]
  if any(k in new_path for k in names						):
   _UpperCamelCase      :	Optional[Any]					      =  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      :	Union[str, Any]					      =  weight[:, :, 0]
  else:
   _UpperCamelCase      :	Optional[int]					      =  weight






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		int  ,	__lowerCAmelCase			:		int=0						)    ->       int:
 _UpperCamelCase      :	int					      =  []
 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 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		Dict=1						)    ->       Tuple:
 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					(					__lowerCAmelCase			:		Union[str, Any]  ,	__lowerCAmelCase			:		Tuple=0						)    ->       Union[str, Any]:
 _UpperCamelCase      :	Optional[Any]					      =  []
 for old_item in old_list:
  mapping.append({"old": old_item, "new": old_item}						)

 return mapping






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]  ,	__lowerCAmelCase			:		Tuple=0						)    ->       int:
 _UpperCamelCase      :	Any					      =  []
 for old_item in old_list:
  _UpperCamelCase      :	int					      =  old_item.replace("in_layers.0"  ,	"norm1"						)
  _UpperCamelCase      :	int					      =  new_item.replace("in_layers.2"  ,	"conv1"						)

  _UpperCamelCase      :	Dict					      =  new_item.replace("out_layers.0"  ,	"norm2"						)
  _UpperCamelCase      :	Optional[int]					      =  new_item.replace("out_layers.3"  ,	"conv2"						)

  _UpperCamelCase      :	Optional[Any]					      =  new_item.replace("emb_layers.1"  ,	"time_emb_proj"						)
  _UpperCamelCase      :	Tuple					      =  new_item.replace("skip_connection"  ,	"conv_shortcut"						)

  _UpperCamelCase      :	Union[str, Any]					      =  shave_segments(__lowerCAmelCase  ,	n_shave_prefix_segments=__lowerCAmelCase						)

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

 return mapping






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		str  ,	__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		Union[str, Any]=None  ,	__lowerCAmelCase			:		List[str]=False						)    ->       Tuple:
 _UpperCamelCase      :	int					      =  {}
 _UpperCamelCase      :	List[str]					      =  list(checkpoint.keys()						)

 _UpperCamelCase      :	Dict					      =  "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      :	Dict					      =  "model_ema." + "".join(key.split("."						)[1:]						)
    _UpperCamelCase      :	List[str]					      =  checkpoint.pop(__lowerCAmelCase						)
 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      :	int					      =  checkpoint.pop(__lowerCAmelCase						)

 _UpperCamelCase      :	Optional[int]					      =  {}

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

 _UpperCamelCase      :	int					      =  unet_state_dict["input_blocks.0.0.weight"]
 _UpperCamelCase      :	str					      =  unet_state_dict["input_blocks.0.0.bias"]

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

 _UpperCamelCase      :	int					      =  unet_state_dict["out.0.weight"]
 _UpperCamelCase      :	Union[str, Any]					      =  unet_state_dict["out.0.bias"]
 _UpperCamelCase      :	Any					      =  unet_state_dict["out.2.weight"]
 _UpperCamelCase      :	List[Any]					      =  unet_state_dict["out.2.bias"]

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

 # Retrieves the keys for the middle blocks only
 _UpperCamelCase      :	List[str]					      =  len({".".join(layer.split("."						)[:2]						) for layer in unet_state_dict if "middle_block" in layer}						)
 _UpperCamelCase      :	List[Any]					      =  {
     layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
     for layer_id in range(__lowerCAmelCase						)
 }

 # Retrieves the keys for the output blocks only
 _UpperCamelCase      :	Tuple					      =  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(__lowerCAmelCase						)
 }

 for i in range(1  ,	__lowerCAmelCase						):
  _UpperCamelCase      :	int					      =  (i - 1) // (config["layers_per_block"] + 1)
  _UpperCamelCase      :	List[str]					      =  (i - 1) % (config["layers_per_block"] + 1)

  _UpperCamelCase      :	Optional[int]					      =  [
      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      :	Dict					      =  [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
  _UpperCamelCase      :	Any					      =  [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      :	List[Any]					      =  unet_state_dict.pop(
       f"input_blocks.{i}.op.weight"						)
   _UpperCamelCase      :	Dict					      =  unet_state_dict.pop(
       f"input_blocks.{i}.op.bias"						)

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

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

  if len(__lowerCAmelCase						):
   _UpperCamelCase      :	List[Any]					      =  renew_attention_paths(__lowerCAmelCase						)
   _UpperCamelCase      :	Union[str, Any]					      =  {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
   assign_to_checkpoint(
       __lowerCAmelCase  ,	__lowerCAmelCase  ,	__lowerCAmelCase  ,	additional_replacements=[meta_path]  ,	config=__lowerCAmelCase						)

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

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

 _UpperCamelCase      :	Dict					      =  renew_resnet_paths(__lowerCAmelCase						)
 _UpperCamelCase      :	List[str]					      =  {"old": "middle_block.0", "new": "mid_block.resnets.0"}
 assign_to_checkpoint(
     __lowerCAmelCase  ,	__lowerCAmelCase  ,	__lowerCAmelCase  ,	config=__lowerCAmelCase  ,	additional_replacements=[meta_path]						)

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

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

 _UpperCamelCase      :	Dict					      =  renew_temp_conv_paths(__lowerCAmelCase						)
 _UpperCamelCase      :	Dict					      =  {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
 assign_to_checkpoint(
     __lowerCAmelCase  ,	__lowerCAmelCase  ,	__lowerCAmelCase  ,	config=__lowerCAmelCase  ,	additional_replacements=[meta_path]						)

 _UpperCamelCase      :	Tuple					      =  renew_attention_paths(__lowerCAmelCase						)
 _UpperCamelCase      :	Dict					      =  {"old": "middle_block.1", "new": "mid_block.attentions.0"}
 assign_to_checkpoint(
     __lowerCAmelCase  ,	__lowerCAmelCase  ,	__lowerCAmelCase  ,	additional_replacements=[meta_path]  ,	config=__lowerCAmelCase						)

 _UpperCamelCase      :	Optional[Any]					      =  renew_attention_paths(__lowerCAmelCase						)
 _UpperCamelCase      :	List[Any]					      =  {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
 assign_to_checkpoint(
     __lowerCAmelCase  ,	__lowerCAmelCase  ,	__lowerCAmelCase  ,	additional_replacements=[meta_path]  ,	config=__lowerCAmelCase						)

 for i in range(__lowerCAmelCase						):
  _UpperCamelCase      :	Union[str, Any]					      =  i // (config["layers_per_block"] + 1)
  _UpperCamelCase      :	Optional[Any]					      =  i % (config["layers_per_block"] + 1)
  _UpperCamelCase      :	Dict					      =  [shave_segments(__lowerCAmelCase  ,	2						) for name in output_blocks[i]]
  _UpperCamelCase      :	Optional[Any]					      =  {}

  for layer in output_block_layers:
   _UpperCamelCase      ,	_UpperCamelCase      :	Optional[int]					      =  layer.split("."						)[0], shave_segments(__lowerCAmelCase  ,	1						)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(__lowerCAmelCase						)
   else:
    _UpperCamelCase      :	int					      =  [layer_name]

  if len(__lowerCAmelCase						) > 1:
   _UpperCamelCase      :	int					      =  [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
   _UpperCamelCase      :	Optional[Any]					      =  [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      :	List[str]					      =  renew_resnet_paths(__lowerCAmelCase						)
   _UpperCamelCase      :	Union[str, Any]					      =  renew_resnet_paths(__lowerCAmelCase						)

   _UpperCamelCase      :	Any					      =  {"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						)

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

   _UpperCamelCase      :	Dict					      =  {k: sorted(__lowerCAmelCase						) 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      :	Any					      =  unet_state_dict[
        f"output_blocks.{i}.{index}.conv.weight"
    ]
    _UpperCamelCase      :	Optional[int]					      =  unet_state_dict[
        f"output_blocks.{i}.{index}.conv.bias"
    ]

    # Clear attentions as they have been attributed above.
    if len(__lowerCAmelCase						) == 2:
     _UpperCamelCase      :	Dict					      =  []

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

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

   _UpperCamelCase      :	int					      =  [l for l in output_block_layers if "temopral_conv" in l]
   for path in temopral_conv_paths:
    _UpperCamelCase      :	Optional[Any]					      =  path.split("temopral_conv."						)[-1]
    _UpperCamelCase      :	Optional[int]					      =  ".".join(["output_blocks", str(__lowerCAmelCase						), str(__lowerCAmelCase						), "temopral_conv", pruned_path]						)
    _UpperCamelCase      :	str					      =  ".".join(["up_blocks", str(__lowerCAmelCase						), "temp_convs", str(__lowerCAmelCase						), pruned_path]						)
    _UpperCamelCase      :	Tuple					      =  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 --









"""simple docstring"""
import math






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Tuple						)    ->       Any:
 _UpperCamelCase      :	str					      =  0
 if n % 2 == 0:
  count += 1
  while n % 2 == 0:
   _UpperCamelCase      :	List[Any]					      =  int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

 _UpperCamelCase      :	Tuple					      =  3

 while i <= int(math.sqrt(__lowerCAmelCase						)						):
  if n % i == 0:
   count += 1
   while n % i == 0:
    _UpperCamelCase      :	Any					      =  int(n / i						)
  _UpperCamelCase      :	Optional[Any]					      =  i + 2

 # this condition checks the prime
 # number n is greater than 2

 if n > 2:
  count += 1
 return count


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE						 =     5_1_2_4_2_1_8_3
    print(f'The number of distinct prime factors is/are {exact_prime_factor_count(n)}')
    print(f'The value of log(log(n)) is {math.log(math.log(n)):.4f}')



