




import argparse

import torch

from diffusers import UNetaDConditionModel
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=None    ,SCREAMING_SNAKE_CASE_=None    ,SCREAMING_SNAKE_CASE_=None    )      ->			Optional[Any]:
							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():
																					lowercase__  :	int        =							old_checkpoint[path]
																					lowercase__  :	List[Any]        =							old_tensor.shape[0] // 3

																					lowercase__  :	Optional[int]        =							(-1, channels) if len(old_tensor.shape    ) == 3 else (-1)

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

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

																					lowercase__  :	str        =							query.reshape(SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Optional[int]        =							key.reshape(SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							value.reshape(SCREAMING_SNAKE_CASE_    )

							for path in paths:
														lowercase__  :	str        =							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:
																												lowercase__  :	List[str]        =							new_path.replace(replacement["old"]    ,replacement["new"]    )

        # proj_attn.weight has to be converted from conv 1D to linear
														lowercase__  :	List[str]        =							old_checkpoint[path["old"]]
														lowercase__  :	str        =							["proj_attn.weight"]
														lowercase__  :	Tuple        =							["proj_out.weight", "proj_in.weight"]
														if any(k in new_path for k in names    ):
																					lowercase__  :	Optional[int]        =							weight[:, :, 0]
														elif any(k in new_path for k in names_a    ) and len(weight.shape    ) > 2 and ".attentions." not in new_path:
																					lowercase__  :	int        =							weight[:, :, 0]
														else:
																					lowercase__  :	Dict        =							weight
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=0    )      ->			Any:
							lowercase__  :	int        =							[]
							for old_item in old_list:
														lowercase__  :	List[str]        =							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_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=1    )      ->			List[Any]:
							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_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=0    )      ->			int:
							lowercase__  :	List[Any]        =							[]
							for old_item in old_list:
														mapping.append({"old": old_item, "new": old_item}    )

							return mapping
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=0    )      ->			Tuple:
							lowercase__  :	Optional[Any]        =							[]
							for old_item in old_list:
														lowercase__  :	int        =							old_item.replace("in_layers.0"    ,"norm1"    )
														lowercase__  :	Any        =							new_item.replace("in_layers.2"    ,"conv1"    )

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

														lowercase__  :	Tuple        =							new_item.replace("emb_layers.1"    ,"time_emb_proj"    )
														lowercase__  :	Any        =							new_item.replace("skip_connection"    ,"conv_shortcut"    )

														lowercase__  :	Tuple        =							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  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=None    ,SCREAMING_SNAKE_CASE_=False    )      ->			Optional[Any]:
							lowercase__  :	int        =							{}
							lowercase__  :	List[str]        =							list(checkpoint.keys()    )

							lowercase__  :	Optional[int]        =							"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_00 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"    ):
																												lowercase__  :	int        =							"model_ema." + "".join(key.split("."    )[1:]    )
																												lowercase__  :	List[Any]        =							checkpoint.pop(SCREAMING_SNAKE_CASE_    )
							else:
														if sum(k.startswith("model_ema"    ) for k in keys    ) > 1_00:
																					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:
																					lowercase__  :	Optional[Any]        =							checkpoint.pop(SCREAMING_SNAKE_CASE_    )

							lowercase__  :	Any        =							{}

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

							lowercase__  :	List[str]        =							unet_state_dict["input_blocks.0.0.weight"]
							lowercase__  :	List[Any]        =							unet_state_dict["input_blocks.0.0.bias"]

							lowercase__  :	List[Any]        =							[v for v in unet_state_dict if v.startswith("input_blocks.0.1"    )]
							lowercase__  :	List[Any]        =							renew_attention_paths(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	str        =							{"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_    )

							lowercase__  :	List[str]        =							unet_state_dict["out.0.weight"]
							lowercase__  :	Optional[int]        =							unet_state_dict["out.0.bias"]
							lowercase__  :	List[str]        =							unet_state_dict["out.2.weight"]
							lowercase__  :	List[str]        =							unet_state_dict["out.2.bias"]

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

														lowercase__  :	List[str]        =							[
														    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
														]
														lowercase__  :	Any        =							[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
														lowercase__  :	Union[str, 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:
																					lowercase__  :	Tuple        =							unet_state_dict.pop(
																					    F"""input_blocks.{i}.op.weight"""    )
																					lowercase__  :	List[str]        =							unet_state_dict.pop(
																					    F"""input_blocks.{i}.op.bias"""    )

														lowercase__  :	str        =							renew_resnet_paths(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Optional[int]        =							{"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_    )

														lowercase__  :	Any        =							[key for key in resnets if "temopral_conv" in key]
														lowercase__  :	Any        =							renew_temp_conv_paths(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Tuple        =							{
														    "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_    ):
																					lowercase__  :	Any        =							renew_attention_paths(SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	int        =							{"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_    ):
																					lowercase__  :	List[Any]        =							renew_attention_paths(SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Optional[Any]        =							{
																					    "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_    )

							lowercase__  :	int        =							middle_blocks[0]
							lowercase__  :	int        =							[key for key in resnet_a if "temopral_conv" in key]
							lowercase__  :	str        =							middle_blocks[1]
							lowercase__  :	str        =							middle_blocks[2]
							lowercase__  :	int        =							middle_blocks[3]
							lowercase__  :	Optional[int]        =							[key for key in resnet_a if "temopral_conv" in key]

							lowercase__  :	List[str]        =							renew_resnet_paths(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							{"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]    )

							lowercase__  :	str        =							renew_temp_conv_paths(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Union[str, Any]        =							{"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]    )

							lowercase__  :	Union[str, Any]        =							renew_resnet_paths(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							{"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]    )

							lowercase__  :	str        =							renew_temp_conv_paths(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	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]    )

							lowercase__  :	List[str]        =							renew_attention_paths(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Tuple        =							{"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_    )

							lowercase__  :	str        =							renew_attention_paths(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	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_    ):
														lowercase__  :	int        =							i // (config["layers_per_block"] + 1)
														lowercase__  :	Dict        =							i % (config["layers_per_block"] + 1)
														lowercase__  :	int        =							[shave_segments(SCREAMING_SNAKE_CASE_    ,2    ) for name in output_blocks[i]]
														lowercase__  :	Optional[int]        =							{}

														for layer in output_block_layers:
																					lowercase__  :	Any        =							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:
																												lowercase__  :	Optional[int]        =							[layer_name]

														if len(SCREAMING_SNAKE_CASE_    ) > 1:
																					lowercase__  :	List[Any]        =							[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
																					lowercase__  :	int        =							[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
																					lowercase__  :	Optional[Any]        =							[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

																					lowercase__  :	str        =							renew_resnet_paths(SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Tuple        =							renew_resnet_paths(SCREAMING_SNAKE_CASE_    )

																					lowercase__  :	Dict        =							{"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_    )

																					lowercase__  :	Optional[int]        =							[key for key in resnets if "temopral_conv" in key]
																					lowercase__  :	Any        =							renew_temp_conv_paths(SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	str        =							{
																					    "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_    )

																					lowercase__  :	Any        =							{k: sorted(SCREAMING_SNAKE_CASE_    ) for k, v in output_block_list.items()}
																					if ["conv.bias", "conv.weight"] in output_block_list.values():
																												lowercase__  :	Union[str, Any]        =							list(output_block_list.values()    ).index(["conv.bias", "conv.weight"]    )
																												lowercase__  :	Optional[int]        =							unet_state_dict[
																												    F"""output_blocks.{i}.{index}.conv.weight"""
																												]
																												lowercase__  :	Optional[int]        =							unet_state_dict[
																												    F"""output_blocks.{i}.{index}.conv.bias"""
																												]

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

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

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

							return new_checkpoint


if __name__ == "__main__":
			__a  :		Dict		     =   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.''')
			__a  :		Dict		     =   parser.parse_args()

			__a  :		Dict		     =   torch.load(args.checkpoint_path, map_location='''cpu''')
			__a  :		Tuple		     =   UNetaDConditionModel()

			__a  :		str		     =   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

			__a  :		int		     =   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
			__a  :		List[str]		     =   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_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			list[int]:
							if hue < 0 or hue > 3_60:
														raise Exception("hue should be between 0 and 360"    )

							if saturation < 0 or saturation > 1:
														raise Exception("saturation should be between 0 and 1"    )

							if value < 0 or value > 1:
														raise Exception("value should be between 0 and 1"    )

							lowercase__  :	List[Any]        =							value * saturation
							lowercase__  :	Optional[int]        =							hue / 60
							lowercase__  :	int        =							chroma * (1 - abs(hue_section % 2 - 1    ))
							lowercase__  :	Any        =							value - chroma

							if hue_section >= 0 and hue_section <= 1:
														lowercase__  :	int        =							round(2_55 * (chroma + match_value)    )
														lowercase__  :	Tuple        =							round(2_55 * (second_largest_component + match_value)    )
														lowercase__  :	Optional[int]        =							round(2_55 * (match_value)    )
							elif hue_section > 1 and hue_section <= 2:
														lowercase__  :	List[str]        =							round(2_55 * (second_largest_component + match_value)    )
														lowercase__  :	List[str]        =							round(2_55 * (chroma + match_value)    )
														lowercase__  :	List[str]        =							round(2_55 * (match_value)    )
							elif hue_section > 2 and hue_section <= 3:
														lowercase__  :	Any        =							round(2_55 * (match_value)    )
														lowercase__  :	List[str]        =							round(2_55 * (chroma + match_value)    )
														lowercase__  :	List[str]        =							round(2_55 * (second_largest_component + match_value)    )
							elif hue_section > 3 and hue_section <= 4:
														lowercase__  :	str        =							round(2_55 * (match_value)    )
														lowercase__  :	Any        =							round(2_55 * (second_largest_component + match_value)    )
														lowercase__  :	Dict        =							round(2_55 * (chroma + match_value)    )
							elif hue_section > 4 and hue_section <= 5:
														lowercase__  :	Tuple        =							round(2_55 * (second_largest_component + match_value)    )
														lowercase__  :	List[str]        =							round(2_55 * (match_value)    )
														lowercase__  :	Dict        =							round(2_55 * (chroma + match_value)    )
							else:
														lowercase__  :	List[Any]        =							round(2_55 * (chroma + match_value)    )
														lowercase__  :	Dict        =							round(2_55 * (match_value)    )
														lowercase__  :	Optional[int]        =							round(2_55 * (second_largest_component + match_value)    )

							return [red, green, blue]
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			list[float]:
							if red < 0 or red > 2_55:
														raise Exception("red should be between 0 and 255"    )

							if green < 0 or green > 2_55:
														raise Exception("green should be between 0 and 255"    )

							if blue < 0 or blue > 2_55:
														raise Exception("blue should be between 0 and 255"    )

							lowercase__  :	List[str]        =							red / 2_55
							lowercase__  :	str        =							green / 2_55
							lowercase__  :	Any        =							blue / 2_55
							lowercase__  :	Optional[int]        =							max(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
							lowercase__  :	List[str]        =							value - min(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Optional[Any]        =							0 if value == 0 else chroma / value

							if chroma == 0:
														lowercase__  :	Dict        =							0.0
							elif value == float_red:
														lowercase__  :	Union[str, Any]        =							60 * (0 + (float_green - float_blue) / chroma)
							elif value == float_green:
														lowercase__  :	Any        =							60 * (2 + (float_blue - float_red) / chroma)
							else:
														lowercase__  :	Any        =							60 * (4 + (float_red - float_green) / chroma)

							lowercase__  :	Tuple        =							(hue + 3_60) % 3_60

							return [hue, saturation, value]




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			bool:
							lowercase__  :	Optional[Any]        =							abs(hsv_a[0] - hsv_a[0]    ) < 0.2
							lowercase__  :	Optional[int]        =							abs(hsv_a[1] - hsv_a[1]    ) < 0.002
							lowercase__  :	int        =							abs(hsv_a[2] - hsv_a[2]    ) < 0.002

							return check_hue and check_saturation and check_value