


"""simple docstring"""


import argparse

import torch

from diffusers import UNetaDConditionModel






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase=None  ,      __lowercase=None  ,      __lowercase=None )						->						Tuple:
				assert isinstance(snake_case__  ,      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():
												_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(snake_case__ )
												_A       	=      key.reshape(snake_case__ )
												_A       	=      value.reshape(snake_case__ )

				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   a__			(					__lowercase  ,      __lowercase=0 )						->						List[Any]:
				_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   a__			(					__lowercase  ,      __lowercase=1 )						->						Optional[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   a__			(					__lowercase  ,      __lowercase=0 )						->						Dict:
				_A       	=      []
				for old_item in old_list:
								mapping.append({"old": old_item, "new": old_item} )

				return mapping






def   a__			(					__lowercase  ,      __lowercase=0 )						->						Any:
				_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(snake_case__  ,      n_shave_prefix_segments=snake_case__ )

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

				return mapping






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=None  ,      __lowercase=False )						->						List[str]:
				_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(snake_case__ )
				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(snake_case__ )

				_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(snake_case__ )
				_A       	=      {"old": "input_blocks.0.1", "new": "transformer_in"}
				assign_to_checkpoint(snake_case__  ,      snake_case__  ,      snake_case__  ,      additional_replacements=[meta_path]  ,      config=snake_case__ )

				_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(snake_case__ )
				}

				# 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(snake_case__ )
				}

				# 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(snake_case__ )
				}

				for i in range(1  ,      snake_case__ ):
								_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(snake_case__ )
								_A       	=      {"old": f"""input_blocks.{i}.0""", "new": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
								assign_to_checkpoint(
								    snake_case__  ,      snake_case__  ,      snake_case__  ,      additional_replacements=[meta_path]  ,      config=snake_case__ )

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

								if len(snake_case__ ):
												_A       	=      renew_attention_paths(snake_case__ )
												_A       	=      {"old": f"""input_blocks.{i}.1""", "new": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
												assign_to_checkpoint(
												    snake_case__  ,      snake_case__  ,      snake_case__  ,      additional_replacements=[meta_path]  ,      config=snake_case__ )

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

				_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(snake_case__ )
				_A       	=      {"old": "middle_block.0", "new": "mid_block.resnets.0"}
				assign_to_checkpoint(
				    snake_case__  ,      snake_case__  ,      snake_case__  ,      config=snake_case__  ,      additional_replacements=[meta_path] )

				_A       	=      renew_temp_conv_paths(snake_case__ )
				_A       	=      {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
				assign_to_checkpoint(
				    snake_case__  ,      snake_case__  ,      snake_case__  ,      config=snake_case__  ,      additional_replacements=[meta_path] )

				_A       	=      renew_resnet_paths(snake_case__ )
				_A       	=      {"old": "middle_block.3", "new": "mid_block.resnets.1"}
				assign_to_checkpoint(
				    snake_case__  ,      snake_case__  ,      snake_case__  ,      config=snake_case__  ,      additional_replacements=[meta_path] )

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

				_A       	=      renew_attention_paths(snake_case__ )
				_A       	=      {"old": "middle_block.1", "new": "mid_block.attentions.0"}
				assign_to_checkpoint(
				    snake_case__  ,      snake_case__  ,      snake_case__  ,      additional_replacements=[meta_path]  ,      config=snake_case__ )

				_A       	=      renew_attention_paths(snake_case__ )
				_A       	=      {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
				assign_to_checkpoint(
				    snake_case__  ,      snake_case__  ,      snake_case__  ,      additional_replacements=[meta_path]  ,      config=snake_case__ )

				for i in range(snake_case__ ):
								_A       	=      i // (config["layers_per_block"] + 1)
								_A       	=      i % (config["layers_per_block"] + 1)
								_A       	=      [shave_segments(snake_case__  ,      2 ) for name in output_blocks[i]]
								_A       	=      {}

								for layer in output_block_layers:
												_A	,		_A       	=      layer.split("." )[0], shave_segments(snake_case__  ,      1 )
												if layer_id in output_block_list:
																output_block_list[layer_id].append(snake_case__ )
												else:
																_A       	=      [layer_name]

								if len(snake_case__ ) > 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(snake_case__ )
												_A       	=      renew_resnet_paths(snake_case__ )

												_A       	=      {"old": f"""output_blocks.{i}.0""", "new": f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(
												    snake_case__  ,      snake_case__  ,      snake_case__  ,      additional_replacements=[meta_path]  ,      config=snake_case__ )

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

												_A       	=      {k: sorted(snake_case__ ) 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(snake_case__ ) == 2:
																				_A       	=      []

												if len(snake_case__ ):
																_A       	=      renew_attention_paths(snake_case__ )
																_A       	=      {
																    "old": f"""output_blocks.{i}.1""",
																    "new": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																}
																assign_to_checkpoint(
																    snake_case__  ,      snake_case__  ,      snake_case__  ,      additional_replacements=[meta_path]  ,      config=snake_case__ )

												if len(snake_case__ ):
																_A       	=      renew_attention_paths(snake_case__ )
																_A       	=      {
																    "old": f"""output_blocks.{i}.2""",
																    "new": f"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																}
																assign_to_checkpoint(
																    snake_case__  ,      snake_case__  ,      snake_case__  ,      additional_replacements=[meta_path]  ,      config=snake_case__ )
								else:
												_A       	=      renew_resnet_paths(snake_case__  ,      n_shave_prefix_segments=1 )
												for path in resnet_0_paths:
																_A       	=      ".".join(["output_blocks", str(snake_case__ ), path["old"]] )
																_A       	=      ".".join(["up_blocks", str(snake_case__ ), "resnets", str(snake_case__ ), 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(snake_case__ ), str(snake_case__ ), "temopral_conv", pruned_path] )
																_A       	=      ".".join(["up_blocks", str(snake_case__ ), "temp_convs", str(snake_case__ ), pruned_path] )
																_A       	=      unet_state_dict[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("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
		a_											=     parser.parse_args()

		a_											=     torch.load(args.checkpoint_path, map_location="cpu")
		a_											=     UNetaDConditionModel()

		a_											=     convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

		a_											=     set(unet.state_dict().keys()) - set(converted_ckpt.keys())
		a_											=     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 --

import sys

sys.set_int_max_str_digits(0)  # type: ignore





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    if not f:
        return f

    # mark first 9 numbers
    A_								=					int(str(snake_case__ )[:9] )

    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10

    # check first 9 numbers for pandigitality

    for x in range(9 ):
        if not check_front[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				)->			int:
    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers

    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers mod 1e9

    # mod m=1e9, done for fast optimisation
    A_								=					[0] * 1000000
    A_								=					1000000000

    for x in range(1000000 ):
        A_								=					(aa + ba) % m
        A_								=					ba % m
        A_								=					ca % m
        if checka(snake_case__ ):
            A_								=					1

    for x in range(1000000 ):
        A_								=					a + b
        A_								=					b
        A_								=					c
        # perform check only if in tocheck
        if tocheck[x + 3] and check(snake_case__ ):
            return x + 3  # first 2 already done
    return -1


if __name__ == "__main__":
     print(f"""{solution() = }""")






