
"""simple docstring"""







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=1 )  ->     Optional[int]:







							"""simple docstring"""


							if n_shave_prefix_segments >= 0:
														return ".".join(path.split("." )[n_shave_prefix_segments:] )
							else:
														return ".".join(path.split("." )[:n_shave_prefix_segments] )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=0 )  ->     List[str]:







							"""simple docstring"""


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

														__snake_case     			=       new_item.replace("out_layers.0"							,    "norm2" )
														__snake_case     			=       new_item.replace("out_layers.3"							,    "conv2" )

														__snake_case     			=       new_item.replace("emb_layers.1"							,    "time_emb_proj" )
														__snake_case     			=       new_item.replace("skip_connection"							,    "conv_shortcut" )

														__snake_case     			=       shave_segments(SCREAMING_SNAKE_CASE							,    n_shave_prefix_segments=SCREAMING_SNAKE_CASE )

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

							return mapping
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=0 )  ->     List[str]:







							"""simple docstring"""


							__snake_case     			=       []
							for old_item in old_list:
														__snake_case     			=       old_item

														__snake_case     			=       new_item.replace("norm.weight"							,    "group_norm.weight" )
														__snake_case     			=       new_item.replace("norm.bias"							,    "group_norm.bias" )

														__snake_case     			=       new_item.replace("proj_out.weight"							,    "proj_attn.weight" )
														__snake_case     			=       new_item.replace("proj_out.bias"							,    "proj_attn.bias" )

														__snake_case     			=       shave_segments(SCREAMING_SNAKE_CASE							,    n_shave_prefix_segments=SCREAMING_SNAKE_CASE )

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

							return mapping
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=None )  ->     Dict:







							"""simple docstring"""


							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():
																					__snake_case     			=       old_checkpoint[path]
																					__snake_case     			=       old_tensor.shape[0] // 3

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

																					__snake_case     			=       old_tensor.shape[0] // config["num_head_channels"] // 3

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

																					__snake_case     			=       query.reshape(SCREAMING_SNAKE_CASE )
																					__snake_case     			=       key.reshape(SCREAMING_SNAKE_CASE )
																					__snake_case     			=       value.reshape(SCREAMING_SNAKE_CASE )

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

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												__snake_case     			=       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:
																					__snake_case     			=       old_checkpoint[path["old"]][:, :, 0]
														else:
																					__snake_case     			=       old_checkpoint[path["old"]]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       {}

							__snake_case     			=       checkpoint["time_embed.0.weight"]
							__snake_case     			=       checkpoint["time_embed.0.bias"]
							__snake_case     			=       checkpoint["time_embed.2.weight"]
							__snake_case     			=       checkpoint["time_embed.2.bias"]

							__snake_case     			=       checkpoint["input_blocks.0.0.weight"]
							__snake_case     			=       checkpoint["input_blocks.0.0.bias"]

							__snake_case     			=       checkpoint["out.0.weight"]
							__snake_case     			=       checkpoint["out.0.bias"]
							__snake_case     			=       checkpoint["out.2.weight"]
							__snake_case     			=       checkpoint["out.2.bias"]

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

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

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

							for i in range(1							,    SCREAMING_SNAKE_CASE ):
														__snake_case     			=       (i - 1) // (config["num_res_blocks"] + 1)
														__snake_case     			=       (i - 1) % (config["num_res_blocks"] + 1)

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

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

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

							__snake_case     			=       middle_blocks[0]
							__snake_case     			=       middle_blocks[1]
							__snake_case     			=       middle_blocks[2]

							__snake_case     			=       renew_resnet_paths(SCREAMING_SNAKE_CASE )
							assign_to_checkpoint(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    config=SCREAMING_SNAKE_CASE )

							__snake_case     			=       renew_resnet_paths(SCREAMING_SNAKE_CASE )
							assign_to_checkpoint(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    config=SCREAMING_SNAKE_CASE )

							__snake_case     			=       renew_attention_paths(SCREAMING_SNAKE_CASE )
							__snake_case     			=       {
							    "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(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    attention_paths_to_split=SCREAMING_SNAKE_CASE							,    config=SCREAMING_SNAKE_CASE )

							for i in range(SCREAMING_SNAKE_CASE ):
														__snake_case     			=       i // (config["num_res_blocks"] + 1)
														__snake_case     			=       i % (config["num_res_blocks"] + 1)
														__snake_case     			=       [shave_segments(SCREAMING_SNAKE_CASE							,    2 ) for name in output_blocks[i]]
														__snake_case     			=       {}

														for layer in output_block_layers:
																					__snake_case			,   __snake_case     			=       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:
																												__snake_case     			=       [layer_name]

														if len(SCREAMING_SNAKE_CASE ) > 1:
																					__snake_case     			=       [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
																					__snake_case     			=       [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

																					__snake_case     			=       renew_resnet_paths(SCREAMING_SNAKE_CASE )
																					__snake_case     			=       renew_resnet_paths(SCREAMING_SNAKE_CASE )

																					__snake_case     			=       {"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 )

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

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

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

																												__snake_case     			=       checkpoint[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(
		    """--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.""")

		_SCREAMING_SNAKE_CASE	       =      parser.parse_args()

		_SCREAMING_SNAKE_CASE	       =      torch.load(args.checkpoint_path)

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

		_SCREAMING_SNAKE_CASE	       =      convert_ldm_checkpoint(checkpoint, config)

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

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

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

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


"""simple docstring"""







from __future__ import annotations

from typing import Any
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							if not postfix_notation:
														return 0

							__snake_case     			=       {"+", "-", "*", "/"}
							__snake_case     			=       []

							for token in postfix_notation:
														if token in operations:
																					__snake_case			,   __snake_case     			=       stack.pop(), stack.pop()
																					if token == "+":
																												stack.append(a + b )
																					elif token == "-":
																												stack.append(a - b )
																					elif token == "*":
																												stack.append(a * b )
																					else:
																												if a * b < 0 and a % b != 0:
																																			stack.append(a // b + 1 )
																												else:
																																			stack.append(a // b )
														else:
																					stack.append(int(SCREAMING_SNAKE_CASE ) )

							return stack.pop()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
