





'''simple docstring'''


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     Any=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    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     Dict=0     )   ->    Tuple:
			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(__lowerCamelCase					,  n_shave_prefix_segments=__lowerCamelCase     )

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

			return mapping







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Dict=0     )   ->    Any:
			a__     = []
			for old_item in old_list:
						a__     = old_item

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

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

						a__     = shave_segments(__lowerCamelCase					,  n_shave_prefix_segments=__lowerCamelCase     )

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

			return mapping







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Union[str, Any]=None					,  __lowerCamelCase  :     Union[str, Any]=None					,  __lowerCamelCase  :     List[str]=None     )   ->    Dict:
			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():
									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(__lowerCamelCase     )
									a__     = key.reshape(__lowerCamelCase     )
									a__     = value.reshape(__lowerCamelCase     )

			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

						# Global renaming happens here
						a__     = new_path.replace("middle_block.0"					,  "mid_block.resnets.0"     )
						a__     = new_path.replace("middle_block.1"					,  "mid_block.attentions.0"     )
						a__     = new_path.replace("middle_block.2"					,  "mid_block.resnets.1"     )

						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
						if "proj_attn.weight" in new_path:
									a__     = old_checkpoint[path["old"]][:, :, 0]
						else:
									a__     = old_checkpoint[path["old"]]







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = {}

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

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

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

			# Retrieves the keys for the input blocks only
			a__     = len({".".join(layer.split("."     )[:2]     ) for layer in checkpoint if "input_blocks" in layer}     )
			a__     = {
			    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
			a__     = len({".".join(layer.split("."     )[:2]     ) for layer in checkpoint if "middle_block" in layer}     )
			a__     = {
			    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
			a__     = len({".".join(layer.split("."     )[:2]     ) for layer in checkpoint if "output_blocks" in layer}     )
			a__     = {
			    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     ):
						a__     = (i - 1) // (config["num_res_blocks"] + 1)
						a__     = (i - 1) % (config["num_res_blocks"] + 1)

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

						a__     = renew_resnet_paths(__lowerCamelCase     )
						a__     = {"old": f'''input_blocks.{i}.0''', "new": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						a__     = {"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     ):
									a__     = renew_attention_paths(__lowerCamelCase     )
									a__     = {
									    "old": f'''input_blocks.{i}.1''',
									    "new": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
									}
									a__     = {
									    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					,  )

			a__     = middle_blocks[0]
			a__     = middle_blocks[1]
			a__     = middle_blocks[2]

			a__     = renew_resnet_paths(__lowerCamelCase     )
			assign_to_checkpoint(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  config=__lowerCamelCase     )

			a__     = renew_resnet_paths(__lowerCamelCase     )
			assign_to_checkpoint(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  config=__lowerCamelCase     )

			a__     = renew_attention_paths(__lowerCamelCase     )
			a__     = {
			    "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     ):
						a__     = i // (config["num_res_blocks"] + 1)
						a__     = i % (config["num_res_blocks"] + 1)
						a__     = [shave_segments(__lowerCamelCase					,  2     ) for name in output_blocks[i]]
						a__     = {}

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

						if len(__lowerCamelCase     ) > 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__     = renew_resnet_paths(__lowerCamelCase     )
									a__     = renew_resnet_paths(__lowerCamelCase     )

									a__     = {"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():
												a__     = list(output_block_list.values()     ).index(["conv.weight", "conv.bias"]     )
												a__     = checkpoint[
												    f'''output_blocks.{i}.{index}.conv.weight'''
												]
												a__     = checkpoint[
												    f'''output_blocks.{i}.{index}.conv.bias'''
												]

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

									if len(__lowerCamelCase     ):
												a__     = renew_attention_paths(__lowerCamelCase     )
												a__     = {
												    "old": f'''output_blocks.{i}.1''',
												    "new": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												a__     = {
												    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:
									a__     = renew_resnet_paths(__lowerCamelCase					,  n_shave_prefix_segments=1     )
									for path in resnet_0_paths:
												a__     = ".".join(["output_blocks", str(__lowerCamelCase     ), path["old"]]     )
												a__     = ".".join(["up_blocks", str(__lowerCamelCase     ), "resnets", str(__lowerCamelCase     ), path["new"]]     )

												a__     = checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
		lowerCAmelCase_   :   List[str]       =					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.")

		lowerCAmelCase_   :   Tuple       =					parser.parse_args()

		lowerCAmelCase_   :   Optional[Any]       =					torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				lowerCAmelCase_   :   List[str]       =					json.loads(f.read())

		lowerCAmelCase_   :   Any       =					convert_ldm_checkpoint(checkpoint, config)

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

		lowerCAmelCase_   :   Any       =					UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

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

				lowerCAmelCase_   :   Optional[Any]       =					LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)










'''simple docstring'''


import argparse
import os
import re

import packaging.version


lowerCAmelCase_   :   Optional[int]       =					"examples/"
lowerCAmelCase_   :   Optional[int]       =					{
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
lowerCAmelCase_   :   Optional[int]       =					{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
lowerCAmelCase_   :   List[Any]       =					"README.md"







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[Any]     )   ->    str:
			with open(__lowerCamelCase					,  "r"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						a__     = f.read()
			a__   ,				a__     = REPLACE_PATTERNS[pattern]
			a__     = replace.replace("VERSION"					,  __lowerCamelCase     )
			a__     = re_pattern.sub(__lowerCamelCase					,  __lowerCamelCase     )
			with open(__lowerCamelCase					,  "w"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						f.write(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]     )   ->    Dict:
			for folder, directories, fnames in os.walk(__lowerCamelCase     ):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove("research_projects"     )
						if "legacy" in directories:
									directories.remove("legacy"     )
						for fname in fnames:
									if fname.endswith(".py"     ):
												update_version_in_file(os.path.join(__lowerCamelCase					,  __lowerCamelCase     )					,  __lowerCamelCase					,  pattern="examples"     )







def    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     Any=False     )   ->    Optional[Any]:
			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
			if not patch:
						update_version_in_examples(__lowerCamelCase     )







def    _lowerCamelCase  ()   ->    Tuple:
			a__     = "🤗 Transformers currently provides the following architectures"
			a__     = "1. Want to contribute a new model?"
			with open(__lowerCamelCase					,  "r"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						a__     = f.readlines()

			# Find the start of the list.
			a__     = 0
			while not lines[start_index].startswith(_start_prompt     ):
						start_index += 1
			start_index += 1

			a__     = start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt     ):
						if lines[index].startswith("1."     ):
									a__     = lines[index].replace(
									    "https://huggingface.co/docs/transformers/main/model_doc"					,  "https://huggingface.co/docs/transformers/model_doc"					,  )
						index += 1

			with open(__lowerCamelCase					,  "w"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						f.writelines(__lowerCamelCase     )







def    _lowerCamelCase  ()   ->    Tuple:
			with open(REPLACE_FILES["init"]					,  "r"     ) as f:
						a__     = f.read()
			a__     = REPLACE_PATTERNS["init"][0].search(__lowerCamelCase     ).groups()[0]
			return packaging.version.parse(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Any=False     )   ->    int:
			a__     = get_version()
			if patch and default_version.is_devrelease:
						raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"     )
			if default_version.is_devrelease:
						a__     = default_version.base_version
			elif patch:
						a__     = f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
			else:
						a__     = f'''{default_version.major}.{default_version.minor + 1}.0'''

			# Now let's ask nicely if that's the right one.
			a__     = input(f'''Which version are you releasing? [{default_version}]'''     )
			if len(__lowerCamelCase     ) == 0:
						a__     = default_version

			print(f'''Updating version to {version}.'''     )
			global_version_update(__lowerCamelCase					,  patch=__lowerCamelCase     )
			if not patch:
						print("Cleaning main README, don't forget to run `make fix-copies`."     )
						clean_main_ref_in_model_list()







def    _lowerCamelCase  ()   ->    Tuple:
			a__     = get_version()
			a__     = f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
			a__     = current_version.base_version

			# Check with the user we got that right.
			a__     = input(f'''Which version are we developing now? [{dev_version}]'''     )
			if len(__lowerCamelCase     ) == 0:
						a__     = dev_version

			print(f'''Updating version to {version}.'''     )
			global_version_update(__lowerCamelCase     )
			print("Cleaning main README, don't forget to run `make fix-copies`."     )
			clean_main_ref_in_model_list()


if __name__ == "__main__":
		lowerCAmelCase_   :   List[str]       =					argparse.ArgumentParser()
		parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
		parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
		lowerCAmelCase_   :   int       =					parser.parse_args()
		if not args.post_release:
				pre_release_work(patch=args.patch)
		elif args.patch:
				print("Nothing to do after a patch :-)")
		else:
				post_release_work()



