
'''simple docstring'''







import argparse

import torch

from diffusers import UNetaDConditionModel



def       _a		(						_SCREAMING_SNAKE_CASE		: Union[str, Any]						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Tuple=None						,      _SCREAMING_SNAKE_CASE		: Optional[Any]=None						,      _SCREAMING_SNAKE_CASE		: Optional[Any]=None	):
						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():
																		_SCREAMING_SNAKE_CASE					=			old_checkpoint[path]
																		_SCREAMING_SNAKE_CASE					=			old_tensor.shape[0] // 3

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

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

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

																		_SCREAMING_SNAKE_CASE					=			query.reshape(_SCREAMING_SNAKE_CASE	)
																		_SCREAMING_SNAKE_CASE					=			key.reshape(_SCREAMING_SNAKE_CASE	)
																		_SCREAMING_SNAKE_CASE					=			value.reshape(_SCREAMING_SNAKE_CASE	)

						for path in paths:
												_SCREAMING_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

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								_SCREAMING_SNAKE_CASE					=			new_path.replace(replacement["old"]						,      replacement["new"]	)

        # proj_attn.weight has to be converted from conv 1D to linear
												_SCREAMING_SNAKE_CASE					=			old_checkpoint[path["old"]]
												_SCREAMING_SNAKE_CASE					=			["proj_attn.weight"]
												_SCREAMING_SNAKE_CASE					=			["proj_out.weight", "proj_in.weight"]
												if any(k in new_path for k in names	):
																		_SCREAMING_SNAKE_CASE					=			weight[:, :, 0]
												elif any(k in new_path for k in names_a	) and len(weight.shape	) > 2 and ".attentions." not in new_path:
																		_SCREAMING_SNAKE_CASE					=			weight[:, :, 0]
												else:
																		_SCREAMING_SNAKE_CASE					=			weight



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[Any]						,      _SCREAMING_SNAKE_CASE		: Any=0	):
						_SCREAMING_SNAKE_CASE					=			[]
						for old_item in old_list:
												_SCREAMING_SNAKE_CASE					=			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		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: str=1	):
						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		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[Any]=0	):
						_SCREAMING_SNAKE_CASE					=			[]
						for old_item in old_list:
												mapping.append({"old": old_item, "new": old_item}	)

						return mapping



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: str=0	):
						_SCREAMING_SNAKE_CASE					=			[]
						for old_item in old_list:
												_SCREAMING_SNAKE_CASE					=			old_item.replace("in_layers.0"						,      "norm1"	)
												_SCREAMING_SNAKE_CASE					=			new_item.replace("in_layers.2"						,      "conv1"	)

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

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

												_SCREAMING_SNAKE_CASE					=			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       _a		(						_SCREAMING_SNAKE_CASE		: str						,      _SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: List[Any]=None						,      _SCREAMING_SNAKE_CASE		: Optional[int]=False	):
						_SCREAMING_SNAKE_CASE					=			{}
						_SCREAMING_SNAKE_CASE					=			list(checkpoint.keys()	)

						_SCREAMING_SNAKE_CASE					=			"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"	):
																								_SCREAMING_SNAKE_CASE					=			"model_ema." + "".join(key.split("."	)[1:]	)
																								_SCREAMING_SNAKE_CASE					=			checkpoint.pop(_SCREAMING_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:
																		_SCREAMING_SNAKE_CASE					=			checkpoint.pop(_SCREAMING_SNAKE_CASE	)

						_SCREAMING_SNAKE_CASE					=			{}

						_SCREAMING_SNAKE_CASE					=			unet_state_dict["time_embed.0.weight"]
						_SCREAMING_SNAKE_CASE					=			unet_state_dict["time_embed.0.bias"]
						_SCREAMING_SNAKE_CASE					=			unet_state_dict["time_embed.2.weight"]
						_SCREAMING_SNAKE_CASE					=			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":
												_SCREAMING_SNAKE_CASE					=			unet_state_dict["label_emb.0.0.weight"]
												_SCREAMING_SNAKE_CASE					=			unet_state_dict["label_emb.0.0.bias"]
												_SCREAMING_SNAKE_CASE					=			unet_state_dict["label_emb.0.2.weight"]
												_SCREAMING_SNAKE_CASE					=			unet_state_dict["label_emb.0.2.bias"]
						else:
												raise NotImplementedError(F'Not implemented `class_embed_type`: {config["class_embed_type"]}'	)

						_SCREAMING_SNAKE_CASE					=			unet_state_dict["input_blocks.0.0.weight"]
						_SCREAMING_SNAKE_CASE					=			unet_state_dict["input_blocks.0.0.bias"]

						_SCREAMING_SNAKE_CASE					=			[v for v in unet_state_dict if v.startswith("input_blocks.0.1"	)]
						_SCREAMING_SNAKE_CASE					=			renew_attention_paths(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			{"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	)

						_SCREAMING_SNAKE_CASE					=			unet_state_dict["out.0.weight"]
						_SCREAMING_SNAKE_CASE					=			unet_state_dict["out.0.bias"]
						_SCREAMING_SNAKE_CASE					=			unet_state_dict["out.2.weight"]
						_SCREAMING_SNAKE_CASE					=			unet_state_dict["out.2.bias"]

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

												_SCREAMING_SNAKE_CASE					=			[
												    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
												]
												_SCREAMING_SNAKE_CASE					=			[key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
												_SCREAMING_SNAKE_CASE					=			[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:
																		_SCREAMING_SNAKE_CASE					=			unet_state_dict.pop(
																		    F'input_blocks.{i}.op.weight'	)
																		_SCREAMING_SNAKE_CASE					=			unet_state_dict.pop(
																		    F'input_blocks.{i}.op.bias'	)

												_SCREAMING_SNAKE_CASE					=			renew_resnet_paths(_SCREAMING_SNAKE_CASE	)
												_SCREAMING_SNAKE_CASE					=			{"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	)

												_SCREAMING_SNAKE_CASE					=			[key for key in resnets if "temopral_conv" in key]
												_SCREAMING_SNAKE_CASE					=			renew_temp_conv_paths(_SCREAMING_SNAKE_CASE	)
												_SCREAMING_SNAKE_CASE					=			{
												    "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	):
																		_SCREAMING_SNAKE_CASE					=			renew_attention_paths(_SCREAMING_SNAKE_CASE	)
																		_SCREAMING_SNAKE_CASE					=			{"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	):
																		_SCREAMING_SNAKE_CASE					=			renew_attention_paths(_SCREAMING_SNAKE_CASE	)
																		_SCREAMING_SNAKE_CASE					=			{
																		    "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	)

						_SCREAMING_SNAKE_CASE					=			middle_blocks[0]
						_SCREAMING_SNAKE_CASE					=			[key for key in resnet_a if "temopral_conv" in key]
						_SCREAMING_SNAKE_CASE					=			middle_blocks[1]
						_SCREAMING_SNAKE_CASE					=			middle_blocks[2]
						_SCREAMING_SNAKE_CASE					=			middle_blocks[3]
						_SCREAMING_SNAKE_CASE					=			[key for key in resnet_a if "temopral_conv" in key]

						_SCREAMING_SNAKE_CASE					=			renew_resnet_paths(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			{"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]	)

						_SCREAMING_SNAKE_CASE					=			renew_temp_conv_paths(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			{"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]	)

						_SCREAMING_SNAKE_CASE					=			renew_resnet_paths(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			{"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]	)

						_SCREAMING_SNAKE_CASE					=			renew_temp_conv_paths(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			{"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]	)

						_SCREAMING_SNAKE_CASE					=			renew_attention_paths(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			{"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	)

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

												for layer in output_block_layers:
																		_SCREAMING_SNAKE_CASE,				_SCREAMING_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:
																								_SCREAMING_SNAKE_CASE					=			[layer_name]

												if len(_SCREAMING_SNAKE_CASE	) > 1:
																		_SCREAMING_SNAKE_CASE					=			[key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
																		_SCREAMING_SNAKE_CASE					=			[key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
																		_SCREAMING_SNAKE_CASE					=			[key for key in output_blocks[i] if F'output_blocks.{i}.2' in key]

																		_SCREAMING_SNAKE_CASE					=			renew_resnet_paths(_SCREAMING_SNAKE_CASE	)
																		_SCREAMING_SNAKE_CASE					=			renew_resnet_paths(_SCREAMING_SNAKE_CASE	)

																		_SCREAMING_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	)

																		_SCREAMING_SNAKE_CASE					=			[key for key in resnets if "temopral_conv" in key]
																		_SCREAMING_SNAKE_CASE					=			renew_temp_conv_paths(_SCREAMING_SNAKE_CASE	)
																		_SCREAMING_SNAKE_CASE					=			{
																		    "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	)

																		_SCREAMING_SNAKE_CASE					=			{k: sorted(_SCREAMING_SNAKE_CASE	) for k, v in output_block_list.items()}
																		if ["conv.bias", "conv.weight"] in output_block_list.values():
																								_SCREAMING_SNAKE_CASE					=			list(output_block_list.values()	).index(["conv.bias", "conv.weight"]	)
																								_SCREAMING_SNAKE_CASE					=			unet_state_dict[
																								    F'output_blocks.{i}.{index}.conv.weight'
																								]
																								_SCREAMING_SNAKE_CASE					=			unet_state_dict[
																								    F'output_blocks.{i}.{index}.conv.bias'
																								]

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

																		if len(_SCREAMING_SNAKE_CASE	):
																								_SCREAMING_SNAKE_CASE					=			renew_attention_paths(_SCREAMING_SNAKE_CASE	)
																								_SCREAMING_SNAKE_CASE					=			{
																								    "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	):
																								_SCREAMING_SNAKE_CASE					=			renew_attention_paths(_SCREAMING_SNAKE_CASE	)
																								_SCREAMING_SNAKE_CASE					=			{
																								    "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:
																		_SCREAMING_SNAKE_CASE					=			renew_resnet_paths(_SCREAMING_SNAKE_CASE						,      n_shave_prefix_segments=1	)
																		for path in resnet_0_paths:
																								_SCREAMING_SNAKE_CASE					=			".".join(["output_blocks", str(_SCREAMING_SNAKE_CASE	), path["old"]]	)
																								_SCREAMING_SNAKE_CASE					=			".".join(["up_blocks", str(_SCREAMING_SNAKE_CASE	), "resnets", str(_SCREAMING_SNAKE_CASE	), path["new"]]	)
																								_SCREAMING_SNAKE_CASE					=			unet_state_dict[old_path]

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

						return new_checkpoint


if __name__ == "__main__":
						_snake_case					: Union[str, Any]     			=					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.""")
						_snake_case					: Optional[Any]     			=					parser.parse_args()

						_snake_case					: int     			=					torch.load(args.checkpoint_path, map_location="""cpu""")
						_snake_case					: Tuple     			=					UNetaDConditionModel()

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

						_snake_case					: Optional[int]     			=					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
						_snake_case					: int     			=					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 os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						import torch

						from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCAmelCase			(					unittest.TestCase      ):




		@slow
		@unittest.skip("Model is not available."   )
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			"nvidia/megatron-gpt2-345m/"
								if "MYDIR" in os.environ:
														_SCREAMING_SNAKE_CASE					=			os.path.join(os.environ["MYDIR"]       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			GPTaLMHeadModel.from_pretrained(UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.half()

								_SCREAMING_SNAKE_CASE					=			torch.tensor(
								    [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]       ,    device=UpperCamelCase       ,    dtype=torch.long       ,    )

								with torch.no_grad():
														_SCREAMING_SNAKE_CASE					=			model(UpperCamelCase   ).logits

								_SCREAMING_SNAKE_CASE					=			torch.Size((1, 9, 50_257)   )
								self.assertEqual(output.shape       ,    UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			torch.tensor(
								    [
								        4.94_14,
								        -0.29_20,
								        -1.21_48,
								        -4.02_73,
								        -0.51_61,
								        -5.21_09,
								        -1.24_12,
								        -1.83_01,
								        -1.77_34,
								        -4.71_48,
								        -0.23_17,
								        -1.08_11,
								        -2.17_77,
								        0.41_41,
								        -3.79_69,
								        -4.05_86,
								        -2.53_32,
								        -3.38_09,
								        4.38_67,
								    ]       ,    device=UpperCamelCase       ,    dtype=torch.half       ,    )

								for i in range(19   ):
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			8 * i // 17, 2_792 * i  # along the diagonal
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			output[0, r, c], expected_diag[i]
														_SCREAMING_SNAKE_CASE					=			F'row={r} col={c} computed={computed} expected={expected}'
														self.assertAlmostEqual(UpperCamelCase       ,    UpperCamelCase       ,    delta=1e-4       ,    msg=UpperCamelCase   )