
'''simple docstring'''







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase					:							List[Any]   =      False
lowercase					:							Optional[Any]   =      True
lowercase					:							Any   =      False


if __name__ == "__main__":
					lowercase					:							Optional[Any]   =      argparse.ArgumentParser()

					parser.add_argument(
					    "--repo_path",
					    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.")

					lowercase					:							int   =      parser.parse_args()

					lowercase					:							Dict   =      {
					    "image_size": "sample_size",
					    "num_res_blocks": "layers_per_block",
					    "block_channels": "block_out_channels",
					    "down_blocks": "down_block_types",
					    "up_blocks": "up_block_types",
					    "downscale_freq_shift": "freq_shift",
					    "resnet_num_groups": "norm_num_groups",
					    "resnet_act_fn": "act_fn",
					    "resnet_eps": "norm_eps",
					    "num_head_channels": "attention_head_dim",
					}

					lowercase					:							Optional[int]   =      {
					    "time_steps": "time_proj",
					    "mid": "mid_block",
					    "downsample_blocks": "down_blocks",
					    "upsample_blocks": "up_blocks",
					}

					lowercase					:							str   =      "" if has_file(args.repo_path, "config.json") else "unet"

					with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
										lowercase					:							List[Any]   =      reader.read()
										lowercase					:							Any   =      json.loads(text)

					if do_only_config:
										for key in config_parameters_to_change.keys():
															config.pop(key, None)

					if has_file(args.repo_path, "config.json"):
										lowercase					:							str   =      UNetaDModel(**config)
					else:
										lowercase					:							Dict   =      UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
										lowercase					:							List[str]   =      class_name(**config)

					if do_only_config:
										model.save_config(os.path.join(args.repo_path, subfolder))

					lowercase					:							Any   =      dict(model.config)

					if do_only_renaming:
										for key, value in config_parameters_to_change.items():
															if key in config:
																				lowercase					:							Union[str, Any]   =      config[key]
																				del config[key]

										lowercase					:							str   =      [k.replace("UNetRes", "") for k in config["down_block_types"]]
										lowercase					:							List[str]   =      [k.replace("UNetRes", "") for k in config["up_block_types"]]

					if do_only_weights:
										lowercase					:							Optional[Any]   =      torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

										lowercase					:							Optional[Any]   =      {}
										for param_key, param_value in state_dict.items():
															if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
																				continue
															lowercase					:							Optional[int]   =      False
															for key, new_key in key_parameters_to_change.items():
																				if not has_changed and param_key.split(".")[0] == key:
																									lowercase					:							Union[str, Any]   =      param_value
																									lowercase					:							Tuple   =      True
															if not has_changed:
																				lowercase					:							Any   =      param_value

										model.load_state_dict(new_state_dict)
										model.save_pretrained(os.path.join(args.repo_path, subfolder))



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase					:							str   =      {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Any   =      [
					    "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "NezhaForNextSentencePrediction",
					    "NezhaForMaskedLM",
					    "NezhaForPreTraining",
					    "NezhaForMultipleChoice",
					    "NezhaForQuestionAnswering",
					    "NezhaForSequenceClassification",
					    "NezhaForTokenClassification",
					    "NezhaModel",
					    "NezhaPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nezha import (
										    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NezhaForMaskedLM,
										    NezhaForMultipleChoice,
										    NezhaForNextSentencePrediction,
										    NezhaForPreTraining,
										    NezhaForQuestionAnswering,
										    NezhaForSequenceClassification,
										    NezhaForTokenClassification,
										    NezhaModel,
										    NezhaPreTrainedModel,
										)


else:
					import sys

					lowercase					:							Union[str, Any]   =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

