


'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM




def 	_A  (       A							)  ->				Optional[Any]:
	if "model" in orig_key:
		lowercase						:		str   					=		orig_key.replace("model."							,""							)
	if "norm1" in orig_key:
		lowercase						:		Any   					=		orig_key.replace("norm1"							,"attention.output.LayerNorm"							)
	if "norm2" in orig_key:
		lowercase						:		List[str]   					=		orig_key.replace("norm2"							,"output.LayerNorm"							)
	if "norm" in orig_key:
		lowercase						:		Dict   					=		orig_key.replace("norm"							,"LayerNorm"							)
	if "transformer" in orig_key:
		lowercase						:		List[Any]   					=		orig_key.split("."							)[0].split("_"							)[-1]
		lowercase						:		Optional[Any]   					=		orig_key.replace(F'''transformer_{layer_num}'''							,F'''encoder.layer.{layer_num}'''							)
	if "mha.attn" in orig_key:
		lowercase						:		Tuple   					=		orig_key.replace("mha.attn"							,"attention.self"							)
	if "mha" in orig_key:
		lowercase						:		List[Any]   					=		orig_key.replace("mha"							,"attention"							)
	if "W_q" in orig_key:
		lowercase						:		Tuple   					=		orig_key.replace("W_q"							,"self.query"							)
	if "W_k" in orig_key:
		lowercase						:		Any   					=		orig_key.replace("W_k"							,"self.key"							)
	if "W_v" in orig_key:
		lowercase						:		Dict   					=		orig_key.replace("W_v"							,"self.value"							)
	if "ff1" in orig_key:
		lowercase						:		Dict   					=		orig_key.replace("ff1"							,"intermediate.dense"							)
	if "ff2" in orig_key:
		lowercase						:		Optional[int]   					=		orig_key.replace("ff2"							,"output.dense"							)
	if "ff" in orig_key:
		lowercase						:		Union[str, Any]   					=		orig_key.replace("ff"							,"output.dense"							)
	if "mlm_class" in orig_key:
		lowercase						:		List[str]   					=		orig_key.replace("mlm.mlm_class"							,"cls.predictions.decoder"							)
	if "mlm" in orig_key:
		lowercase						:		int   					=		orig_key.replace("mlm"							,"cls.predictions.transform"							)
	if "cls" not in orig_key:
		lowercase						:		List[str]   					=		"yoso." + orig_key

	return orig_key




def 	_A  (       A							,A							)  ->				int:
	for key in orig_state_dict.copy().keys():
		lowercase						:		str   					=		orig_state_dict.pop(A							)

		if ("pooler" in key) or ("sen_class" in key):
			continue
		else:
			lowercase						:		Dict   					=		val

	lowercase						:		Dict   					=		orig_state_dict["cls.predictions.decoder.bias"]
	lowercase						:		Tuple   					=		torch.arange(A							).expand((1, -1)							) + 2

	return orig_state_dict




def 	_A  (       A							,A							,A							)  ->				Dict:
	lowercase						:		List[str]   					=		torch.load(A							,map_location="cpu"							)["model_state_dict"]
	lowercase						:		Union[str, Any]   					=		YosoConfig.from_json_file(A							)
	lowercase						:		Any   					=		YosoForMaskedLM(A							)

	lowercase						:		Tuple   					=		convert_checkpoint_helper(config.max_position_embeddings							,A							)

	print(model.load_state_dict(A							)							)
	model.eval()
	model.save_pretrained(A							)

	print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''							)


if __name__ == "__main__":
					lowerCAmelCase   : Optional[int]             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The json file for YOSO model config.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					lowerCAmelCase   : List[str]             =				parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .multicontrolnet import MultiControlNetModel
					from .pipeline_controlnet import StableDiffusionControlNetPipeline
					from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
					from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
					from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


