





'''simple docstring'''

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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

				return orig_key







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				for key in orig_state_dict.copy().keys():
								lowercase         = orig_state_dict.pop(__snake_case							)

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

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

				return orig_state_dict







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = torch.load(__snake_case		,							map_location="cpu"							)["model_state_dict"]
				lowercase         = YosoConfig.from_json_file(__snake_case							)
				lowercase         = YosoForMaskedLM(__snake_case							)

				lowercase         = convert_checkpoint_helper(config.max_position_embeddings		,							__snake_case							)

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

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


if __name__ == "__main__":
			__lowerCamelCase : Union[str, Any]											=			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 : Dict											=			parser.parse_args()
			convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate

@dataclass(frozen=_lowercase  )
class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	str 			=		field(default='''image-classification'''			,       metadata={'''include_in_asdict_even_if_is_default''': True}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''image''': Image()}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''labels''': ClassLabel}  )
		UpperCAmelCase					:	str 			=		"image"
		UpperCAmelCase					:	str 			=		"labels"




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Union[str, Any]				)					-> Union[str, Any]:
						if self.label_column not in features:
										raise ValueError(f'Column {self.label_column} is not present in features.'				)
						if not isinstance(features[self.label_column]							,			A__				):
										raise ValueError(f'Column {self.label_column} is not a ClassLabel.'				)
						lowercase         = copy.deepcopy(self				)
						lowercase         = self.label_schema.copy()
						lowercase         = features[self.label_column]
						lowercase         = label_schema
						return task_template





		@property
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict[str, str]:
						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}




