
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


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

			return orig_key






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			for key in orig_state_dict.copy().keys():
						lowerCAmelCase_						=	orig_state_dict.pop(__lowerCAmelCase				)

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

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

			return orig_state_dict






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


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

			lowerCAmelCase_						=	convert_checkpoint_helper(config.max_position_embeddings    ,							__lowerCAmelCase				)

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

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


if __name__ == "__main__":
			_A   =     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."
			)
			_A   =     parser.parse_args()
			convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_A   =     logging.get_logger(__name__)


_A   =     TypeVar("DatasetType", Dataset, IterableDataset)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[List[float]] = None    ,							__lowerCAmelCase  :  Optional[int] = None    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,							):




			"""simple docstring"""


			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""				)
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)
			else:
						return _interleave_iterable_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  int = 0    ,							):




			"""simple docstring"""


			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)
			else:
						return _concatenate_iterable_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)



