



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

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

						return orig_key

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Union[str, Any]:
						for key in orig_state_dict.copy().keys():
												UpperCAmelCase         =     orig_state_dict.pop(lowerCamelCase_    )

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

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

						return orig_state_dict

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     torch.load(lowerCamelCase_    ,   map_location="cpu"    )["model_state_dict"]
						UpperCAmelCase         =     YosoConfig.from_json_file(lowerCamelCase_    )
						UpperCAmelCase         =     YosoForMaskedLM(lowerCamelCase_    )

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








from typing import TYPE_CHECKING

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


__lowerCamelCase     :							Optional[int]  							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							List[Any]  							=				[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__lowerCamelCase     :							Optional[int]  							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



