


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''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__		:						str  ,							lowerCamelCase__		:						List[Any]      ):





			'''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__		:						Tuple  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						Optional[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__":
		UpperCAmelCase   :    List[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."
		)
		UpperCAmelCase   :    Tuple     =		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


UpperCAmelCase   :    Any     =		{
    "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:
		UpperCAmelCase   :    Union[str, 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

		UpperCAmelCase   :    Tuple     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

