





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Tuple )      ->       Dict:




		'''simple docstring'''
		if "model" in orig_key:
				lowercase__				:			Dict            =		orig_key.replace('model.'      ,						'' )
		if "norm1" in orig_key:
				lowercase__				:			Union[str, Any]            =		orig_key.replace('norm1'      ,						'attention.output.LayerNorm' )
		if "norm2" in orig_key:
				lowercase__				:			Dict            =		orig_key.replace('norm2'      ,						'output.LayerNorm' )
		if "norm" in orig_key:
				lowercase__				:			Optional[Any]            =		orig_key.replace('norm'      ,						'LayerNorm' )
		if "transformer" in orig_key:
				lowercase__				:			Union[str, 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__				:			Union[str, Any]            =		orig_key.replace('mha.attn'      ,						'attention.self' )
		if "mha" in orig_key:
				lowercase__				:			Union[str, Any]            =		orig_key.replace('mha'      ,						'attention' )
		if "W_q" in orig_key:
				lowercase__				:			Optional[int]            =		orig_key.replace('W_q'      ,						'self.query' )
		if "W_k" in orig_key:
				lowercase__				:			Optional[int]            =		orig_key.replace('W_k'      ,						'self.key' )
		if "W_v" in orig_key:
				lowercase__				:			Union[str, Any]            =		orig_key.replace('W_v'      ,						'self.value' )
		if "ff.0" in orig_key:
				lowercase__				:			Optional[Any]            =		orig_key.replace('ff.0'      ,						'intermediate.dense' )
		if "ff.2" in orig_key:
				lowercase__				:			Union[str, Any]            =		orig_key.replace('ff.2'      ,						'output.dense' )
		if "ff" in orig_key:
				lowercase__				:			Optional[Any]            =		orig_key.replace('ff'      ,						'output.dense' )
		if "mlm_class" in orig_key:
				lowercase__				:			List[Any]            =		orig_key.replace('mlm.mlm_class'      ,						'cls.predictions.decoder' )
		if "mlm" in orig_key:
				lowercase__				:			Optional[int]            =		orig_key.replace('mlm'      ,						'cls.predictions.transform' )
		if "backbone.backbone.encoders" in orig_key:
				lowercase__				:			List[str]            =		orig_key.replace('backbone.backbone.encoders'      ,						'encoder.layer' )
		if "cls" not in orig_key:
				lowercase__				:			Optional[int]            =		'mra.' + orig_key

		return orig_key


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   List[str]      ,						_lowercase    :   Any )      ->       Optional[int]:




		'''simple docstring'''
		for key in orig_state_dict.copy().keys():
				lowercase__				:			List[Any]            =		orig_state_dict.pop(_lowercase )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowercase__				:			Optional[int]            =		val

		lowercase__				:			int            =		orig_state_dict['cls.predictions.decoder.bias']
		lowercase__				:			Any            =		torch.arange(_lowercase ).expand((1, -1) ) + 2

		return orig_state_dict


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[Any]      ,						_lowercase    :   Tuple      ,						_lowercase    :   Tuple )      ->       int:




		'''simple docstring'''
		lowercase__				:			Union[str, Any]            =		torch.load(_lowercase      ,						map_location='cpu' )['model_state_dict']
		lowercase__				:			str            =		MraConfig.from_json_file(_lowercase )
		lowercase__				:			Union[str, Any]            =		MraForMaskedLM(_lowercase )

		lowercase__				:			Dict            =		convert_checkpoint_helper(config.max_position_embeddings      ,						_lowercase )

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

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


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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase:  Dict   =   {
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[Any]   =   ["""DebertaV2TokenizerFast"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  str   =   [
			    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFDebertaV2ForMaskedLM""",
			    """TFDebertaV2ForQuestionAnswering""",
			    """TFDebertaV2ForSequenceClassification""",
			    """TFDebertaV2ForTokenClassification""",
			    """TFDebertaV2Model""",
			    """TFDebertaV2PreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   [
			    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DebertaV2ForMaskedLM""",
			    """DebertaV2ForMultipleChoice""",
			    """DebertaV2ForQuestionAnswering""",
			    """DebertaV2ForSequenceClassification""",
			    """DebertaV2ForTokenClassification""",
			    """DebertaV2Model""",
			    """DebertaV2PreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deberta_va import (
			    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DebertaVaConfig,
			    DebertaVaOnnxConfig,
			)
			from .tokenization_deberta_va import DebertaVaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_deberta_va import (
						    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						    TFDebertaVaPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deberta_va import (
						    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DebertaVaForMaskedLM,
						    DebertaVaForMultipleChoice,
						    DebertaVaForQuestionAnswering,
						    DebertaVaForSequenceClassification,
						    DebertaVaForTokenClassification,
						    DebertaVaModel,
						    DebertaVaPreTrainedModel,
						)

else:
			import sys

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


