


'''simple docstring'''



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







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

			return orig_key







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase__					:   Union[str, Any]       				=       orig_state_dict.pop(UpperCamelCase__   )

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

			UpperCAmelCase__					:   str       				=       orig_state_dict["""cls.predictions.decoder.bias"""]
			UpperCAmelCase__					:   Union[str, Any]       				=       torch.arange(UpperCamelCase__   ).expand((1, -1)   ) + 2

			return orig_state_dict







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Tuple       				=       torch.load(UpperCamelCase__	, map_location="""cpu"""   )["""model_state_dict"""]
			UpperCAmelCase__					:   Dict       				=       YosoConfig.from_json_file(UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       YosoForMaskedLM(UpperCamelCase__   )

			UpperCAmelCase__					:   str       				=       convert_checkpoint_helper(config.max_position_embeddings	, UpperCamelCase__   )

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

			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)



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A		={
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'M2M100ForConditionalGeneration',
		    'M2M100Model',
		    'M2M100PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)