"""simple docstring"""







import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def      a__					(						UpperCAmelCase__				:	Tuple	)->		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__    		=						"""nystromformer.""" + orig_key

						return orig_key


def      a__					(						UpperCAmelCase__				:	Optional[Any]						,	UpperCAmelCase__				:	Optional[Any]	)->		List[str]:
						for key in orig_state_dict.copy().keys():
												UpperCAmelCase__    		=						orig_state_dict.pop(UpperCamelCase__	)

												if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
																		continue
												else:
																		UpperCAmelCase__    		=						val

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

						return orig_state_dict


def      a__					(						UpperCAmelCase__				:	Any						,	UpperCAmelCase__				:	Tuple						,	UpperCAmelCase__				:	Optional[Any]	)->		List[str]:
						UpperCAmelCase__    		=						torch.load(UpperCamelCase__						,	map_location="""cpu"""	)["""model_state_dict"""]
						UpperCAmelCase__    		=						NystromformerConfig.from_json_file(UpperCamelCase__	)
						UpperCAmelCase__    		=						NystromformerForMaskedLM(UpperCamelCase__	)

						UpperCAmelCase__    		=						convert_checkpoint_helper(UpperCamelCase__						,	UpperCamelCase__	)

						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__":
						UpperCamelCase_				:				Tuple							      =						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Nystromformer pytorch checkpoint."""
						)
						parser.add_argument(
						    """--config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help="""The json file for Nystromformer model config.""",
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						UpperCamelCase_				:				Dict							      =						parser.parse_args()
						convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








from typing import TYPE_CHECKING

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


_lowerCamelCase       =	{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase       =	[
	    'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTSanJapaneseForConditionalGeneration',
	    'GPTSanJapaneseModel',
	    'GPTSanJapanesePreTrainedModel',
	]
	_lowerCamelCase       =	[
	    'GPTSanJapaneseTokenizer',
	]


if TYPE_CHECKING:
	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptsan_japanese import (
		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTSanJapaneseForConditionalGeneration,
		    GPTSanJapaneseModel,
		    GPTSanJapanesePreTrainedModel,
		)
		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
	import sys

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