





import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def    UpperCAmelCase				(  _lowerCamelCase   :	int  ):





						'''simple docstring'''


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

						return orig_key







def    UpperCAmelCase				(  _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	Dict  ):





						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE__  :					List[Any] =       orig_state_dict.pop(_UpperCamelCase  )

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

						SCREAMING_SNAKE_CASE__  :					str =       orig_state_dict['''cls.predictions.decoder.bias''']
						SCREAMING_SNAKE_CASE__  :					Any =       (
						    torch.arange(config.max_position_embeddings  ).expand((1, -1)  ) + 2
						)

						return orig_state_dict







def    UpperCAmelCase				(  _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	int      , _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       torch.load(_UpperCamelCase      , map_location="cpu"  )['''model_state_dict''']
						SCREAMING_SNAKE_CASE__  :					str =       NystromformerConfig.from_json_file(_UpperCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       NystromformerForMaskedLM(_UpperCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       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__":
				__lowercase							:Optional[int]							=	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."
				)
				__lowercase							:Tuple							=	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_sentencepiece_available, is_tokenizers_available


UpperCAmelCase__			:   Optional[Any]      			=    {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   List[Any]      			=    ['MBart50Tokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   int      			=    ['MBart50TokenizerFast']


if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbartaa import MBartaaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
					import sys

					UpperCAmelCase__			:   str      			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
