






'''simple docstring'''





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       NestedDataStructureLike[PathLike]						,_SCREAMING_SNAKE_CASE		:       Optional[NamedSplit] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Features] = None						,_SCREAMING_SNAKE_CASE		:       str = None						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,**_SCREAMING_SNAKE_CASE		:       Dict						,)    ->   Optional[Any]:


						'''simple docstring'''
						super().__init__(
						    _SCREAMING_SNAKE_CASE						,split=_SCREAMING_SNAKE_CASE						,features=_SCREAMING_SNAKE_CASE						,cache_dir=_SCREAMING_SNAKE_CASE						,keep_in_memory=_SCREAMING_SNAKE_CASE						,streaming=_SCREAMING_SNAKE_CASE						,num_proc=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)
						A	 =      path_or_paths if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) else {self.split: path_or_paths}
						A	 =      Text(
						    cache_dir=_SCREAMING_SNAKE_CASE						,data_files=_SCREAMING_SNAKE_CASE						,features=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)







		def       A(  self		:       int		)    ->   Any:


						'''simple docstring'''
						# Build iterable dataset
						if self.streaming:
										A	 =      self.builder.as_streaming_dataset(split=self.split		)
						# Build regular (map-style) dataset
						else:
										A	 =      None
										A	 =      None
										A	 =      None
										A	 =      None

										self.builder.download_and_prepare(
										    download_config=_SCREAMING_SNAKE_CASE						,download_mode=_SCREAMING_SNAKE_CASE						,verification_mode=_SCREAMING_SNAKE_CASE						,base_path=_SCREAMING_SNAKE_CASE						,num_proc=self.num_proc						,)
										A	 =      self.builder.as_dataset(
										    split=self.split						,verification_mode=_SCREAMING_SNAKE_CASE						,in_memory=self.keep_in_memory		)
						return dataset













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







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

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      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__":
					lowerCAmelCase_           = 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.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





