"""simple docstring"""






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM




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

				return orig_key




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE__		  =				orig_state_dict.pop(__UpperCAmelCase       )

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

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

				return orig_state_dict




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase  ,					map_location="cpu"       )["model_state_dict"]
				SCREAMING_SNAKE_CASE__		  =				YosoConfig.from_json_file(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				YosoForMaskedLM(__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				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 Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				List[str]   , _snake_case			:				Callable   , _snake_case			:				Optional[Features] = None   , _snake_case			:				str = None   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				Optional[dict] = None   , _snake_case			:				Optional[int] = None   , **_snake_case			:				Dict   , )							->       Any:
									super().__init__(
									    features=_snake_case   , cache_dir=_snake_case   , keep_in_memory=_snake_case   , streaming=_snake_case   , num_proc=_snake_case   , **_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				Generator(
									    cache_dir=_snake_case   , features=_snake_case   , generator=_snake_case   , gen_kwargs=_snake_case   , **_snake_case   , )



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[Any]:
									# Build iterable dataset
									if self.streaming:
													SCREAMING_SNAKE_CASE__		  =				self.builder.as_streaming_dataset(split="train"		)
									# Build regular (map-style) dataset
									else:
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None

													self.builder.download_and_prepare(
													    download_config=_snake_case   , download_mode=_snake_case   , verification_mode=_snake_case   , base_path=_snake_case   , num_proc=self.num_proc   , )
													SCREAMING_SNAKE_CASE__		  =				self.builder.as_dataset(
													    split="train"   , verification_mode=_snake_case   , in_memory=self.keep_in_memory		)
									return dataset




