



"""simple docstring"""







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



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

		return orig_key



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    Optional[Any]				=			orig_state_dict.pop(__lowerCamelCase	)

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

		lowercase__   :    Union[str, Any]				=			orig_state_dict['''cls.predictions.decoder.bias''']
		lowercase__   :    List[str]				=			torch.arange(__lowerCamelCase	).expand((1, -1)	) + 2

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Tuple				=			torch.load(__lowerCamelCase						,     map_location='''cpu'''	)['''model_state_dict''']
		lowercase__   :    List[Any]				=			YosoConfig.from_json_file(__lowerCamelCase	)
		lowercase__   :    List[Any]				=			YosoForMaskedLM(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			convert_checkpoint_helper(config.max_position_embeddings						,     __lowerCamelCase	)

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

		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)





"""simple docstring"""







import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase_   						=   4
lowerCAmelCase_   						=   3
class        __A       (	A_    ):







		'''simple docstring'''





		pass





def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for shard in shards:
				for i in range(__lowerCamelCase	):
						yield {"i": i, "shard": shard}



def      __UpperCAmelCase      (    )							->      Tuple:
		lowercase__   :    int				=			int(os.environ['''RANK''']	)
		lowercase__   :    str				=			int(os.environ['''WORLD_SIZE''']	)

		lowercase__   :    List[Any]				=			ArgumentParser()
		parser.add_argument('''--streaming'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--local_rank'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--num_workers'''						,     type=__lowerCamelCase						,     default=0	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Optional[Any]				=			args.streaming
		lowercase__   :    List[Any]				=			args.num_workers

		lowercase__   :    Optional[Any]				=			{'''shards''': [f"""shard_{shard_idx}""" for shard_idx in range(__lowerCamelCase	)]}
		lowercase__   :    Dict				=			IterableDataset.from_generator(__lowerCamelCase						,     gen_kwargs=__lowerCamelCase	)
		if not streaming:
				lowercase__   :    int				=			Dataset.from_list(list(__lowerCamelCase	)	)

		lowercase__   :    int				=			split_dataset_by_node(__lowerCamelCase						,     rank=__lowerCamelCase						,     world_size=__lowerCamelCase	)
		lowercase__   :    Optional[Any]				=			torch.utils.data.DataLoader(__lowerCamelCase						,     num_workers=__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			NUM_SHARDS * NUM_ITEMS_PER_SHARD
		lowercase__   :    str				=			full_size // world_size
		expected_local_size += int(rank < (full_size % world_size)	)

		lowercase__   :    str				=			sum(1 for _ in dataloader	)
		if local_size != expected_local_size:
				raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()
