



"""simple docstring"""







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowerCAmelCase_   						=   logging.get_logger(__name__)



def      __UpperCAmelCase      (    __lowerCamelCase=None						,     __lowerCamelCase=None	)							->      Union[str, Any]:
		return field(default_factory=lambda: default						,     metadata=__lowerCamelCase	)





@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					List[str]   	=				list_field(
		    default=[]     ,metadata={
		        "help": (
		            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
		            " of all available models"
		        )
		    }     ,)

		lowerCAmelCase			:					List[int]   	=				list_field(
		    default=[8]     ,metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}    )

		lowerCAmelCase			:					List[int]   	=				list_field(
		    default=[8, 3_2, 1_2_8, 5_1_2]     ,metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}     ,)

		lowerCAmelCase			:					bool   	=				field(
		    default=A_     ,metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}     ,)
		lowerCAmelCase			:					bool   	=				field(
		    default=A_     ,metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}     ,)
		lowerCAmelCase			:					bool   	=				field(
		    default=A_     ,metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}    )
		lowerCAmelCase			:					bool   	=				field(default=A_     ,metadata={"help": "Use FP16 to accelerate inference."}    )
		lowerCAmelCase			:					bool   	=				field(default=A_     ,metadata={"help": "Benchmark training of model"}    )
		lowerCAmelCase			:					bool   	=				field(default=A_     ,metadata={"help": "Verbose memory tracing"}    )
		lowerCAmelCase			:					bool   	=				field(
		    default=A_     ,metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}     ,)
		lowerCAmelCase			:					bool   	=				field(
		    default=A_     ,metadata={
		        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
		    }     ,)
		lowerCAmelCase			:					bool   	=				field(default=A_     ,metadata={"help": "Trace memory line by line"}    )
		lowerCAmelCase			:					bool   	=				field(default=A_     ,metadata={"help": "Save result to a CSV file"}    )
		lowerCAmelCase			:					bool   	=				field(default=A_     ,metadata={"help": "Save all print statements in a log file"}    )
		lowerCAmelCase			:					bool   	=				field(default=A_     ,metadata={"help": "Whether to print environment information"}    )
		lowerCAmelCase			:					bool   	=				field(
		    default=A_     ,metadata={
		        "help": (
		            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
		            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
		            " for debugging / testing and on TPU."
		        )
		    }     ,)
		lowerCAmelCase			:					str   	=				field(
		    default=F"inference_time_{round(time()    )}.csv"     ,metadata={"help": "CSV filename used if saving time results to csv."}     ,)
		lowerCAmelCase			:					str   	=				field(
		    default=F"inference_memory_{round(time()    )}.csv"     ,metadata={"help": "CSV filename used if saving memory results to csv."}     ,)
		lowerCAmelCase			:					str   	=				field(
		    default=F"train_time_{round(time()    )}.csv"     ,metadata={"help": "CSV filename used if saving time results to csv for training."}     ,)
		lowerCAmelCase			:					str   	=				field(
		    default=F"train_memory_{round(time()    )}.csv"     ,metadata={"help": "CSV filename used if saving memory results to csv for training."}     ,)
		lowerCAmelCase			:					str   	=				field(
		    default=F"env_info_{round(time()    )}.csv"     ,metadata={"help": "CSV filename used if saving environment information."}     ,)
		lowerCAmelCase			:					str   	=				field(
		    default=F"log_{round(time()    )}.csv"     ,metadata={"help": "Log filename used if print statements are saved in log."}     ,)
		lowerCAmelCase			:					int   	=				field(default=3     ,metadata={"help": "Times an experiment will be run."}    )
		lowerCAmelCase			:					bool   	=				field(
		    default=A_     ,metadata={
		        "help": (
		            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
		            " model weights."
		        )
		    }     ,)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		int:






				"""simple docstring"""



				warnings.warn(
				    f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
				    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
				    ''' to benchmark Transformer models.'''			,_snake_case			,)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		str:






				"""simple docstring"""



				return json.dumps(dataclasses.asdict(self						)			,indent=2						)







		@property
		def 					UpperCAmelCase    (	self						:  int						)					->		List[str]:






				"""simple docstring"""



				if len(self.models						) <= 0:
						raise ValueError(
						    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
						    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''						)
				return self.models







		@property
		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				if not self.multi_process:
						return False
				elif self.is_tpu:
						logger.info('''Multiprocessing is currently not possible on TPU.'''						)
						return False
				else:
						return True





"""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)
