




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__A    :				Optional[int]    						=	logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

__A    :				Any    						=	{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__A    :				int    						=	{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__A    :				Any    						=	sorted(arg_to_scheduler.keys())
__A    :				Tuple    						=	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class       UpperCAmelCase_   ( pl.LightningModule							):




							'''simple docstring'''



							def __init__(       self							:						Union[str, Any]							,  a							:						argparse.Namespace							,  a							:						int=None							,  a							:						List[str]="base"							,  a							:						List[str]=None							,  a							:						Any=None							,  a							:						Optional[int]=None							,  **a							:						Any							,  )      ->       str:
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(a      )
										SCREAMING_SNAKE_CASE     =       0
										SCREAMING_SNAKE_CASE     =       Path(self.hparams.output_dir      )
										SCREAMING_SNAKE_CASE     =       self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
													SCREAMING_SNAKE_CASE     =       AutoConfig.from_pretrained(
													    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,  **({"""num_labels""": num_labels} if num_labels is not None else {})							,  cache_dir=a							,  **a							,  )
										else:
													SCREAMING_SNAKE_CASE     =       config

										SCREAMING_SNAKE_CASE     =       ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
										for p in extra_model_params:
													if getattr(self.hparams							,  a							,  a      ):
																assert hasattr(self.config							,  a      ), f"""model config doesn't have a `{p}` attribute"""
																setattr(self.config							,  a							,  getattr(self.hparams							,  a      )      )

										if tokenizer is None:
													SCREAMING_SNAKE_CASE     =       AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,  cache_dir=a							,  )
										else:
													SCREAMING_SNAKE_CASE     =       tokenizer
										SCREAMING_SNAKE_CASE     =       MODEL_MODES[mode]
										if model is None:
													SCREAMING_SNAKE_CASE     =       self.model_type.from_pretrained(
													    self.hparams.model_name_or_path							,  from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path      )							,  config=self.config							,  cache_dir=a							,  )
										else:
													SCREAMING_SNAKE_CASE     =       model





							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  *a							:						str							,  **a							:						List[Any]      )      ->       str:
										SCREAMING_SNAKE_CASE     =       self.model_type.from_pretrained(*a							,  **a      )





							def  _UpperCAmelCase  (       self							:						int      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       arg_to_scheduler[self.hparams.lr_scheduler]
										SCREAMING_SNAKE_CASE     =       get_schedule_func(
										    self.opt							,  num_warmup_steps=self.hparams.warmup_steps							,  num_training_steps=self.total_steps()      )
										SCREAMING_SNAKE_CASE     =       {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
										return scheduler





							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       self.model
										SCREAMING_SNAKE_CASE     =       ["""bias""", """LayerNorm.weight"""]
										SCREAMING_SNAKE_CASE     =       [
										    {
										        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )],
										        """weight_decay""": self.hparams.weight_decay,
										    },
										    {
										        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )],
										        """weight_decay""": 0.0,
										    },
										]
										if self.hparams.adafactor:
													SCREAMING_SNAKE_CASE     =       Adafactor(
													    a							,  lr=self.hparams.learning_rate							,  scale_parameter=a							,  relative_step=a      )

										else:
													SCREAMING_SNAKE_CASE     =       AdamW(
													    a							,  lr=self.hparams.learning_rate							,  eps=self.hparams.adam_epsilon      )
										SCREAMING_SNAKE_CASE     =       optimizer

										SCREAMING_SNAKE_CASE     =       self.get_lr_scheduler()

										return [optimizer], [scheduler]





							def  _UpperCAmelCase  (       self							:						Optional[Any]							,  a							:						Union[str, Any]							,  a							:						Optional[Any]      )      ->       int:
										return self.validation_step(a							,  a      )





							def  _UpperCAmelCase  (       self							:						List[Any]							,  a							:						List[str]      )      ->       Tuple:
										return self.validation_end(a      )





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       int:
										SCREAMING_SNAKE_CASE     =       max(1							,  self.hparams.gpus      )  # TODO: consider num_tpu_cores
										SCREAMING_SNAKE_CASE     =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





							def  _UpperCAmelCase  (       self							:						int							,  a							:						Optional[Any]      )      ->       Any:
										if mode == "test":
													SCREAMING_SNAKE_CASE     =       len(self.test_dataloader().dataset      )
										else:
													SCREAMING_SNAKE_CASE     =       self.get_dataloader("""train"""							,  self.hparams.train_batch_size							,  shuffle=a      )
													SCREAMING_SNAKE_CASE     =       len(self.train_dataloader().dataset      )





							def  _UpperCAmelCase  (       self							:						List[str]							,  a							:						str							,  a							:						int							,  a							:						bool = False      )      ->       Any:
										raise NotImplementedError("""You must implement this for your task"""      )





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       str:
										return self.train_loader





							def  _UpperCAmelCase  (       self							:						str      )      ->       int:
										return self.get_dataloader("""dev"""							,  self.hparams.eval_batch_size							,  shuffle=a      )





							def  _UpperCAmelCase  (       self							:						int      )      ->       Union[str, Any]:
										return self.get_dataloader("""test"""							,  self.hparams.eval_batch_size							,  shuffle=a      )





							def  _UpperCAmelCase  (       self							:						Any							,  a							:						Tuple      )      ->       Dict:
										return os.path.join(
										    self.hparams.data_dir							,  """cached_{}_{}_{}""".format(
										        a							,  list(filter(a							,  self.hparams.model_name_or_path.split("""/"""      )      )      ).pop()							,  str(self.hparams.max_seq_length      )							,  )							,  )





							@pl.utilities.rank_zero_only
							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						Dict[str, Any]      )      ->       None:
										SCREAMING_SNAKE_CASE     =       self.output_dir.joinpath("""best_tfmr"""      )
										SCREAMING_SNAKE_CASE     =       self.step_count
										self.model.save_pretrained(a      )
										self.tokenizer.save_pretrained(a      )







							@staticmethod
							def  _UpperCAmelCase  (       a							:						Dict							,  a							:						str      )      ->       List[str]:
										parser.add_argument(
										    """--model_name_or_path"""							,  default=a							,  type=a							,  required=a							,  help="""Path to pretrained model or model identifier from huggingface.co/models"""							,  )
										parser.add_argument(
										    """--config_name"""							,  default=""""""							,  type=a							,  help="""Pretrained config name or path if not the same as model_name"""      )
										parser.add_argument(
										    """--tokenizer_name"""							,  default=a							,  type=a							,  help="""Pretrained tokenizer name or path if not the same as model_name"""							,  )
										parser.add_argument(
										    """--cache_dir"""							,  default=""""""							,  type=a							,  help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""							,  )
										parser.add_argument(
										    """--encoder_layerdrop"""							,  type=a							,  help="""Encoder layer dropout probability (Optional). Goes into model.config"""							,  )
										parser.add_argument(
										    """--decoder_layerdrop"""							,  type=a							,  help="""Decoder layer dropout probability (Optional). Goes into model.config"""							,  )
										parser.add_argument(
										    """--dropout"""							,  type=a							,  help="""Dropout probability (Optional). Goes into model.config"""							,  )
										parser.add_argument(
										    """--attention_dropout"""							,  type=a							,  help="""Attention dropout probability (Optional). Goes into model.config"""							,  )
										parser.add_argument("""--learning_rate"""							,  default=5E-5							,  type=a							,  help="""The initial learning rate for Adam."""      )
										parser.add_argument(
										    """--lr_scheduler"""							,  default="""linear"""							,  choices=a							,  metavar=a							,  type=a							,  help="""Learning rate scheduler"""							,  )
										parser.add_argument("""--weight_decay"""							,  default=0.0							,  type=a							,  help="""Weight decay if we apply some."""      )
										parser.add_argument("""--adam_epsilon"""							,  default=1E-8							,  type=a							,  help="""Epsilon for Adam optimizer."""      )
										parser.add_argument("""--warmup_steps"""							,  default=0							,  type=a							,  help="""Linear warmup over warmup_steps."""      )
										parser.add_argument("""--num_workers"""							,  default=4							,  type=a							,  help="""kwarg passed to DataLoader"""      )
										parser.add_argument("""--num_train_epochs"""							,  dest="""max_epochs"""							,  default=3							,  type=a      )
										parser.add_argument("""--train_batch_size"""							,  default=32							,  type=a      )
										parser.add_argument("""--eval_batch_size"""							,  default=32							,  type=a      )
										parser.add_argument("""--adafactor"""							,  action="""store_true"""      )





class       UpperCAmelCase_   ( pl.Callback							):




							'''simple docstring'''



							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						Dict							,  a							:						List[str]      )      ->       Optional[Any]:
										SCREAMING_SNAKE_CASE     =       trainer.lr_schedulers[0]["""scheduler"""]
										SCREAMING_SNAKE_CASE     =       {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
										pl_module.logger.log_metrics(a      )





							def  _UpperCAmelCase  (       self							:						List[str]							,  a							:						pl.Trainer							,  a							:						pl.LightningModule      )      ->       List[Any]:
										rank_zero_info("""***** Validation results *****"""      )
										SCREAMING_SNAKE_CASE     =       trainer.callback_metrics
										# Log results
										for key in sorted(a      ):
													if key not in ["log", "progress_bar"]:
																rank_zero_info("""{} = {}\n""".format(a							,  str(metrics[key]      )      )      )







							def  _UpperCAmelCase  (       self							:						Optional[int]							,  a							:						pl.Trainer							,  a							:						pl.LightningModule      )      ->       int:
										rank_zero_info("""***** Test results *****"""      )
										SCREAMING_SNAKE_CASE     =       trainer.callback_metrics
										# Log and save results to file
										SCREAMING_SNAKE_CASE     =       os.path.join(pl_module.hparams.output_dir							,  """test_results.txt"""      )
										with open(a							,  """w"""      ) as writer:
													for key in sorted(a      ):
																if key not in ["log", "progress_bar"]:
																			rank_zero_info("""{} = {}\n""".format(a							,  str(metrics[key]      )      )      )
																			writer.write("""{} = {}\n""".format(a							,  str(metrics[key]      )      )      )





def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			#  To allow all pl args uncomment the following line
			#  parser = pl.Trainer.add_argparse_args(parser)
			parser.add_argument(
			    """--output_dir"""       ,						default=SCREAMING_SNAKE_CASE       ,						type=SCREAMING_SNAKE_CASE       ,						required=SCREAMING_SNAKE_CASE       ,						help="""The output directory where the model predictions and checkpoints will be written."""       ,						)
			parser.add_argument(
			    """--fp16"""       ,						action="""store_true"""       ,						help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""       ,						)

			parser.add_argument(
			    """--fp16_opt_level"""       ,						type=SCREAMING_SNAKE_CASE       ,						default="""O2"""       ,						help=(
			        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
			        """See details at https://nvidia.github.io/apex/amp.html"""
			    )       ,						)
			parser.add_argument("""--n_tpu_cores"""       ,						dest="""tpu_cores"""       ,						type=SCREAMING_SNAKE_CASE )
			parser.add_argument("""--max_grad_norm"""       ,						dest="""gradient_clip_val"""       ,						default=1.0       ,						type=SCREAMING_SNAKE_CASE       ,						help="""Max gradient norm""" )
			parser.add_argument("""--do_train"""       ,						action="""store_true"""       ,						help="""Whether to run training.""" )
			parser.add_argument("""--do_predict"""       ,						action="""store_true"""       ,						help="""Whether to run predictions on the test set.""" )
			parser.add_argument(
			    """--gradient_accumulation_steps"""       ,						dest="""accumulate_grad_batches"""       ,						type=SCREAMING_SNAKE_CASE       ,						default=1       ,						help="""Number of updates steps to accumulate before performing a backward/update pass."""       ,						)
			parser.add_argument("""--seed"""       ,						type=SCREAMING_SNAKE_CASE       ,						default=42       ,						help="""random seed for initialization""" )
			parser.add_argument(
			    """--data_dir"""       ,						default=SCREAMING_SNAKE_CASE       ,						type=SCREAMING_SNAKE_CASE       ,						required=SCREAMING_SNAKE_CASE       ,						help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""       ,						)




def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE=None       ,						SCREAMING_SNAKE_CASE=True       ,						SCREAMING_SNAKE_CASE=[]       ,						SCREAMING_SNAKE_CASE=None       ,						SCREAMING_SNAKE_CASE=None       ,						**SCREAMING_SNAKE_CASE       ,						):
			'''simple docstring'''
			pl.seed_everything(args.seed )

			# init model
			SCREAMING_SNAKE_CASE     =       Path(model.hparams.output_dir )
			odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE )

			# add custom checkpoints
			if checkpoint_callback is None:
						SCREAMING_SNAKE_CASE     =       pl.callbacks.ModelCheckpoint(
						    filepath=args.output_dir       ,						prefix="""checkpoint"""       ,						monitor="""val_loss"""       ,						mode="""min"""       ,						save_top_k=1 )
			if early_stopping_callback:
						extra_callbacks.append(SCREAMING_SNAKE_CASE )
			if logging_callback is None:
						SCREAMING_SNAKE_CASE     =       LoggingCallback()

			SCREAMING_SNAKE_CASE     =       {}

			# TODO: remove with PyTorch 1.6 since pl uses native amp
			if args.fpaa:
						SCREAMING_SNAKE_CASE     =       16
						SCREAMING_SNAKE_CASE     =       args.fpaa_opt_level

			if args.gpus > 1:
						SCREAMING_SNAKE_CASE     =       """ddp"""

			SCREAMING_SNAKE_CASE     =       args.accumulate_grad_batches
			SCREAMING_SNAKE_CASE     =       extra_train_kwargs.get("""accelerator"""       ,						SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       extra_train_kwargs.get("""profiler"""       ,						SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       pl.Trainer.from_argparse_args(
			    SCREAMING_SNAKE_CASE       ,						weights_summary=SCREAMING_SNAKE_CASE       ,						callbacks=[logging_callback] + extra_callbacks       ,						logger=SCREAMING_SNAKE_CASE       ,						checkpoint_callback=SCREAMING_SNAKE_CASE       ,						**SCREAMING_SNAKE_CASE       ,						)

			if args.do_train:
						trainer.fit(SCREAMING_SNAKE_CASE )

			return trainer












import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			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     =       """nystromformer.""" + orig_key

			return orig_key
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE     =       orig_state_dict.pop(SCREAMING_SNAKE_CASE )

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

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

			return orig_state_dict
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       torch.load(SCREAMING_SNAKE_CASE       ,						map_location="""cpu""" )["""model_state_dict"""]
			SCREAMING_SNAKE_CASE     =       NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       NystromformerForMaskedLM(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       convert_checkpoint_helper(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

			model.load_state_dict(SCREAMING_SNAKE_CASE )
			model.eval()
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
		__A    :				List[str]    						=	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."""
		)
		__A    :				Union[str, Any]    						=	parser.parse_args()
		convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






