
'''simple docstring'''





import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


A							     = logging.getLogger(__name__)
def 	snake_case_							(				_a  :				Tuple      ):
      random.seed(SCREAMING_SNAKE_CASE__      )
      np.random.seed(SCREAMING_SNAKE_CASE__      )
      torch.manual_seed(SCREAMING_SNAKE_CASE__      )
      torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE__      )
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Optional[int]			,       _a  :				Union[str, Any]      ):
      model.eval()
      snake_case_    							=				next(model.parameters()      ).device
      snake_case_    							=				1
      snake_case_    							=				torch.zeros((eval_batch_size, context_len)			,       dtype=torch.long			,       device=SCREAMING_SNAKE_CASE__      )
      snake_case_    							=				DataLoader(SCREAMING_SNAKE_CASE__			,       shuffle=SCREAMING_SNAKE_CASE__			,       batch_size=SCREAMING_SNAKE_CASE__      )
      snake_case_    							=				torch.zeros(1			,       device=SCREAMING_SNAKE_CASE__      )
      snake_case_    							=				0
      for batch in eval_dataloader:
            batch.to(SCREAMING_SNAKE_CASE__      )
            # pad
            context.zero_()
            for i in range(SCREAMING_SNAKE_CASE__      ):
                  snake_case_    							=				batch[i]
            snake_case_    							=				model(SCREAMING_SNAKE_CASE__			,       labels=SCREAMING_SNAKE_CASE__      )
            eval_loss += outputs[0].sum().item()
            nb_eval_examples += batch.size(0      )
      snake_case_    							=				eval_loss / nb_eval_examples
      snake_case_    							=				torch.exp(SCREAMING_SNAKE_CASE__      )
      model.train()
      return perplexity
def 	snake_case_							(				_a  :				Tuple="gpt2"      ):
      snake_case_    							=				GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE__			,       output_hidden_states=SCREAMING_SNAKE_CASE__      )
      torch.save(model.state_dict()			,       model_name + "local.pt"      )
      return model
def 	snake_case_							(				_a  :				Tuple			,       _a  :				List[str]			,       _a  :				List[str]      ):
      snake_case_    							=				copy.deepcopy(SCREAMING_SNAKE_CASE__      )
      model.to(SCREAMING_SNAKE_CASE__      )

      snake_case_    							=				["""bias""", """LayerNorm.weight"""]
      snake_case_    							=				[
          {
              """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )],
              """weight_decay""": 0.0,
          },
          {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )], """weight_decay""": 0.0},
      ]
      snake_case_    							=				AdamW(SCREAMING_SNAKE_CASE__			,       lr=5E-5			,       eps=1E-8      )
      snake_case_    							=				get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE__			,       0			,       SCREAMING_SNAKE_CASE__      )
      torch.cuda.empty_cache()
      return model, lm_optimizer, lm_scheduler
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				List[Any]			,       _a  :				int			,       _a  :				List[str]      ):
      snake_case_    							=				np.array(SCREAMING_SNAKE_CASE__      ).mean()
      snake_case_    							=				np.array(SCREAMING_SNAKE_CASE__      ).std()
      snake_case_    							=				(real_perps - avg) / std
      snake_case_    							=				list(zip(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__      )      )
      joblib.dump(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__      )
def 	snake_case_							(				_a  :				str			,       _a  :				int			,       _a  :				Dict			,       _a  :				Dict			,       _a  :				Optional[int]			,       _a  :				Union[str, Any]			,       _a  :				Dict			,       _a  :				Any="dev.jbl"			,       _a  :				List[str]=recopy_gpta			,       ):
      snake_case_    							=				[]
      snake_case_    							=				[]
      snake_case_    							=				[]

      # Initialize the transformer model
      snake_case_    							=				copy.deepcopy(SCREAMING_SNAKE_CASE__      )
      orig_model.to(device="cpu"      )
      torch.cuda.empty_cache()

      # Compute perplexity of initial transformer model for comparison
      model.train()
      snake_case_    							=				recopy_model(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__      )

      for step in tqdm(range(SCREAMING_SNAKE_CASE__      )      ):
            snake_case_    							=				torch.zeros((1, context_len)			,       dtype=torch.long			,       device=SCREAMING_SNAKE_CASE__      )
            snake_case_    							=				random.choice(SCREAMING_SNAKE_CASE__      )
            snake_case_    							=				random.randint(0			,       len(story[0]      ) - context_len - 1      )
            snake_case_    							=				story[0][start : start + context_len]
            lm_optimizer.zero_grad()
            snake_case_    							=				model(SCREAMING_SNAKE_CASE__			,       labels=SCREAMING_SNAKE_CASE__      )
            snake_case_    							=				outputs[0]
            snake_case_    							=				compute_perplexity(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__      )
            model.train()
            lm_loss.backward()
            # Do LM backprop
            torch.nn.utils.clip_grad_norm_(model.parameters()			,       3.0      )
            lm_optimizer.step()
            lm_scheduler.step()  # Update learning rate schedule

            # Compute perplexity after back-propagating on the selected context
            snake_case_    							=				compute_perplexity(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__      )

            # Periodically save the stored (X, IG(X)) pairs
            if step % 1_000 == 0 and step > 1:
                  intermittent_save(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__      )

            # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
            snake_case_    							=				recopy_model(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__      )

            past_perps.append(past_perp.item()      )
            real_perps.append(orig_perp - real_perp.item()      )
            contexts.append(np.array(context.cpu()      )      )

      intermittent_save(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__      )
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Dict="data/tokenized_stories_train_wikitext103.jbl"			,       _a  :				Optional[Any]=100			,       _a  :				Tuple=1_026			,       _a  :				Optional[int]=True      ):
      snake_case_    							=				joblib.load(SCREAMING_SNAKE_CASE__      )
      print("data loaded"      )
      snake_case_    							=				[]
      if trim:
            for i, example in enumerate(SCREAMING_SNAKE_CASE__      ):
                  if len(example[0]      ) > min_len:
                        snake_case_    							=				random.randint(0			,       len(example[0]      ) - context_len - 1      )
                        objective_set.append(example[0, start : start + context_len]      )
                  if len(SCREAMING_SNAKE_CASE__      ) >= number:
                        break
            snake_case_    							=				[]
            for j in range(i + 1			,       len(SCREAMING_SNAKE_CASE__      )      ):
                  if len(data[j][0]      ) > min_len:
                        train_data.append(data[j]      )
      else:
            snake_case_    							=				data[0:number]
            snake_case_    							=				data[number:]

      joblib.dump(SCREAMING_SNAKE_CASE__			,       "objective_set.jbl"      )
      print("objective set saved"      )
      return train_data, objective_set
def 	snake_case_							(				_a  :				Tuple			,       _a  :				List[Any]			,       _a  :				List[str]			,       _a  :				Dict			,       _a  :				Any=50			,       _a  :				Optional[Any]="secondary_learner.pt"      ):
      snake_case_    							=				torch.device("cuda:0" if torch.cuda.is_available() else "cpu"      )
      # We will use the first 512 pairs from our dataset as a test set for
      # our secondary learner and the rest to train
      snake_case_    							=				train_dataset[:512]
      snake_case_    							=				train_dataset[512:]
      snake_case_    							=				DataLoader(SCREAMING_SNAKE_CASE__			,       shuffle=SCREAMING_SNAKE_CASE__			,       batch_size=SCREAMING_SNAKE_CASE__      )
      snake_case_    							=				DataLoader(SCREAMING_SNAKE_CASE__			,       shuffle=SCREAMING_SNAKE_CASE__			,       batch_size=SCREAMING_SNAKE_CASE__      )

      # secondary learner model set up
      snake_case_    							=				nn.MSELoss()
      snake_case_    							=				nn.MSELoss(reduction="sum"      )
      secondary_learner.to(SCREAMING_SNAKE_CASE__      )
      snake_case_    							=				torch.optim.Adam(secondary_learner.parameters()			,       lr=0.0_0_0_0_1      )
      secondary_learner.train()

      # TODO in original code this is written as number of actual batches seen
      # not number of items seen but other places it is number of items instead.
      # improve consistency! changed this to epochs for clarity
      snake_case_    							=				float("inf"      )
      # Iterate through batches until we've used max_steps batches
      for epoch in range(int(SCREAMING_SNAKE_CASE__      )      ):
            snake_case_    							=				0.0
            secondary_learner.train()
            for step, batch in enumerate(SCREAMING_SNAKE_CASE__      ):
                  snake_case_    							=				batch[0].to(SCREAMING_SNAKE_CASE__      )
                  snake_case_    							=				batch[1].to(SCREAMING_SNAKE_CASE__      )
                  snake_case_    							=				secondary_learner(SCREAMING_SNAKE_CASE__      )
                  q_optimizer.zero_grad()
                  snake_case_    							=				loss(SCREAMING_SNAKE_CASE__			,       real_q.float()      )
                  q_loss.backward()
                  q_optimizer.step()
                  tr_q_loss += q_loss.item()

                  # model trains fairly quickly so we won't wait for a full epoch
                  # eval is triggered at eval_freq and end of epochs
                  if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(SCREAMING_SNAKE_CASE__      )):
                        snake_case_    							=				tr_q_loss / (step + 1)

                        secondary_learner.eval()
                        snake_case_    							=				0.0
                        snake_case_    							=				0.0
                        snake_case_    							=				[]
                        snake_case_    							=				[]
                        # Compute performance of the secondary learner after this batch
                        for stepa, batcha in enumerate(SCREAMING_SNAKE_CASE__      ):
                              snake_case_    							=				batcha[0].to(SCREAMING_SNAKE_CASE__      )
                              snake_case_    							=				batcha[1].to(SCREAMING_SNAKE_CASE__      )
                              snake_case_    							=				secondary_learner(SCREAMING_SNAKE_CASE__      )
                              q_lossa += test_loss(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__      ).item()
                              sum_qa += torch.sum(SCREAMING_SNAKE_CASE__      ).item()
                              for ei, i in enumerate(predicted_qa.cpu().detach().numpy()      ):
                                    predicted.append(i.item()      )
                              for ei, i in enumerate(real_qa.cpu().detach().numpy()      ):
                                    actual.append(i.item()      )

                        q_lossa /= len(SCREAMING_SNAKE_CASE__      )
                        print(
                            "Epoch: "			,       SCREAMING_SNAKE_CASE__			,       "step: "			,       SCREAMING_SNAKE_CASE__			,       "Avg. q:"			,       sum_qa / len(SCREAMING_SNAKE_CASE__      )			,       "Train Loss: "			,       SCREAMING_SNAKE_CASE__			,       "Test Loss: "			,       SCREAMING_SNAKE_CASE__			,       )
                        if q_lossa < best_test_loss:
                              joblib.dump((predicted, actual)			,       "pred_vs_actual.jbl"      )
                              torch.save(secondary_learner.state_dict()			,       SCREAMING_SNAKE_CASE__      )
                              snake_case_    							=				q_lossa

                  secondary_learner.train()
      return secondary_learner




class 			lowerCAmelCase__ (							nn.Module							):



  '''simple docstring'''






  def __init__(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Union[str, Any]     ) ->							Dict:
        super(lowerCAmelCase_						,			self     ).__init__()
        snake_case_    							=				model.transformer.wte
        snake_case_    							=				copy.deepcopy(model.transformer.wte.weight     )
        snake_case_    							=				nn.Convad(self.embeddings.weight.size(1     )						,			2_56						,			3						,			padding=1     )
        snake_case_    							=				nn.Sequential(nn.Linear(2_56						,			32     )						,			nn.Dropout(p=0.1     )						,			nn.Linear(32						,			32     )						,			nn.Linear(32						,			1     )     )

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			List[str]     ) ->							List[str]:
        snake_case_    							=				torch.max(self.conv(self.embeddings(lowerCAmelCase_     ).squeeze(1     ).transpose(1						,			2     )     )						,			2     )[0]
        snake_case_    							=				self.fc(lowerCAmelCase_     )
        return qs.squeeze(1     )


  @classmethod
  def 		_lowerCamelCase						(cls	:			str						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			List[Any]     ) ->							List[str]:
        snake_case_    							=				cls(lowerCAmelCase_     )  # this calls __init__
        snake_case_    							=				torch.load(lowerCAmelCase_     )
        secondary_learner.load_state_dict(lowerCAmelCase_     )
        snake_case_    							=				model.transformer.wte
        snake_case_    							=				copy.deepcopy(model.transformer.wte.weight     )
        return secondary_learner








import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







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

  return orig_key







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  for key in orig_state_dict.copy().keys():
    __snake_case:		str					    =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__)

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

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

  return orig_state_dict







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  __snake_case:		int					    =		torch.load(SCREAMING_SNAKE_CASE__   ,   map_location="""cpu""")["""model_state_dict"""]
  __snake_case:		List[Any]					    =		NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[int]					    =		NystromformerForMaskedLM(SCREAMING_SNAKE_CASE__)

  __snake_case:		Dict					    =		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__":
      __UpperCAmelCase			:  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."
      )
      __UpperCAmelCase			:  Optional[Any]										=				parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






