code: 
  
  
  """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
  
  
  UpperCAmelCase   =	logging.getLogger(__name__)
  
  def 							_snake_case			(      __snake_case       :			List[Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     random.seed(__snake_case      )
     np.random.seed(__snake_case      )
     torch.manual_seed(__snake_case      )
     torch.cuda.manual_seed_all(__snake_case      )
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Tuple	,   __snake_case       :			Any      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     model.eval()
     _lowerCamelCase  :				Optional[int]      =		next(model.parameters()      ).device
     _lowerCamelCase  :				Optional[Any]      =		1
     _lowerCamelCase  :				int      =		torch.zeros((eval_batch_size, context_len)	,   dtype=torch.long	,   device=__snake_case      )
     _lowerCamelCase  :				Union[str, Any]      =		DataLoader(__snake_case	,   shuffle=__snake_case	,   batch_size=__snake_case      )
     _lowerCamelCase  :				Any      =		torch.zeros(1	,   device=__snake_case      )
     _lowerCamelCase  :				Optional[Any]      =		0
     for batch in eval_dataloader:
        batch.to(__snake_case      )
        # pad
        context.zero_()
        for i in range(__snake_case      ):
           _lowerCamelCase  :				Tuple      =		batch[i]
        _lowerCamelCase  :				Tuple      =		model(__snake_case	,   labels=__snake_case      )
        eval_loss += outputs[0].sum().item()
        nb_eval_examples += batch.size(0      )
     _lowerCamelCase  :				List[str]      =		eval_loss / nb_eval_examples
     _lowerCamelCase  :				str      =		torch.exp(__snake_case      )
     model.train()
     return perplexity
  
  def 							_snake_case			(      __snake_case       :			int="gpt2"      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				str      =		GPTaLMHeadModel.from_pretrained(__snake_case	,   output_hidden_states=__snake_case      )
     torch.save(model.state_dict()	,   model_name + """local.pt"""      )
     return model
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			str	,   __snake_case       :			List[Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Optional[Any]      =		copy.deepcopy(__snake_case      )
     model.to(__snake_case      )
  
     _lowerCamelCase  :				int      =		["""bias""", """LayerNorm.weight"""]
     _lowerCamelCase  :				Optional[Any]      =		[
         {
             """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},
     ]
     _lowerCamelCase  :				Union[str, Any]      =		AdamW(__snake_case	,   lr=5E-5	,   eps=1E-8      )
     _lowerCamelCase  :				str      =		get_linear_schedule_with_warmup(__snake_case	,   0	,   __snake_case      )
     torch.cuda.empty_cache()
     return model, lm_optimizer, lm_scheduler
  
  def 							_snake_case			(      __snake_case       :			Dict	,   __snake_case       :			Optional[int]	,   __snake_case       :			Dict	,   __snake_case       :			List[str]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				List[Any]      =		np.array(__snake_case      ).mean()
     _lowerCamelCase  :				List[str]      =		np.array(__snake_case      ).std()
     _lowerCamelCase  :				Optional[int]      =		(real_perps - avg) / std
     _lowerCamelCase  :				Any      =		list(zip(__snake_case	,   __snake_case	,   __snake_case      )      )
     joblib.dump(__snake_case	,   __snake_case      )
  
  def 							_snake_case			(      __snake_case       :			str	,   __snake_case       :			Optional[int]	,   __snake_case       :			Any	,   __snake_case       :			str	,   __snake_case       :			Union[str, Any]	,   __snake_case       :			int	,   __snake_case       :			Optional[int]	,   __snake_case       :			Optional[Any]="dev.jbl"	,   __snake_case       :			List[Any]=recopy_gpta	,   ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Any      =		[]
     _lowerCamelCase  :				List[Any]      =		[]
     _lowerCamelCase  :				Optional[Any]      =		[]
  
     # Initialize the transformer model
     _lowerCamelCase  :				Optional[Any]      =		copy.deepcopy(__snake_case      )
     orig_model.to(device="""cpu"""      )
     torch.cuda.empty_cache()
  
     # Compute perplexity of initial transformer model for comparison
     model.train()
     _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		recopy_model(__snake_case	,   __snake_case	,   __snake_case      )
  
     for step in tqdm(range(__snake_case      )      ):
        _lowerCamelCase  :				Any      =		torch.zeros((1, context_len)	,   dtype=torch.long	,   device=__snake_case      )
        _lowerCamelCase  :				Any      =		random.choice(__snake_case      )
        _lowerCamelCase  :				Any      =		random.randint(0	,   len(story[0]      ) - context_len - 1      )
        _lowerCamelCase  :				Union[str, Any]      =		story[0][start : start + context_len]
        lm_optimizer.zero_grad()
        _lowerCamelCase  :				List[str]      =		model(__snake_case	,   labels=__snake_case      )
        _lowerCamelCase  :				Tuple      =		outputs[0]
        _lowerCamelCase  :				Dict      =		compute_perplexity(__snake_case	,   __snake_case	,   __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
        _lowerCamelCase  :				Optional[Any]      =		compute_perplexity(__snake_case	,   __snake_case	,   __snake_case      )
  
        # Periodically save the stored (X, IG(X)) pairs
        if step % 1000 == 0 and step > 1:
           intermittent_save(__snake_case	,   __snake_case	,   __snake_case	,   __snake_case      )
  
        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				List[Any]      =		recopy_model(__snake_case	,   __snake_case	,   __snake_case      )
  
        past_perps.append(past_perp.item()      )
        real_perps.append(orig_perp - real_perp.item()      )
        contexts.append(np.array(context.cpu()      )      )
  
     intermittent_save(__snake_case	,   __snake_case	,   __snake_case	,   __snake_case      )
  
  def 							_snake_case			(      __snake_case       :			str	,   __snake_case       :			Optional[int]="data/tokenized_stories_train_wikitext103.jbl"	,   __snake_case       :			Tuple=100	,   __snake_case       :			Any=1026	,   __snake_case       :			int=True      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Tuple      =		joblib.load(__snake_case      )
     print("""data loaded"""      )
     _lowerCamelCase  :				Optional[Any]      =		[]
     if trim:
        for i, example in enumerate(__snake_case      ):
           if len(example[0]      ) > min_len:
              _lowerCamelCase  :				str      =		random.randint(0	,   len(example[0]      ) - context_len - 1      )
              objective_set.append(example[0, start : start + context_len]      )
           if len(__snake_case      ) >= number:
              break
        _lowerCamelCase  :				List[Any]      =		[]
        for j in range(i + 1	,   len(__snake_case      )      ):
           if len(data[j][0]      ) > min_len:
              train_data.append(data[j]      )
     else:
        _lowerCamelCase  :				List[str]      =		data[0:number]
        _lowerCamelCase  :				Tuple      =		data[number:]
  
     joblib.dump(__snake_case	,   """objective_set.jbl"""      )
     print("""objective set saved"""      )
     return train_data, objective_set
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			List[Any]	,   __snake_case       :			str	,   __snake_case       :			Union[str, Any]	,   __snake_case       :			Dict=50	,   __snake_case       :			Union[str, Any]="secondary_learner.pt"      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				List[str]      =		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
     _lowerCamelCase  :				Optional[Any]      =		train_dataset[:512]
     _lowerCamelCase  :				Union[str, Any]      =		train_dataset[512:]
     _lowerCamelCase  :				str      =		DataLoader(__snake_case	,   shuffle=__snake_case	,   batch_size=__snake_case      )
     _lowerCamelCase  :				List[Any]      =		DataLoader(__snake_case	,   shuffle=__snake_case	,   batch_size=__snake_case      )
  
     # secondary learner model set up
     _lowerCamelCase  :				List[Any]      =		nn.MSELoss()
     _lowerCamelCase  :				Optional[int]      =		nn.MSELoss(reduction="""sum"""      )
     secondary_learner.to(__snake_case      )
     _lowerCamelCase  :				List[Any]      =		torch.optim.Adam(secondary_learner.parameters()	,   lr=0.00001      )
     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
     _lowerCamelCase  :				Dict      =		float("""inf"""      )
     # Iterate through batches until we've used max_steps batches
     for epoch in range(int(__snake_case      )      ):
        _lowerCamelCase  :				str      =		0.0
        secondary_learner.train()
        for step, batch in enumerate(__snake_case      ):
           _lowerCamelCase  :				Dict      =		batch[0].to(__snake_case      )
           _lowerCamelCase  :				Union[str, Any]      =		batch[1].to(__snake_case      )
           _lowerCamelCase  :				Optional[int]      =		secondary_learner(__snake_case      )
           q_optimizer.zero_grad()
           _lowerCamelCase  :				Optional[int]      =		loss(__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(__snake_case      )):
              _lowerCamelCase  :				Optional[Any]      =		tr_q_loss / (step + 1)
  
              secondary_learner.eval()
              _lowerCamelCase  :				Optional[Any]      =		0.0
              _lowerCamelCase  :				str      =		0.0
              _lowerCamelCase  :				Any      =		[]
              _lowerCamelCase  :				str      =		[]
              # Compute performance of the secondary learner after this batch
              for stepa, batcha in enumerate(__snake_case      ):
                 _lowerCamelCase  :				Tuple      =		batcha[0].to(__snake_case      )
                 _lowerCamelCase  :				str      =		batcha[1].to(__snake_case      )
                 _lowerCamelCase  :				Tuple      =		secondary_learner(__snake_case      )
                 q_lossa += test_loss(__snake_case	,   __snake_case      ).item()
                 sum_qa += torch.sum(__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(__snake_case      )
              print(
                  """Epoch: """	,   __snake_case	,   """step: """	,   __snake_case	,   """Avg. q:"""	,   sum_qa / len(__snake_case      )	,   """Train Loss: """	,   __snake_case	,   """Test Loss: """	,   __snake_case	,   )
              if q_lossa < best_test_loss:
                 joblib.dump((predicted, actual)	,   """pred_vs_actual.jbl"""      )
                 torch.save(secondary_learner.state_dict()	,   __snake_case      )
                 _lowerCamelCase  :				str      =		q_lossa
  
           secondary_learner.train()
     return secondary_learner
  
  
  
  
  
  class   lowercase__							(				nn.Module							):
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        super(SCREAMING_SNAKE_CASE			,      self).__init__()
        _lowerCamelCase  :				List[str]      =		model.transformer.wte
        _lowerCamelCase  :				Optional[int]      =		copy.deepcopy(model.transformer.wte.weight)
        _lowerCamelCase  :				Dict      =		nn.Convad(self.embeddings.weight.size(1)			,      256			,      3			,      padding=1)
        _lowerCamelCase  :				int      =		nn.Sequential(nn.Linear(256			,      32)			,      nn.Dropout(p=0.1)			,      nn.Linear(32			,      32)			,      nn.Linear(32			,      1))
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[Any]:
        _lowerCamelCase  :				Optional[int]      =		torch.max(self.conv(self.embeddings(SCREAMING_SNAKE_CASE).squeeze(1).transpose(1			,      2))			,      2)[0]
        _lowerCamelCase  :				List[str]      =		self.fc(SCREAMING_SNAKE_CASE)
        return qs.squeeze(1)
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        _lowerCamelCase  :				str      =		cls(SCREAMING_SNAKE_CASE)  # this calls __init__
        _lowerCamelCase  :				Optional[Any]      =		torch.load(SCREAMING_SNAKE_CASE)
        secondary_learner.load_state_dict(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Optional[Any]      =		model.transformer.wte
        _lowerCamelCase  :				int      =		copy.deepcopy(model.transformer.wte.weight)
        return secondary_learner
  
  
style_context: 
  
  
  """simple docstring"""
  import subprocess
  from argparse import ArgumentParser
  from typing import List, Union
  
  from huggingface_hub.hf_api import HfFolder, create_repo, whoami
  from requests.exceptions import HTTPError
  
  from . import BaseTransformersCLICommand
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     @staticmethod
     def 		UpperCamelCase_     (       SCREAMING_SNAKE_CASE)     ->     Dict:
        _lowerCamelCase  :				Optional[Any]      =		parser.add_parser("""login"""			,      help="""Log in using the same credentials as on huggingface.co""")
        login_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: LoginCommand(SCREAMING_SNAKE_CASE))
        _lowerCamelCase  :				Any      =		parser.add_parser("""whoami"""			,      help="""Find out which huggingface.co account you are logged in as.""")
        whoami_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: WhoamiCommand(SCREAMING_SNAKE_CASE))
        _lowerCamelCase  :				Optional[int]      =		parser.add_parser("""logout"""			,      help="""Log out""")
        logout_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: LogoutCommand(SCREAMING_SNAKE_CASE))
  
        # new system: git-based repo system
        _lowerCamelCase  :				Optional[int]      =		parser.add_parser(
            """repo"""			,      help="""Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."""			,      )
        _lowerCamelCase  :				Optional[Any]      =		repo_parser.add_subparsers(
            help="""Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands""")
        _lowerCamelCase  :				Dict      =		repo_subparsers.add_parser(
            """create"""			,      help="""Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co""")
        repo_create_parser.add_argument(
            """name"""			,      type=SCREAMING_SNAKE_CASE			,      help="""Name for your model's repo. Will be namespaced under your username to build the model id."""			,      )
        repo_create_parser.add_argument("""--organization"""			,      type=SCREAMING_SNAKE_CASE			,      help="""Optional: organization namespace.""")
        repo_create_parser.add_argument("""-y"""			,      """--yes"""			,      action="""store_true"""			,      help="""Optional: answer Yes to the prompt""")
        repo_create_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: RepoCreateCommand(SCREAMING_SNAKE_CASE))
  
  
  
  
  
  
  
  class   lowercase__							:
     __UpperCAmelCase       					=				'''\u001b[1m'''
     __UpperCAmelCase       					=				'''\u001b[31m'''
     __UpperCAmelCase       					=				'''\u001b[90m'''
     __UpperCAmelCase       					=				'''\u001b[0m'''
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE)     ->     Dict:
        return F'{cls._bold}{s}{cls._reset}'
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        return F'{cls._bold}{cls._red}{s}{cls._reset}'
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE)     ->     Tuple:
        return F'{cls._gray}{s}{cls._reset}'
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			List[List[Union[str, int]]]	,   __snake_case       :			List[str]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				str      =		[max(len(str(__snake_case      )      ) for x in col      ) for col in zip(*__snake_case	,   __snake_case      )]
     _lowerCamelCase  :				Any      =		("""{{:{}}} """ * len(__snake_case      )).format(*__snake_case      )
     _lowerCamelCase  :				Optional[Any]      =		[]
     lines.append(row_format.format(*__snake_case      )      )
     lines.append(row_format.format(*["""-""" * w for w in col_widths]      )      )
     for row in rows:
        lines.append(row_format.format(*__snake_case      )      )
     return "\n".join(__snake_case      )
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE)     ->     List[str]:
        _lowerCamelCase  :				Any      =		args
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        print(
            ANSI.red(
                """ERROR! `huggingface-cli login` uses an outdated login mechanism """
                """that is not compatible with the Hugging Face Hub backend anymore. """
                """Please use `huggingface-cli login instead."""))
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        print(
            ANSI.red(
                """WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use """
                """`huggingface-cli whoami` instead."""))
        _lowerCamelCase  :				Tuple      =		HfFolder.get_token()
        if token is None:
           print("""Not logged in""")
           exit()
        try:
           _lowerCamelCase      ,						_lowerCamelCase  :				int      =		whoami(SCREAMING_SNAKE_CASE)
           print(SCREAMING_SNAKE_CASE)
           if orgs:
              print(ANSI.bold("""orgs: """)			,      """,""".join(SCREAMING_SNAKE_CASE))
        except HTTPError as e:
           print(SCREAMING_SNAKE_CASE)
           print(ANSI.red(e.response.text))
           exit(1)
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        print(
            ANSI.red(
                """ERROR! `transformers-cli logout` uses an outdated logout mechanism """
                """that is not compatible with the Hugging Face Hub backend anymore. """
                """Please use `huggingface-cli logout instead."""))
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        print(
            ANSI.red(
                """WARNING! Managing repositories through transformers-cli is deprecated. """
                """Please use `huggingface-cli` instead."""))
        _lowerCamelCase  :				Union[str, Any]      =		HfFolder.get_token()
        if token is None:
           print("""Not logged in""")
           exit(1)
        try:
           _lowerCamelCase  :				Union[str, Any]      =		subprocess.check_output(["""git""", """--version"""]).decode("""utf-8""")
           print(ANSI.gray(stdout.strip()))
        except FileNotFoundError:
           print("""Looks like you do not have git installed, please install.""")
  
        try:
           _lowerCamelCase  :				List[str]      =		subprocess.check_output(["""git-lfs""", """--version"""]).decode("""utf-8""")
           print(ANSI.gray(stdout.strip()))
        except FileNotFoundError:
           print(
               ANSI.red(
                   """Looks like you do not have git-lfs installed, please install."""
                   """ You can install from https://git-lfs.github.com/."""
                   """ Then run `git lfs install` (you only have to do this once)."""))
        print("""""")
  
        _lowerCamelCase      ,						_lowerCamelCase  :				str      =		whoami(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				str      =		self.args.organization if self.args.organization is not None else user
        _lowerCamelCase  :				Union[str, Any]      =		F'{namespace}/{self.args.name}'
        print(F'You are about to create {ANSI.bold(SCREAMING_SNAKE_CASE)}')
  
        if not self.args.yes:
           _lowerCamelCase  :				Tuple      =		input("""Proceed? [Y/n] """).lower()
           if not (choice == "" or choice == "y" or choice == "yes"):
              print("""Abort""")
              exit()
        try:
           _lowerCamelCase  :				int      =		create_repo(SCREAMING_SNAKE_CASE			,      name=self.args.name			,      organization=self.args.organization)
        except HTTPError as e:
           print(SCREAMING_SNAKE_CASE)
           print(ANSI.red(e.response.text))
           exit(1)
        print("""\nYour repo now lives at:""")
        print(F'  {ANSI.bold(SCREAMING_SNAKE_CASE)}')
        print("""\nYou can clone it locally with the command below, and commit/push as usual.""")
        print(F'\n  git clone {url}')
        print("""""")
  
  