



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			:     List[str]     = logging.getLogger(__name__)







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict     ):





   """simple docstring"""


   random.seed(lowerCAmelCase__     )
   np.random.seed(lowerCAmelCase__     )
   torch.manual_seed(lowerCAmelCase__     )
   torch.cuda.manual_seed_all(lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		Dict     ):





   """simple docstring"""


   model.eval()
   UpperCAmelCase_:		Optional[Any]       			=    next(model.parameters()     ).device
   UpperCAmelCase_:		Tuple       			=    1
   UpperCAmelCase_:		Any       			=    torch.zeros((eval_batch_size, context_len)		,	dtype=torch.long		,	device=lowerCAmelCase__     )
   UpperCAmelCase_:		Optional[Any]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )
   UpperCAmelCase_:		Optional[Any]       			=    torch.zeros(1		,	device=lowerCAmelCase__     )
   UpperCAmelCase_:		Optional[int]       			=    0
   for batch in eval_dataloader:
      batch.to(lowerCAmelCase__     )
      # pad
      context.zero_()
      for i in range(lowerCAmelCase__     ):
         UpperCAmelCase_:		Optional[int]       			=    batch[i]
      UpperCAmelCase_:		Union[str, Any]       			=    model(lowerCAmelCase__		,	labels=lowerCAmelCase__     )
      eval_loss += outputs[0].sum().item()
      nb_eval_examples += batch.size(0     )
   UpperCAmelCase_:		Tuple       			=    eval_loss / nb_eval_examples
   UpperCAmelCase_:		List[Any]       			=    torch.exp(lowerCAmelCase__     )
   model.train()
   return perplexity







def    lowerCAmelCase_  (lowerCAmelCase__:		List[Any]="gpt2"     ):





   """simple docstring"""


   UpperCAmelCase_:		List[str]       			=    GPTaLMHeadModel.from_pretrained(lowerCAmelCase__		,	output_hidden_states=lowerCAmelCase__     )
   torch.save(model.state_dict()		,	model_name + """local.pt"""     )
   return model







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		str     ):





   """simple docstring"""


   UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
   model.to(lowerCAmelCase__     )

   UpperCAmelCase_:		Dict       			=    ["""bias""", """LayerNorm.weight"""]
   UpperCAmelCase_:		Optional[int]       			=    [
       {
           """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},
   ]
   UpperCAmelCase_:		str       			=    AdamW(lowerCAmelCase__		,	lr=5e-5		,	eps=1e-8     )
   UpperCAmelCase_:		Tuple       			=    get_linear_schedule_with_warmup(lowerCAmelCase__		,	0		,	lowerCAmelCase__     )
   torch.cuda.empty_cache()
   return model, lm_optimizer, lm_scheduler







def    lowerCAmelCase_  (lowerCAmelCase__:		int		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		str     ):





   """simple docstring"""


   UpperCAmelCase_:		str       			=    np.array(lowerCAmelCase__     ).mean()
   UpperCAmelCase_:		Optional[Any]       			=    np.array(lowerCAmelCase__     ).std()
   UpperCAmelCase_:		Tuple       			=    (real_perps - avg) / std
   UpperCAmelCase_:		Tuple       			=    list(zip(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )     )
   joblib.dump(lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Optional[Any]="dev.jbl"		,	lowerCAmelCase__:		Tuple=recopy_gpta		,	):





   """simple docstring"""


   UpperCAmelCase_:		int       			=    []
   UpperCAmelCase_:		int       			=    []
   UpperCAmelCase_:		int       			=    []

   # Initialize the transformer model
   UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
   orig_model.to(device="""cpu"""     )
   torch.cuda.empty_cache()

   # Compute perplexity of initial transformer model for comparison
   model.train()
   UpperCAmelCase_:		List[str]       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

   for step in tqdm(range(lowerCAmelCase__     )     ):
      UpperCAmelCase_:		Union[str, Any]       			=    torch.zeros((1, context_len)		,	dtype=torch.long		,	device=lowerCAmelCase__     )
      UpperCAmelCase_:		str       			=    random.choice(lowerCAmelCase__     )
      UpperCAmelCase_:		int       			=    random.randint(0		,	len(story[0]     ) - context_len - 1     )
      UpperCAmelCase_:		Optional[int]       			=    story[0][start : start + context_len]
      lm_optimizer.zero_grad()
      UpperCAmelCase_:		Dict       			=    model(lowerCAmelCase__		,	labels=lowerCAmelCase__     )
      UpperCAmelCase_:		List[str]       			=    outputs[0]
      UpperCAmelCase_:		Optional[Any]       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )
      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
      UpperCAmelCase_:		int       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

      # Periodically save the stored (X, IG(X)) pairs
      if step % 1_0_0_0 == 0 and step > 1:
         intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

      # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
      UpperCAmelCase_:		str       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

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

   intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]		,	lowerCAmelCase__:		Any="data/tokenized_stories_train_wikitext103.jbl"		,	lowerCAmelCase__:		Any=1_0_0		,	lowerCAmelCase__:		Optional[int]=1_0_2_6		,	lowerCAmelCase__:		Optional[int]=True     ):





   """simple docstring"""


   UpperCAmelCase_:		Union[str, Any]       			=    joblib.load(lowerCAmelCase__     )
   print("""data loaded"""     )
   UpperCAmelCase_:		List[Any]       			=    []
   if trim:
      for i, example in enumerate(lowerCAmelCase__     ):
         if len(example[0]     ) > min_len:
            UpperCAmelCase_:		Optional[int]       			=    random.randint(0		,	len(example[0]     ) - context_len - 1     )
            objective_set.append(example[0, start : start + context_len]     )
         if len(lowerCAmelCase__     ) >= number:
            break
      UpperCAmelCase_:		Union[str, Any]       			=    []
      for j in range(i + 1		,	len(lowerCAmelCase__     )     ):
         if len(data[j][0]     ) > min_len:
            train_data.append(data[j]     )
   else:
      UpperCAmelCase_:		Optional[int]       			=    data[0:number]
      UpperCAmelCase_:		Tuple       			=    data[number:]

   joblib.dump(lowerCAmelCase__		,	"""objective_set.jbl"""     )
   print("""objective set saved"""     )
   return train_data, objective_set







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		int=5_0		,	lowerCAmelCase__:		List[Any]="secondary_learner.pt"     ):





   """simple docstring"""


   UpperCAmelCase_:		Tuple       			=    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
   UpperCAmelCase_:		int       			=    train_dataset[:5_1_2]
   UpperCAmelCase_:		Any       			=    train_dataset[5_1_2:]
   UpperCAmelCase_:		Union[str, Any]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )
   UpperCAmelCase_:		Optional[int]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )

   # secondary learner model set up
   UpperCAmelCase_:		Optional[int]       			=    nn.MSELoss()
   UpperCAmelCase_:		str       			=    nn.MSELoss(reduction="""sum"""     )
   secondary_learner.to(lowerCAmelCase__     )
   UpperCAmelCase_:		Optional[int]       			=    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
   UpperCAmelCase_:		Any       			=    float("""inf"""     )
   # Iterate through batches until we've used max_steps batches
   for epoch in range(int(lowerCAmelCase__     )     ):
      UpperCAmelCase_:		Optional[Any]       			=    0.0
      secondary_learner.train()
      for step, batch in enumerate(lowerCAmelCase__     ):
         UpperCAmelCase_:		Union[str, Any]       			=    batch[0].to(lowerCAmelCase__     )
         UpperCAmelCase_:		List[Any]       			=    batch[1].to(lowerCAmelCase__     )
         UpperCAmelCase_:		Tuple       			=    secondary_learner(lowerCAmelCase__     )
         q_optimizer.zero_grad()
         UpperCAmelCase_:		Any       			=    loss(lowerCAmelCase__		,	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(lowerCAmelCase__     )):
            UpperCAmelCase_:		int       			=    tr_q_loss / (step + 1)

            secondary_learner.eval()
            UpperCAmelCase_:		Optional[Any]       			=    0.0
            UpperCAmelCase_:		Union[str, Any]       			=    0.0
            UpperCAmelCase_:		Any       			=    []
            UpperCAmelCase_:		Any       			=    []
            # Compute performance of the secondary learner after this batch
            for stepa, batcha in enumerate(lowerCAmelCase__     ):
               UpperCAmelCase_:		Optional[Any]       			=    batcha[0].to(lowerCAmelCase__     )
               UpperCAmelCase_:		int       			=    batcha[1].to(lowerCAmelCase__     )
               UpperCAmelCase_:		Dict       			=    secondary_learner(lowerCAmelCase__     )
               q_lossa += test_loss(lowerCAmelCase__		,	lowerCAmelCase__     ).item()
               sum_qa += torch.sum(lowerCAmelCase__     ).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(lowerCAmelCase__     )
            print(
                """Epoch: """		,	lowerCAmelCase__		,	"""step: """		,	lowerCAmelCase__		,	"""Avg. q:"""		,	sum_qa / len(lowerCAmelCase__     )		,	"""Train Loss: """		,	lowerCAmelCase__		,	"""Test Loss: """		,	lowerCAmelCase__		,	)
            if q_lossa < best_test_loss:
               joblib.dump((predicted, actual)		,	"""pred_vs_actual.jbl"""     )
               torch.save(secondary_learner.state_dict()		,	lowerCAmelCase__     )
               UpperCAmelCase_:		str       			=    q_lossa

         secondary_learner.train()
   return secondary_learner







class 							_a							(      nn.Module				):

   def __init__(self,      SCREAMING_SNAKE_CASE_				)					->      str:
      super(SCREAMING_SNAKE_CASE_,      self				).__init__()
      UpperCAmelCase_:		List[str]       			=    model.transformer.wte
      UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(model.transformer.wte.weight				)
      UpperCAmelCase_:		List[str]       			=    nn.Convad(self.embeddings.weight.size(1				),      256,      3,      padding=1				)
      UpperCAmelCase_:		Union[str, Any]       			=    nn.Sequential(nn.Linear(256,      32				),      nn.Dropout(p=0.1				),      nn.Linear(32,      32				),      nn.Linear(32,      1				)				)

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Any:
      UpperCAmelCase_:		Dict       			=    torch.max(self.conv(self.embeddings(SCREAMING_SNAKE_CASE_				).squeeze(1				).transpose(1,      2				)				),      2				)[0]
      UpperCAmelCase_:		Dict       			=    self.fc(SCREAMING_SNAKE_CASE_				)
      return qs.squeeze(1				)

   @classmethod
   def     __snake_case					(cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Union[str, Any]:
      UpperCAmelCase_:		Optional[int]       			=    cls(SCREAMING_SNAKE_CASE_				)  # this calls __init__
      UpperCAmelCase_:		Any       			=    torch.load(SCREAMING_SNAKE_CASE_				)
      secondary_learner.load_state_dict(SCREAMING_SNAKE_CASE_				)
      UpperCAmelCase_:		str       			=    model.transformer.wte
      UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(model.transformer.wte.weight				)
      return secondary_learner











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a			:     Union[str, Any]     = {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Optional[int]     = [
	    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlaubertForMultipleChoice',
	    'FlaubertForQuestionAnswering',
	    'FlaubertForQuestionAnsweringSimple',
	    'FlaubertForSequenceClassification',
	    'FlaubertForTokenClassification',
	    'FlaubertModel',
	    'FlaubertWithLMHeadModel',
	    'FlaubertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Dict     = [
	    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFFlaubertForMultipleChoice',
	    'TFFlaubertForQuestionAnsweringSimple',
	    'TFFlaubertForSequenceClassification',
	    'TFFlaubertForTokenClassification',
	    'TFFlaubertModel',
	    'TFFlaubertPreTrainedModel',
	    'TFFlaubertWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
	from .tokenization_flaubert import FlaubertTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flaubert import (
		    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertPreTrainedModel,
		    FlaubertWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_flaubert import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertPreTrainedModel,
		    TFFlaubertWithLMHeadModel,
		)

else:
	import sys

	a			:     Tuple     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






