

import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
      import torch_xla.core.xla_model as xm  # noqa: F401

      _SCREAMING_SNAKE_CASE          = True
except ImportError:
      _SCREAMING_SNAKE_CASE          = False


# Cache this result has it's a C FFI call which can be pretty time-consuming
_SCREAMING_SNAKE_CASE          = torch.distributed.is_available()





def    _snake_case		(snake_case_				:    Dict     )       ->		Optional[Any]:
  # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
  __UpperCAmelCase  :     str		=    importlib.util.find_spec(snake_case_     ) is not None
  if package_exists:
    try:
      __UpperCAmelCase  :     Any		=    importlib.metadata.metadata(snake_case_     )
      return True
    except importlib.metadata.PackageNotFoundError:
      return False





def    _snake_case		()       ->		bool:
  return _torch_distributed_available





def    _snake_case		()       ->		Tuple:
  try:
    pass
  except ImportError:
    print(
        'Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'
        ' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'
        ' Bindings for PyTorch*.'     )
  return (
      importlib.util.find_spec('torch_ccl'     ) is not None
      or importlib.util.find_spec('oneccl_bindings_for_pytorch'     ) is not None
  )





def    _snake_case		()       ->		Dict:
  return importlib.metadata.version('oneccl_bind_pt'     )





def    _snake_case		()       ->		str:
  return _is_package_available('transformer_engine'     )





@lru_cache
def    _snake_case		(snake_case_				:    str=True     )       ->		Dict:
  # Due to bugs on the amp series GPUs, we disable torch-xla on them
  if torch.cuda.is_available():
    return False
  if _tpu_available and check_device:
    try:
      # Will raise a RuntimeError if no XLA configuration is found
      __UpperCAmelCase  :     Tuple		=    xm.xla_device()
      return True
    except RuntimeError:
      return False
  return _tpu_available





def    _snake_case		()       ->		Optional[int]:
  return _is_package_available('deepspeed'     )





def    _snake_case		(snake_case_				:    Tuple=False     )       ->		int:
  if is_tpu_available():
    return not ignore_tpu
  if torch.cuda.is_available():
    return torch.cuda.is_bfaa_supported()
  if is_npu_available():
    return False
  return True





def    _snake_case		()       ->		Dict:
  __UpperCAmelCase  :     List[str]		=    _is_package_available('bitsandbytes'     )
  if package_exists:
    __UpperCAmelCase  :     Optional[Any]		=    version.parse(importlib.metadata.version('bitsandbytes'     )     )
    return compare_versions(snake_case_						,					'>='						,					'0.39.0'     )
  return False





def    _snake_case		()       ->		Optional[int]:
  __UpperCAmelCase  :     Tuple		=    _is_package_available('bitsandbytes'     )
  if package_exists:
    __UpperCAmelCase  :     Tuple		=    version.parse(importlib.metadata.version('bitsandbytes'     )     )
    return compare_versions(snake_case_						,					'>='						,					'0.37.2'     )
  return False





def    _snake_case		()       ->		str:
  return _is_package_available('bitsandbytes'     )





def    _snake_case		()       ->		Union[str, Any]:
  if strtobool(os.environ.get('ACCELERATE_USE_MEGATRON_LM'						,					'False'     )     ) == 1:
    __UpperCAmelCase  :     int		=    importlib.util.find_spec('megatron'     ) is not None
    if package_exists:
      try:
        __UpperCAmelCase  :     int		=    parse(importlib.metadata.version('megatron-lm'     )     )
        return compare_versions(snake_case_						,					'>='						,					'2.2.0'     )
      except Exception as e:
        warnings.warn(F"Parse Megatron version failed. Exception:{e}"     )
        return False





def    _snake_case		()       ->		str:
  return _is_package_available('safetensors'     )





def    _snake_case		()       ->		Optional[Any]:
  return _is_package_available('transformers'     )





def    _snake_case		()       ->		Any:
  return _is_package_available('datasets'     )





def    _snake_case		()       ->		Any:
  return _is_package_available('aim'     )





def    _snake_case		()       ->		str:
  return _is_package_available('tensorboard'     ) or _is_package_available('tensorboardX'     )





def    _snake_case		()       ->		Dict:
  return _is_package_available('wandb'     )





def    _snake_case		()       ->		List[str]:
  return _is_package_available('comet_ml'     )





def    _snake_case		()       ->		Dict:
  return _is_package_available('boto3'     )





def    _snake_case		()       ->		List[str]:
  if _is_package_available('rich'     ):
    if "ACCELERATE_DISABLE_RICH" in os.environ:
      warnings.warn(
          '`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'     )
      return not parse_flag_from_env('ACCELERATE_DISABLE_RICH'						,					snake_case_     )
    return parse_flag_from_env('ACCELERATE_ENABLE_RICH'						,					snake_case_     )
  return False





def    _snake_case		()       ->		Optional[Any]:
  return _is_package_available('sagemaker'     )





def    _snake_case		()       ->		Tuple:
  return _is_package_available('tqdm'     )





def    _snake_case		()       ->		Tuple:
  return _is_package_available('mlflow'     )





def    _snake_case		()       ->		int:
  return is_torch_version('>='						,					'1.12'     ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()





def    _snake_case		()       ->		Dict:
  def get_major_and_minor_from_version(snake_case_				:    Tuple     ):
    return str(version.parse(snake_case_     ).major     ) + "." + str(version.parse(snake_case_     ).minor     )

  __UpperCAmelCase  :     Union[str, Any]		=    importlib.metadata.version('torch'     )
  if importlib.util.find_spec('intel_extension_for_pytorch'     ) is None:
    return False
  __UpperCAmelCase  :     str		=    'N/A'
  try:
    __UpperCAmelCase  :     Optional[int]		=    importlib.metadata.version('intel_extension_for_pytorch'     )
  except importlib.metadata.PackageNotFoundError:
    return False
  __UpperCAmelCase  :     Optional[Any]		=    get_major_and_minor_from_version(_torch_version     )
  __UpperCAmelCase  :     Optional[Any]		=    get_major_and_minor_from_version(_ipex_version     )
  if torch_major_and_minor != ipex_major_and_minor:
    warnings.warn(
        F"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"
        F" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."     )
    return False
  return True





@lru_cache
def    _snake_case		(snake_case_				:    Union[str, Any]=False     )       ->		str:
  if importlib.util.find_spec('torch'     ) is None or importlib.util.find_spec('torch_npu'     ) is None:
    return False

  import torch
  import torch_npu  # noqa: F401

  if check_device:
    try:
      # Will raise a RuntimeError if no NPU is found
      __UpperCAmelCase  :     int		=    torch.npu.device_count()
      return torch.npu.is_available()
    except RuntimeError:
      return False
  return hasattr(snake_case_						,					'npu'     ) and torch.npu.is_available()







@lru_cache
def    _snake_case		(snake_case_				:    List[Any]=False     )       ->		Tuple:
  if not parse_flag_from_env('ACCELERATE_USE_XPU'						,					default=snake_case_     ):
    return False
  if is_ipex_available():
    import torch

    if is_torch_version('<='						,					'1.12'     ):
      return False
  else:
    return False

  import intel_extension_for_pytorch  # noqa: F401

  if check_device:
    try:
      # Will raise a RuntimeError if no XPU  is found
      __UpperCAmelCase  :     Optional[int]		=    torch.xpu.device_count()
      return torch.xpu.is_available()
    except RuntimeError:
      return False
  return hasattr(snake_case_						,					'xpu'     ) and torch.xpu.is_available()








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


_SCREAMING_SNAKE_CASE          = logging.getLogger(__name__)





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





def    _snake_case		(snake_case_				:    Optional[int]="gpt2"     )       ->		Optional[Any]:
  __UpperCAmelCase  :     Dict		=    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_				:    Any						,					snake_case_				:    Optional[int]						,					snake_case_				:    List[str]     )       ->		Dict:
  __UpperCAmelCase  :     List[str]		=    copy.deepcopy(snake_case_     )
  model.to(snake_case_     )

  __UpperCAmelCase  :     Optional[int]		=    ['bias', 'LayerNorm.weight']
  __UpperCAmelCase  :     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  :     List[Any]		=    AdamW(snake_case_						,					lr=5e-5						,					eps=1e-8     )
  __UpperCAmelCase  :     int		=    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_				:    List[str]						,					snake_case_				:    List[str]						,					snake_case_				:    Union[str, Any]						,					snake_case_				:    Tuple     )       ->		Tuple:
  __UpperCAmelCase  :     Optional[Any]		=    np.array(snake_case_     ).mean()
  __UpperCAmelCase  :     str		=    np.array(snake_case_     ).std()
  __UpperCAmelCase  :     List[str]		=    (real_perps - avg) / std
  __UpperCAmelCase  :     List[str]		=    list(zip(snake_case_						,					snake_case_						,					snake_case_     )     )
  joblib.dump(snake_case_						,					snake_case_     )





def    _snake_case		(snake_case_				:    str						,					snake_case_				:    Optional[Any]						,					snake_case_				:    Optional[int]						,					snake_case_				:    List[str]						,					snake_case_				:    Any						,					snake_case_				:    Dict						,					snake_case_				:    int						,					snake_case_				:    Optional[int]="dev.jbl"						,					snake_case_				:    Tuple=recopy_gpta						,					)       ->		Dict:
  __UpperCAmelCase  :     Dict		=    []
  __UpperCAmelCase  :     int		=    []
  __UpperCAmelCase  :     Optional[Any]		=    []

  # Initialize the transformer model
  __UpperCAmelCase  :     Tuple		=    copy.deepcopy(snake_case_     )
  orig_model.to(device='cpu'     )
  torch.cuda.empty_cache()

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

  for step in tqdm(range(snake_case_     )     ):
    __UpperCAmelCase  :     Optional[int]		=    torch.zeros((1, context_len)						,					dtype=torch.long						,					device=snake_case_     )
    __UpperCAmelCase  :     List[Any]		=    random.choice(snake_case_     )
    __UpperCAmelCase  :     Optional[Any]		=    random.randint(0						,					len(story[0]     ) - context_len - 1     )
    __UpperCAmelCase  :     Union[str, Any]		=    story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    __UpperCAmelCase  :     List[Any]		=    model(snake_case_						,					labels=snake_case_     )
    __UpperCAmelCase  :     Optional[int]		=    outputs[0]
    __UpperCAmelCase  :     Union[str, Any]		=    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
    __UpperCAmelCase  :     List[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
    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     int		=    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_				:    int="data/tokenized_stories_train_wikitext103.jbl"						,					snake_case_				:    Tuple=100						,					snake_case_				:    str=1026						,					snake_case_				:    Union[str, Any]=True     )       ->		List[str]:
  __UpperCAmelCase  :     Union[str, Any]		=    joblib.load(snake_case_     )
  print('data loaded'     )
  __UpperCAmelCase  :     Any		=    []
  if trim:
    for i, example in enumerate(snake_case_     ):
      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(snake_case_     ) >= number:
        break
    __UpperCAmelCase  :     Union[str, Any]		=    []
    for j in range(i + 1						,					len(snake_case_     )     ):
      if len(data[j][0]     ) > min_len:
        train_data.append(data[j]     )
  else:
    __UpperCAmelCase  :     Any		=    data[0:number]
    __UpperCAmelCase  :     int		=    data[number:]

  joblib.dump(snake_case_						,					'objective_set.jbl'     )
  print('objective set saved'     )
  return train_data, objective_set





def    _snake_case		(snake_case_				:    Optional[Any]						,					snake_case_				:    Tuple						,					snake_case_				:    Optional[Any]						,					snake_case_				:    str						,					snake_case_				:    Dict=50						,					snake_case_				:    Any="secondary_learner.pt"     )       ->		Tuple:
  __UpperCAmelCase  :     int		=    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[:512]
  __UpperCAmelCase  :     Tuple		=    train_dataset[512:]
  __UpperCAmelCase  :     Any		=    DataLoader(snake_case_						,					shuffle=snake_case_						,					batch_size=snake_case_     )
  __UpperCAmelCase  :     str		=    DataLoader(snake_case_						,					shuffle=snake_case_						,					batch_size=snake_case_     )

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

        secondary_learner.eval()
        __UpperCAmelCase  :     List[str]		=    0.0
        __UpperCAmelCase  :     Tuple		=    0.0
        __UpperCAmelCase  :     int		=    []
        __UpperCAmelCase  :     Dict		=    []
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(snake_case_     ):
          __UpperCAmelCase  :     Optional[Any]		=    batcha[0].to(snake_case_     )
          __UpperCAmelCase  :     Optional[Any]		=    batcha[1].to(snake_case_     )
          __UpperCAmelCase  :     List[str]		=    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_     )
          __UpperCAmelCase  :     int		=    q_lossa

      secondary_learner.train()
  return secondary_learner







class     lowerCamelCase_   ( nn.Module):




 def __init__( self     ,     _UpperCAmelCase       ):
   super(_UpperCAmelCase     ,     self       ).__init__()
   __UpperCAmelCase  :     List[str]		=    model.transformer.wte
   __UpperCAmelCase  :     Optional[int]		=    copy.deepcopy(model.transformer.wte.weight       )
   __UpperCAmelCase  :     str		=    nn.Convad(self.embeddings.weight.size(1       )     ,     256     ,     3     ,     padding=1       )
   __UpperCAmelCase  :     Dict		=    nn.Sequential(nn.Linear(256     ,     32       )     ,     nn.Dropout(p=0.1       )     ,     nn.Linear(32     ,     32       )     ,     nn.Linear(32     ,     1       )       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Union[str, Any]		=    torch.max(self.conv(self.embeddings(_UpperCAmelCase       ).squeeze(1       ).transpose(1     ,     2       )       )     ,     2       )[0]
   __UpperCAmelCase  :     Dict		=    self.fc(_UpperCAmelCase       )
   return qs.squeeze(1       )




 @classmethod
 def 						a					( cls     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     List[Any]		=    cls(_UpperCAmelCase       )  # this calls __init__
   __UpperCAmelCase  :     int		=    torch.load(_UpperCAmelCase       )
   secondary_learner.load_state_dict(_UpperCAmelCase       )
   __UpperCAmelCase  :     List[Any]		=    model.transformer.wte
   __UpperCAmelCase  :     List[Any]		=    copy.deepcopy(model.transformer.wte.weight       )
   return secondary_learner





