





"""simple docstring"""





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__A											=		logging.get_logger(__name__)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=False			)					->Optional[int]:





  """simple docstring"""


  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
        ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
        ' instructions.'			)
    raise

  if not is_sharded:
    lowerCAmelCase__			:Optional[Any]											=							os.path.abspath(_SCREAMING_SNAKE_CASE			)
    logger.info(F"Loading PyTorch weights from {pt_path}"			)

    lowerCAmelCase__			:Union[str, Any]											=							torch.load(_SCREAMING_SNAKE_CASE     ,   map_location='cpu'			)
    logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()			):,} parameters."			)

    lowerCAmelCase__			:List[str]											=							convert_pytorch_state_dict_to_flax(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    lowerCAmelCase__			:Optional[Any]											=							convert_pytorch_sharded_state_dict_to_flax(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  return flax_state_dict
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   )					->(Tuple[str], np.ndarray):





  """simple docstring"""


  def is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE			) -> bool:
    return len(set(_SCREAMING_SNAKE_CASE			) & {key, (model_prefix,) + key}			) > 0

  # layer norm
  lowerCAmelCase__			:Dict											=							pt_tuple_key[:-1] + ('scale',)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE			):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  lowerCAmelCase__			:Dict											=							pt_tuple_key[:-1] + ('mean',)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE			):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  lowerCAmelCase__			:Optional[Any]											=							pt_tuple_key[:-1] + ('var',)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE			):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  lowerCAmelCase__			:str											=							pt_tuple_key[:-1] + ('embedding',)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE			):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  lowerCAmelCase__			:Union[str, Any]											=							pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE			):
    lowerCAmelCase__			:Union[str, Any]											=							pt_tensor.transpose(2     ,   3     ,   1     ,   0			)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  lowerCAmelCase__			:int											=							pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE			):
    lowerCAmelCase__			:Any											=							pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  lowerCAmelCase__			:str											=							pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  lowerCAmelCase__			:Optional[int]											=							pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  lowerCAmelCase__			:List[str]											=							None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    lowerCAmelCase__			:Union[str, Any]											=							pt_tuple_key[-2] + '_g'
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    lowerCAmelCase__			:Any											=							pt_tuple_key[-2] + '_v'
  if name is not None:
    lowerCAmelCase__			:List[str]											=							pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__			:str											=							{k: v.numpy() for k, v in pt_state_dict.items()}

  lowerCAmelCase__			:Tuple											=							flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    lowerCAmelCase__			:Union[str, Any]											=							flax_model.params['params']
  else:
    lowerCAmelCase__			:int											=							flax_model.params
  lowerCAmelCase__			:str											=							flatten_dict(_SCREAMING_SNAKE_CASE			)

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    lowerCAmelCase__			:List[str]											=							flatten_dict(flax_model.params['batch_stats']			)
    random_flax_state_dict.update(_SCREAMING_SNAKE_CASE			)

  lowerCAmelCase__			:Tuple											=							{}

  lowerCAmelCase__			:int											=							(model_prefix not in flax_model_params) and (
      model_prefix in {k.split('.'			)[0] for k in pt_state_dict.keys()}
  )
  lowerCAmelCase__			:Dict											=							(model_prefix in flax_model_params) and (
      model_prefix not in {k.split('.'			)[0] for k in pt_state_dict.keys()}
  )

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    lowerCAmelCase__			:str											=							tuple(pt_key.split('.'			)			)

    # remove base model prefix if necessary
    lowerCAmelCase__			:Union[str, Any]											=							pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      lowerCAmelCase__			:Any											=							pt_tuple_key[1:]

    # Correctly rename weight parameters
    lowerCAmelCase__							,				lowerCAmelCase__			:str											=							rename_key_and_reshape_tensor(
        _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

    # add model prefix if necessary
    lowerCAmelCase__			:Dict											=							(model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      lowerCAmelCase__			:str											=							(model_prefix,) + flax_key

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
            F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."			)

        # add batch stats if the model contains batchnorm layers
    if "batch_stats" in flax_model.params:
      if "mean" in flax_key[-1] or "var" in flax_key[-1]:
        lowerCAmelCase__			:Union[str, Any]											=							jnp.asarray(_SCREAMING_SNAKE_CASE			)
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
        continue

      # also add unexpected weight so that warning is thrown
      lowerCAmelCase__			:Optional[int]											=							jnp.asarray(_SCREAMING_SNAKE_CASE			)

    else:
      # also add unexpected weight so that warning is thrown
      lowerCAmelCase__			:Any											=							jnp.asarray(_SCREAMING_SNAKE_CASE			)

  return unflatten_dict(_SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->List[str]:





  """simple docstring"""


  import torch

  # Load the index
  lowerCAmelCase__			:Optional[Any]											=							{}
  for shard_file in shard_filenames:
    # load using msgpack utils
    lowerCAmelCase__			:List[str]											=							torch.load(_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:str											=							{k: v.numpy() for k, v in pt_state_dict.items()}

    lowerCAmelCase__			:int											=							flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
      lowerCAmelCase__			:List[str]											=							flax_model.params['params']

      lowerCAmelCase__			:str											=							flatten_dict(_SCREAMING_SNAKE_CASE			)
      random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']			)			)
    else:
      lowerCAmelCase__			:Any											=							flax_model.params
      lowerCAmelCase__			:Optional[int]											=							flatten_dict(_SCREAMING_SNAKE_CASE			)

    lowerCAmelCase__			:List[str]											=							(model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.'			)[0] for k in pt_state_dict.keys()}
    )
    lowerCAmelCase__			:Dict											=							(model_prefix in flax_model_params) and (
        model_prefix not in {k.split('.'			)[0] for k in pt_state_dict.keys()}
    )
    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
      lowerCAmelCase__			:Tuple											=							tuple(pt_key.split('.'			)			)

      # remove base model prefix if necessary
      lowerCAmelCase__			:List[str]											=							pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        lowerCAmelCase__			:Optional[int]											=							pt_tuple_key[1:]

      # Correctly rename weight parameters
      lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							rename_key_and_reshape_tensor(
          _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
      # add model prefix if necessary
      lowerCAmelCase__			:Dict											=							(model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        lowerCAmelCase__			:List[str]											=							(model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
        if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
          raise ValueError(
              F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
              F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."			)

            # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
        if "mean" in flax_key[-1]:
          lowerCAmelCase__			:Any											=							jnp.asarray(_SCREAMING_SNAKE_CASE			)
          continue
        if "var" in flax_key[-1]:
          lowerCAmelCase__			:Tuple											=							jnp.asarray(_SCREAMING_SNAKE_CASE			)
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
          continue

        # also add unexpected weight so that warning is thrown
        lowerCAmelCase__			:int											=							jnp.asarray(_SCREAMING_SNAKE_CASE			)

      else:
        # also add unexpected weight so that warning is thrown
        lowerCAmelCase__			:Optional[Any]											=							jnp.asarray(_SCREAMING_SNAKE_CASE			)
  return unflatten_dict(_SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__			:Optional[Any]											=							os.path.abspath(_SCREAMING_SNAKE_CASE			)
  logger.info(F"Loading Flax weights from {flax_checkpoint_path}"			)

  # import correct flax class
  lowerCAmelCase__			:List[Any]											=							getattr(_SCREAMING_SNAKE_CASE     ,   'Flax' + model.__class__.__name__			)

  # load flax weight dict
  with open(_SCREAMING_SNAKE_CASE     ,   'rb'			) as state_f:
    try:
      lowerCAmelCase__			:str											=							from_bytes(_SCREAMING_SNAKE_CASE     ,   state_f.read()			)
    except UnpicklingError:
      raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. "			)

  return load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)




def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->List[str]:





  """simple docstring"""


  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
        ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
        ' instructions.'			)
    raise

  # check if we have bf16 weights
  lowerCAmelCase__			:str											=							flatten_dict(jax.tree_util.tree_map(lambda _SCREAMING_SNAKE_CASE			: x.dtype == jnp.bfloataa     ,   _SCREAMING_SNAKE_CASE			)			).values()
  if any(_SCREAMING_SNAKE_CASE			):
    # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
    # and bf16 is not fully supported in PT yet.
    logger.warning(
        'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
        'before loading those in PyTorch model.'			)
    lowerCAmelCase__			:Optional[int]											=							jax.tree_util.tree_map(
        lambda _SCREAMING_SNAKE_CASE			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params     ,   _SCREAMING_SNAKE_CASE			)

  lowerCAmelCase__			:Dict											=							flatten_dict(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:int											=							pt_model.state_dict()

  lowerCAmelCase__			:Union[str, Any]											=							(pt_model.base_model_prefix in flax_state) and (
      pt_model.base_model_prefix not in {k.split('.'			)[0] for k in pt_model_dict.keys()}
  )
  lowerCAmelCase__			:List[str]											=							(pt_model.base_model_prefix not in flax_state) and (
      pt_model.base_model_prefix in {k.split('.'			)[0] for k in pt_model_dict.keys()}
  )

  # keep track of unexpected & missing keys
  lowerCAmelCase__			:Any											=							[]
  lowerCAmelCase__			:Union[str, Any]											=							set(pt_model_dict.keys()			)

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    lowerCAmelCase__			:Tuple											=							flax_key_tuple[0] == pt_model.base_model_prefix
    lowerCAmelCase__			:Tuple											=							'.'.join((pt_model.base_model_prefix,) + flax_key_tuple			) in pt_model_dict

    # adapt flax_key to prepare for loading from/to base model only
    if load_model_with_head_into_base_model and has_base_model_prefix:
      lowerCAmelCase__			:Tuple											=							flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      lowerCAmelCase__			:Union[str, Any]											=							(pt_model.base_model_prefix,) + flax_key_tuple

    # rename flax weights to PyTorch format
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_SCREAMING_SNAKE_CASE			) not in pt_model_dict:
      # conv layer
      lowerCAmelCase__			:List[Any]											=							flax_key_tuple[:-1] + ('weight',)
      lowerCAmelCase__			:int											=							jnp.transpose(_SCREAMING_SNAKE_CASE     ,   (3, 2, 0, 1)			)
    elif flax_key_tuple[-1] == "kernel" and ".".join(_SCREAMING_SNAKE_CASE			) not in pt_model_dict:
      # linear layer
      lowerCAmelCase__			:List[str]											=							flax_key_tuple[:-1] + ('weight',)
      lowerCAmelCase__			:int											=							flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      lowerCAmelCase__			:int											=							flax_key_tuple[:-1] + ('weight',)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      lowerCAmelCase__			:Any											=							flax_key_tuple[:-1] + ('running_mean',)
    elif "var" in flax_key_tuple[-1]:
      lowerCAmelCase__			:str											=							flax_key_tuple[:-1] + ('running_var',)

    if "batch_stats" in flax_state:
      lowerCAmelCase__			:Optional[int]											=							'.'.join(flax_key_tuple[1:]			)  # Remove the params/batch_stats header
    else:
      lowerCAmelCase__			:Tuple											=							'.'.join(_SCREAMING_SNAKE_CASE			)

    # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
    lowerCAmelCase__			:Tuple											=							{}
    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    for key in pt_model_dict:
      lowerCAmelCase__			:List[str]											=							key.split('.'			)
      lowerCAmelCase__			:Tuple											=							None
      if key_components[-3::2] == ["parametrizations", "original0"]:
        lowerCAmelCase__			:str											=							key_components[-2] + '_g'
      elif key_components[-3::2] == ["parametrizations", "original1"]:
        lowerCAmelCase__			:List[str]											=							key_components[-2] + '_v'
      if name is not None:
        lowerCAmelCase__			:Optional[int]											=							key_components[:-3] + [name]
        lowerCAmelCase__			:Optional[Any]											=							'.'.join(_SCREAMING_SNAKE_CASE			)
        lowerCAmelCase__			:Optional[Any]											=							key

    if flax_key in special_pt_names:
      lowerCAmelCase__			:Optional[int]											=							special_pt_names[flax_key]

    if flax_key in pt_model_dict:
      if flax_tensor.shape != pt_model_dict[flax_key].shape:
        raise ValueError(
            F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
            F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."			)
      else:
        # add weight to pytorch dict
        lowerCAmelCase__			:List[Any]											=							np.asarray(_SCREAMING_SNAKE_CASE			) if not isinstance(_SCREAMING_SNAKE_CASE     ,   np.ndarray			) else flax_tensor
        lowerCAmelCase__			:str											=							torch.from_numpy(_SCREAMING_SNAKE_CASE			)
        # remove from missing keys
        missing_keys.remove(_SCREAMING_SNAKE_CASE			)
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(_SCREAMING_SNAKE_CASE			)

  pt_model.load_state_dict(_SCREAMING_SNAKE_CASE			)

  # re-transform missing_keys to list
  lowerCAmelCase__			:Optional[int]											=							list(_SCREAMING_SNAKE_CASE			)

  if len(_SCREAMING_SNAKE_CASE			) > 0:
    logger.warning(
        'Some weights of the Flax model were not used when initializing the PyTorch model'
        F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
        F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
        ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
        F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
        ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
        ' FlaxBertForSequenceClassification model).'			)
  else:
    logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"			)
  if len(_SCREAMING_SNAKE_CASE			) > 0:
    logger.warning(
        F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
        F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
        ' use it for predictions and inference.'			)
  else:
    logger.warning(
        F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
        'If your task is similar to the task the model of the checkpoint was trained on, '
        F"you can already use {pt_model.__class__.__name__} for predictions without further training."			)

  return pt_model







"""simple docstring"""





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


__A											=		logging.getLogger(__name__)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Union[str, Any]:





  """simple docstring"""


  if os.path.exists(_SCREAMING_SNAKE_CASE			):
    if os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)			) and os.path.isfile(
        os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)			):
      os.remove(os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)			)
    if os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,   'pytorch_model.bin'			)			) and os.path.isfile(
        os.path.join(_SCREAMING_SNAKE_CASE     ,   'pytorch_model.bin'			)			):
      os.remove(os.path.join(_SCREAMING_SNAKE_CASE     ,   'pytorch_model.bin'			)			)
  else:
    os.makedirs(_SCREAMING_SNAKE_CASE			)
  model.save_pretrained(_SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=False			)					->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__			:Dict											=							2
  if unlogit:
    lowerCAmelCase__			:List[str]											=							torch.pow(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:str											=							p * torch.log(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[str]											=							0
  return -plogp.sum(dim=-1			)
def  __A   (_SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  logger.info('lv, h >\t' + '\t'.join(F"{x + 1}" for x in range(len(_SCREAMING_SNAKE_CASE			)			)			)			)
  for row in range(len(_SCREAMING_SNAKE_CASE			)			):
    if tensor.dtype != torch.long:
      logger.info(F"layer {row + 1}:\t" + '\t'.join(F"{x:.5f}" for x in tensor[row].cpu().data			)			)
    else:
      logger.info(F"layer {row + 1}:\t" + '\t'.join(F"{x:d}" for x in tensor[row].cpu().data			)			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=True     ,   _SCREAMING_SNAKE_CASE=True     ,   _SCREAMING_SNAKE_CASE=None     ,   _SCREAMING_SNAKE_CASE=False			)					->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							model.config.num_hidden_layers, model.config.num_attention_heads
  lowerCAmelCase__			:Any											=							torch.zeros(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).to(args.device			)
  lowerCAmelCase__			:Tuple											=							torch.zeros(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).to(args.device			)

  if head_mask is None:
    lowerCAmelCase__			:Optional[int]											=							torch.ones(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).to(args.device			)

  head_mask.requires_grad_(requires_grad=_SCREAMING_SNAKE_CASE			)
  # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  if actually_pruned:
    lowerCAmelCase__			:List[str]											=							None

  lowerCAmelCase__			:Any											=							0.0
  lowerCAmelCase__			:Any											=							0.0
  for step, inputs in enumerate(tqdm(_SCREAMING_SNAKE_CASE     ,   desc='Iteration'     ,   disable=args.local_rank not in [-1, 0]			)			):
    lowerCAmelCase__			:str											=							tuple(t.to(args.device			) for t in inputs			)
    ((lowerCAmelCase__)							,				)			:Dict											=							inputs

    # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
    lowerCAmelCase__			:str											=							model(_SCREAMING_SNAKE_CASE     ,   labels=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE			)
    #  (loss), lm_logits, presents, (all hidden_states), (attentions)
    lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:str											=							(
        outputs[0],
        outputs[1],
        outputs[-1],
    )  # Loss and logits are the first, attention the last
    loss.backward()  # Backpropagate to populate the gradients in the head mask
    total_loss += loss.detach().cpu().numpy()
    if compute_entropy:
      for layer, attn in enumerate(_SCREAMING_SNAKE_CASE			):
        lowerCAmelCase__			:Optional[Any]											=							entropy(attn.detach()     ,   _SCREAMING_SNAKE_CASE			)
        attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).sum(0			).detach()

    if compute_importance:
      head_importance += head_mask.grad.abs().detach()
    tot_tokens += torch.ones_like(_SCREAMING_SNAKE_CASE			).float().detach().sum().data

  # Normalize
  attn_entropy /= tot_tokens
  head_importance /= tot_tokens
  # Layerwise importance normalization
  if not args.dont_normalize_importance_by_layer:
    lowerCAmelCase__			:Union[str, Any]											=							2
    lowerCAmelCase__			:Tuple											=							torch.pow(torch.pow(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).sum(-1			)     ,   1 / exponent			)
    head_importance /= norm_by_layer.unsqueeze(-1			) + 1e-20

  if not args.dont_normalize_global_importance:
    lowerCAmelCase__			:str											=							(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

  # Print matrices
  if compute_entropy:
    logger.info('Attention entropies'			)
    print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  if compute_importance:
    logger.info('Head importance scores'			)
    print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  logger.info('Head ranked by importance scores'			)
  lowerCAmelCase__			:List[Any]											=							torch.zeros(head_importance.numel()     ,   dtype=torch.long     ,   device=args.device			)
  lowerCAmelCase__			:List[Any]											=							torch.arange(
      head_importance.numel()     ,   device=args.device			)
  lowerCAmelCase__			:int											=							head_ranks.view_as(_SCREAMING_SNAKE_CASE			)
  print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  return attn_entropy, head_importance, total_loss
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							compute_heads_importance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[Any]											=							1 / loss  # instead of downsteam score use the LM loss
  logger.info('Pruning: original score: %f, threshold: %f'     ,   _SCREAMING_SNAKE_CASE     ,   original_score * args.masking_threshold			)

  lowerCAmelCase__			:Optional[int]											=							torch.ones_like(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Dict											=							max(1     ,   int(new_head_mask.numel() * args.masking_amount			)			)

  lowerCAmelCase__			:List[str]											=							original_score
  while current_score >= original_score * args.masking_threshold:
    lowerCAmelCase__			:List[str]											=							new_head_mask.clone().detach()  # save current head mask
    # heads from least important to most - keep only not-masked heads
    lowerCAmelCase__			:str											=							float('Inf'			)
    lowerCAmelCase__			:List[str]											=							head_importance.view(-1			).sort()[1]

    if len(_SCREAMING_SNAKE_CASE			) <= num_to_mask:
      print('BREAK BY num_to_mask'			)
      break

    # mask heads
    lowerCAmelCase__			:int											=							current_heads_to_mask[:num_to_mask]
    logger.info('Heads to mask: %s'     ,   str(current_heads_to_mask.tolist()			)			)
    lowerCAmelCase__			:Dict											=							new_head_mask.view(-1			)
    lowerCAmelCase__			:Any											=							0.0
    lowerCAmelCase__			:Tuple											=							new_head_mask.view_as(_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:Optional[int]											=							new_head_mask.clone().detach()
    print_ad_tensor(_SCREAMING_SNAKE_CASE			)

    # Compute metric and head importance again
    lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Optional[Any]											=							compute_heads_importance(
        _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:Any											=							1 / loss
    logger.info(
        'Masking: current score: %f, remaining heads %d (%.1f percents)'     ,   _SCREAMING_SNAKE_CASE     ,   new_head_mask.sum()     ,   new_head_mask.sum() / new_head_mask.numel() * 100     ,   )

  logger.info('Final head mask'			)
  print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  np.save(os.path.join(args.output_dir     ,   'head_mask.npy'			)     ,   head_mask.detach().cpu().numpy()			)

  return head_mask
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__			:Union[str, Any]											=							datetime.now()
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							compute_heads_importance(
      _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE     ,   compute_importance=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Any											=							1 / loss
  lowerCAmelCase__			:Tuple											=							datetime.now() - before_time

  lowerCAmelCase__			:List[str]											=							sum(p.numel() for p in model.parameters()			)
  lowerCAmelCase__			:List[Any]											=							{
      layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_SCREAMING_SNAKE_CASE			)			)
  }

  for k, v in heads_to_prune.items():
    if isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			):
      lowerCAmelCase__			:Union[str, Any]											=							[
          v,
      ]

  assert sum(len(_SCREAMING_SNAKE_CASE			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
  model.prune_heads(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Any											=							sum(p.numel() for p in model.parameters()			)

  lowerCAmelCase__			:int											=							datetime.now()
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							compute_heads_importance(
      _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE     ,   compute_importance=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE     ,   actually_pruned=_SCREAMING_SNAKE_CASE     ,   )

  lowerCAmelCase__			:int											=							1 / loss
  lowerCAmelCase__			:Tuple											=							datetime.now() - before_time

  logger.info(
      'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   pruned_num_params / original_num_params * 100     ,   )
  logger.info('Pruning: score with masking: %f score with pruning: %f'     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  logger.info('Pruning: speed ratio (original timing / new timing): %f percents'     ,   original_time / new_time * 100			)
  save_model(_SCREAMING_SNAKE_CASE     ,   args.output_dir			)
def  __A   ()					->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__			:List[str]											=							argparse.ArgumentParser()
  # Required parameters
  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 .tsv files (or other data files) for the task.'     ,   )
  parser.add_argument(
      '--model_name_or_path'     ,   default=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   required=_SCREAMING_SNAKE_CASE     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
  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.'     ,   )

  # Other parameters
  parser.add_argument(
      '--config_name'     ,   default=''     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Pretrained config name or path if not the same as model_name_or_path'     ,   )
  parser.add_argument(
      '--tokenizer_name'     ,   default=''     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Pretrained tokenizer name or path if not the same as model_name_or_path'     ,   )
  parser.add_argument(
      '--cache_dir'     ,   default=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Where do you want to store the pre-trained models downloaded from s3'     ,   )
  parser.add_argument(
      '--data_subset'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=-1     ,   help='If > 0: limit the data to a subset of data_subset instances.'			)
  parser.add_argument(
      '--overwrite_output_dir'     ,   action='store_true'     ,   help='Whether to overwrite data in output directory'			)
  parser.add_argument(
      '--overwrite_cache'     ,   action='store_true'     ,   help='Overwrite the cached training and evaluation sets'			)

  parser.add_argument(
      '--dont_normalize_importance_by_layer'     ,   action='store_true'     ,   help='Don\'t normalize importance score by layers'			)
  parser.add_argument(
      '--dont_normalize_global_importance'     ,   action='store_true'     ,   help='Don\'t normalize all importance scores between 0 and 1'     ,   )

  parser.add_argument(
      '--try_masking'     ,   action='store_true'     ,   help='Whether to try to mask head until a threshold of accuracy.'			)
  parser.add_argument(
      '--masking_threshold'     ,   default=0.9     ,   type=_SCREAMING_SNAKE_CASE     ,   help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'     ,   )
  parser.add_argument(
      '--masking_amount'     ,   default=0.1     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Amount to heads to masking at each masking step.'			)
  parser.add_argument('--metric_name'     ,   default='acc'     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Metric to use for head masking.'			)

  parser.add_argument(
      '--max_seq_length'     ,   default=128     ,   type=_SCREAMING_SNAKE_CASE     ,   help=(
          'The maximum total input sequence length after WordPiece tokenization. \n'
          'Sequences longer than this will be truncated, sequences shorter padded.'
      )     ,   )
  parser.add_argument('--batch_size'     ,   default=1     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Batch size.'			)

  parser.add_argument('--seed'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=42			)
  parser.add_argument('--local_rank'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=-1     ,   help='local_rank for distributed training on gpus'			)
  parser.add_argument('--no_cuda'     ,   action='store_true'     ,   help='Whether not to use CUDA when available'			)
  parser.add_argument('--server_ip'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=''     ,   help='Can be used for distant debugging.'			)
  parser.add_argument('--server_port'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=''     ,   help='Can be used for distant debugging.'			)
  lowerCAmelCase__			:Any											=							parser.parse_args()

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print('Waiting for debugger attach'			)
    ptvsd.enable_attach(address=(args.server_ip, args.server_port)     ,   redirect_output=_SCREAMING_SNAKE_CASE			)
    ptvsd.wait_for_attach()

  # Setup devices and distributed training
  if args.local_rank == -1 or args.no_cuda:
    lowerCAmelCase__			:List[Any]											=							torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'			)
    lowerCAmelCase__			:Optional[int]											=							0 if args.no_cuda else torch.cuda.device_count()
  else:
    torch.cuda.set_device(args.local_rank			)
    lowerCAmelCase__			:Dict											=							torch.device('cuda'     ,   args.local_rank			)
    lowerCAmelCase__			:Tuple											=							1
    torch.distributed.init_process_group(backend='nccl'			)  # Initializes the distributed backend

  # Setup logging
  logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN			)
  logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device     ,   args.n_gpu     ,   bool(args.local_rank != -1			)			)			)

  lowerCAmelCase__			:int											=							GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

  # Distributed and parallel training
  model.to(args.device			)
  if args.local_rank != -1:
    lowerCAmelCase__			:Optional[Any]											=							nn.parallel.DistributedDataParallel(
        _SCREAMING_SNAKE_CASE     ,   device_ids=[args.local_rank]     ,   output_device=args.local_rank     ,   find_unused_parameters=_SCREAMING_SNAKE_CASE			)
  elif args.n_gpu > 1:
    lowerCAmelCase__			:Union[str, Any]											=							nn.DataParallel(_SCREAMING_SNAKE_CASE			)

  # Print/save training arguments
  os.makedirs(args.output_dir     ,   exist_ok=_SCREAMING_SNAKE_CASE			)
  torch.save(_SCREAMING_SNAKE_CASE     ,   os.path.join(args.output_dir     ,   'run_args.bin'			)			)
  logger.info('Training/evaluation parameters %s'     ,   _SCREAMING_SNAKE_CASE			)

  # Prepare dataset
  lowerCAmelCase__			:Optional[int]											=							np.concatenate(
      [
          np.loadtxt(args.data_dir     ,   dtype=np.intaa			),
      ]			)
  lowerCAmelCase__			:Union[str, Any]											=							(torch.from_numpy(_SCREAMING_SNAKE_CASE			),)
  lowerCAmelCase__			:Optional[int]											=							TensorDataset(*_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[Any]											=							RandomSampler(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Dict											=							DataLoader(_SCREAMING_SNAKE_CASE     ,   sampler=_SCREAMING_SNAKE_CASE     ,   batch_size=args.batch_size			)

  # Compute head entropy and importance score
  compute_heads_importance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Try head masking (set heads to zero until the score goes under a threshole)
  # and head pruning (remove masked heads and see the effect on the network)
  if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
    lowerCAmelCase__			:Optional[Any]											=							mask_heads(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
    prune_heads(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  main()
