


'''simple docstring'''

import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[Tuple[int, ...]]:






       """simple docstring"""







       lowercase__      			= []
       if isinstance(A  , A       ):
              for v in tree.values():
                     shapes.extend(_fetch_dims(A       )       )
       elif isinstance(A  , (list, tuple)       ):
              for t in tree:
                     shapes.extend(_fetch_dims(A       )       )
       elif isinstance(A  , torch.Tensor       ):
              shapes.append(tree.shape       )
       else:
              raise ValueError('''Not supported'''       )

       return shapes

@torch.jit.ignore
def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Tuple[int, ...]:






       """simple docstring"""







       lowercase__      			= []
       for d in reversed(A       ):
              idx.append(flat_idx % d       )
              lowercase__      			= flat_idx // d

       return tuple(reversed(A       )       )

@torch.jit.ignore
def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A = None  , A = None  , )       ->       List[Tuple[slice, ...]]:






       """simple docstring"""







       def reduce_edge_list(A       ) -> None:
              lowercase__      			= True
              for i in range(len(A       )       ):
                     lowercase__      			= -1 * (i + 1)
                     l[reversed_idx] &= tally
                     lowercase__      			= l[reversed_idx]

       if start_edges is None:
              lowercase__      			= [s == 0 for s in start]
              reduce_edge_list(A       )
       if end_edges is None:
              lowercase__      			= [e == (d - 1) for e, d in zip(A  , A       )]
              reduce_edge_list(A       )

       # Base cases. Either start/end are empty and we're done, or the final,
       # one-dimensional tensor can be simply sliced
       if len(A       ) == 0:
              return [()]
       elif len(A       ) == 1:
              return [(slice(start[0]  , end[0] + 1       ),)]

       lowercase__      			= []
       lowercase__      			= []

       # Dimensions common to start and end can be selected directly
       for s, e in zip(A  , A       ):
              if s == e:
                     path_list.append(slice(A  , s + 1       )       )
              else:
                     break

       lowercase__      			= tuple(A       )
       lowercase__      			= len(A       )

       # start == end, and we're done
       if divergence_idx == len(A       ):
              return [path]

       def upper() -> Tuple[Tuple[slice, ...], ...]:
              assert start_edges is not None
              assert end_edges is not None

              lowercase__      			= start[divergence_idx]
              return tuple(
                  path + (slice(A  , sdi + 1       ),) + s
                  for s in _get_minimal_slice_set(
                      start[divergence_idx + 1 :]  , [d - 1 for d in dims[divergence_idx + 1 :]]  , dims[divergence_idx + 1 :]  , start_edges=start_edges[divergence_idx + 1 :]  , end_edges=[True for _ in end_edges[divergence_idx + 1 :]]  , )       )

       def lower() -> Tuple[Tuple[slice, ...], ...]:
              assert start_edges is not None
              assert end_edges is not None

              lowercase__      			= end[divergence_idx]
              return tuple(
                  path + (slice(A  , edi + 1       ),) + s
                  for s in _get_minimal_slice_set(
                      [0 for _ in start[divergence_idx + 1 :]]  , end[divergence_idx + 1 :]  , dims[divergence_idx + 1 :]  , start_edges=[True for _ in start_edges[divergence_idx + 1 :]]  , end_edges=end_edges[divergence_idx + 1 :]  , )       )

       # If both start and end are at the edges of the subtree rooted at
       # divergence_idx, we can just select the whole subtree at once
       if start_edges[divergence_idx] and end_edges[divergence_idx]:
              slices.append(path + (slice(start[divergence_idx]  , end[divergence_idx] + 1       ),)       )
       # If just start is at the edge, we can grab almost all of the subtree,
       # treating only the ragged bottom edge as an edge case
       elif start_edges[divergence_idx]:
              slices.append(path + (slice(start[divergence_idx]  , end[divergence_idx]       ),)       )
              slices.extend(lower()       )
       # Analogous to the previous case, but the top is ragged this time
       elif end_edges[divergence_idx]:
              slices.extend(upper()       )
              slices.append(path + (slice(start[divergence_idx] + 1  , end[divergence_idx] + 1       ),)       )
       # If both sides of the range are ragged, we need to handle both sides
       # separately. If there's contiguous meat in between them, we can index it
       # in one big chunk
       else:
              slices.extend(upper()       )
              lowercase__      			= end[divergence_idx] - start[divergence_idx]
              if middle_ground > 1:
                     slices.append(path + (slice(start[divergence_idx] + 1  , end[divergence_idx]       ),)       )
              slices.extend(lower()       )

       return slices

@torch.jit.ignore
def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A       )       ->       torch.Tensor:






       """simple docstring"""







       lowercase__      			= t.shape[:no_batch_dims]
       lowercase__      			= list(_flat_idx_to_idx(A  , A       )       )
       # _get_minimal_slice_set is inclusive
       lowercase__      			= list(_flat_idx_to_idx(flat_end - 1  , A       )       )

       # Get an ordered list of slices to perform
       lowercase__      			= _get_minimal_slice_set(
           A  , A  , A  , )

       lowercase__      			= [t[s] for s in slices]

       return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]       ) for s in sliced_tensors]       )

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A  , A = False  , A = None  , A = False  , )       ->       Any:






       """simple docstring"""







       if not (len(A       ) > 0):
              raise ValueError('''Must provide at least one input'''       )

       lowercase__      			= [shape[:no_batch_dims] for shape in _fetch_dims(A       )]
       lowercase__      			= tuple([max(A       ) for s in zip(*A       )]       )

       def _prep_inputs(A       ) -> torch.Tensor:
              if not low_mem:
                     if not sum(t.shape[:no_batch_dims]       ) == no_batch_dims:
                            lowercase__      			= t.expand(orig_batch_dims + t.shape[no_batch_dims:]       )
                     lowercase__      			= t.reshape(-1  , *t.shape[no_batch_dims:]       )
              else:
                     lowercase__      			= t.expand(orig_batch_dims + t.shape[no_batch_dims:]       )
              return t

       lowercase__      			= tensor_tree_map(_prep_inputs  , A       )
       lowercase__      			= None
       if _out is not None:
              lowercase__      			= tensor_tree_map(lambda A       : t.view([-1] + list(t.shape[no_batch_dims:]       )       )  , _out       )

       lowercase__      			= 1
       for d in orig_batch_dims:
              flat_batch_dim *= d

       lowercase__      			= flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

       def _select_chunk(A       ) -> torch.Tensor:
              return t[i : i + chunk_size] if t.shape[0] != 1 else t

       lowercase__      			= 0
       lowercase__      			= prepped_outputs
       for _ in range(A       ):
              # Chunk the input
              if not low_mem:
                     lowercase__      			= _select_chunk
              else:
                     lowercase__      			= partial(
                         _chunk_slice  , flat_start=A  , flat_end=min(A  , i + chunk_size       )  , no_batch_dims=len(A       )  , )

              lowercase__      			= tensor_tree_map(A  , A       )

              # Run the layer on the chunk
              lowercase__      			= layer(**A       )

              # Allocate space for the output
              if out is None:
                     lowercase__      			= tensor_tree_map(lambda A       : t.new_zeros((flat_batch_dim,) + t.shape[1:]       )  , A       )

              # Put the chunk in its pre-allocated space
              if isinstance(A  , A       ):

                     def assign(A  , A       ) -> None:
                            for k, v in da.items():
                                   if isinstance(A  , A       ):
                                          assign(A  , da[k]       )
                                   else:
                                          if _add_into_out:
                                                 v[i : i + chunk_size] += da[k]
                                          else:
                                                 lowercase__      			= da[k]

                     assign(A  , A       )
              elif isinstance(A  , A       ):
                     for xa, xa in zip(A  , A       ):
                            if _add_into_out:
                                   xa[i : i + chunk_size] += xa
                            else:
                                   lowercase__      			= xa
              elif isinstance(A  , torch.Tensor       ):
                     if _add_into_out:
                            out[i : i + chunk_size] += output_chunk
                     else:
                            lowercase__      			= output_chunk
              else:
                     raise ValueError('''Not supported'''       )

              i += chunk_size

       lowercase__      			= tensor_tree_map(lambda A       : t.view(orig_batch_dims + t.shape[1:]       )  , A       )

       return out

class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    Tuple   ,						UpperCamelCase    :    int = 512   ,						):

             '''simple docstring'''







             lowercase__      			= max_chunk_size
             lowercase__      			= None
             lowercase__      			= None



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    Callable   ,						UpperCamelCase    :    tuple   ,						UpperCamelCase    :    int ):

             '''simple docstring'''







             logging.info('''Tuning chunk size...''' )

             if min_chunk_size >= self.max_chunk_size:
                    return min_chunk_size

             lowercase__      			= [2**l for l in range(int(math.log(self.max_chunk_size   ,						2 ) ) + 1 )]
             lowercase__      			= [c for c in candidates if c > min_chunk_size]
             lowercase__      			= [min_chunk_size] + candidates
             candidates[-1] += 4

             def test_chunk_size(UpperCamelCase    :    int ) -> bool:
                    try:
                           with torch.no_grad():
                                  fn(*UpperCamelCase   ,						chunk_size=UpperCamelCase )
                           return True
                    except RuntimeError:
                           return False

             lowercase__      			= 0
             lowercase__      			= len(UpperCamelCase ) - 1
             while i > min_viable_chunk_size_index:
                    lowercase__      			= test_chunk_size(candidates[i] )
                    if not viable:
                           lowercase__      			= (min_viable_chunk_size_index + i) // 2
                    else:
                           lowercase__      			= i
                           lowercase__      			= (i + len(UpperCamelCase ) - 1) // 2

             return candidates[min_viable_chunk_size_index]



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    Iterable   ,						UpperCamelCase    :    Iterable ):

             '''simple docstring'''







             lowercase__      			= True
             for aa, aa in zip(UpperCamelCase   ,						UpperCamelCase ):
                    assert type(UpperCamelCase ) == type(UpperCamelCase )
                    if isinstance(UpperCamelCase   ,						(list, tuple) ):
                           consistent &= self._compare_arg_caches(UpperCamelCase   ,						UpperCamelCase )
                    elif isinstance(UpperCamelCase   ,						UpperCamelCase ):
                           lowercase__      			= [v for _, v in sorted(aa.items()   ,						key=lambda UpperCamelCase : x[0] )]
                           lowercase__      			= [v for _, v in sorted(aa.items()   ,						key=lambda UpperCamelCase : x[0] )]
                           consistent &= self._compare_arg_caches(UpperCamelCase   ,						UpperCamelCase )
                    else:
                           consistent &= aa == aa

             return consistent






      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    Callable   ,						UpperCamelCase    :    tuple   ,						UpperCamelCase    :    int   ,						):

             '''simple docstring'''







             lowercase__      			= True
             lowercase__      			= tree_map(lambda UpperCamelCase : a.shape if isinstance(UpperCamelCase   ,						torch.Tensor ) else a   ,						UpperCamelCase   ,						UpperCamelCase )
             if self.cached_arg_data is not None:
                    # If args have changed shape/value, we need to re-tune
                    assert len(self.cached_arg_data ) == len(UpperCamelCase )
                    lowercase__      			= self._compare_arg_caches(self.cached_arg_data   ,						UpperCamelCase )
             else:
                    # Otherwise, we can reuse the precomputed value
                    lowercase__      			= False

             if not consistent:
                    lowercase__      			= self._determine_favorable_chunk_size(
                        UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						)
                    lowercase__      			= arg_data

             assert self.cached_chunk_size is not None

             return self.cached_chunk_size








"""simple docstring"""



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__snake_case   			=    {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}







def   __lowerCAmelCase						(						lowercase  :  Any )	->						Union[str, Any]:


    """simple docstring"""





    assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
    assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
    if args.mlm:
        assert os.path.isfile(args.token_counts )
        assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
    else:
        assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

    assert args.teacher_type == args.student_type or (
        args.student_type == "distilbert" and args.teacher_type == "bert"
    )
    assert os.path.isfile(args.student_config )
    if args.student_pretrained_weights is not None:
        assert os.path.isfile(args.student_pretrained_weights )

    if args.freeze_token_type_embds:
        assert args.student_type in ["roberta"]

    assert args.alpha_ce >= 0.0
    assert args.alpha_mlm >= 0.0
    assert args.alpha_clm >= 0.0
    assert args.alpha_mse >= 0.0
    assert args.alpha_cos >= 0.0
    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Dict )	->						Tuple:


    """simple docstring"""





    if args.student_type == "roberta":
        snake_case    :      List[str]      					=    False
    elif args.student_type == "gpt2":
        snake_case    :      Optional[int]      					=    False







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Optional[int] )	->						Optional[Any]:


    """simple docstring"""





    if args.student_type == "roberta":
        snake_case    :      Optional[Any]      					=    False







def   __lowerCAmelCase						(						)	->						int:


    """simple docstring"""





    snake_case    :      Any      					=    argparse.ArgumentParser(description="Training" )
    parser.add_argument("--force"						,							action="store_true"						,							help="Overwrite dump_path if it already exists." )

    parser.add_argument(
        "--dump_path"						,							type=lowercase						,							required=lowercase						,							help="The output directory (log, checkpoints, parameters, etc.)" )
    parser.add_argument(
        "--data_file"						,							type=lowercase						,							required=lowercase						,							help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."						,							)

    parser.add_argument(
        "--student_type"						,							type=lowercase						,							choices=["distilbert", "roberta", "gpt2"]						,							required=lowercase						,							help="The student type (DistilBERT, RoBERTa)."						,							)
    parser.add_argument("--student_config"						,							type=lowercase						,							required=lowercase						,							help="Path to the student configuration." )
    parser.add_argument(
        "--student_pretrained_weights"						,							default=lowercase						,							type=lowercase						,							help="Load student initialization checkpoint." )

    parser.add_argument(
        "--teacher_type"						,							choices=["bert", "roberta", "gpt2"]						,							required=lowercase						,							help="Teacher type (BERT, RoBERTa)." )
    parser.add_argument("--teacher_name"						,							type=lowercase						,							required=lowercase						,							help="The teacher model." )

    parser.add_argument("--temperature"						,							default=2.0						,							type=lowercase						,							help="Temperature for the softmax temperature." )
    parser.add_argument(
        "--alpha_ce"						,							default=0.5						,							type=lowercase						,							help="Linear weight for the distillation loss. Must be >=0." )
    parser.add_argument(
        "--alpha_mlm"						,							default=0.0						,							type=lowercase						,							help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."						,							)
    parser.add_argument("--alpha_clm"						,							default=0.5						,							type=lowercase						,							help="Linear weight for the CLM loss. Must be >=0." )
    parser.add_argument("--alpha_mse"						,							default=0.0						,							type=lowercase						,							help="Linear weight of the MSE loss. Must be >=0." )
    parser.add_argument(
        "--alpha_cos"						,							default=0.0						,							type=lowercase						,							help="Linear weight of the cosine embedding loss. Must be >=0." )

    parser.add_argument(
        "--mlm"						,							action="store_true"						,							help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
    parser.add_argument(
        "--mlm_mask_prop"						,							default=0.15						,							type=lowercase						,							help="Proportion of tokens for which we need to make a prediction."						,							)
    parser.add_argument("--word_mask"						,							default=0.8						,							type=lowercase						,							help="Proportion of tokens to mask out." )
    parser.add_argument("--word_keep"						,							default=0.1						,							type=lowercase						,							help="Proportion of tokens to keep." )
    parser.add_argument("--word_rand"						,							default=0.1						,							type=lowercase						,							help="Proportion of tokens to randomly replace." )
    parser.add_argument(
        "--mlm_smoothing"						,							default=0.7						,							type=lowercase						,							help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."						,							)
    parser.add_argument("--token_counts"						,							type=lowercase						,							help="The token counts in the data_file for MLM." )

    parser.add_argument(
        "--restrict_ce_to_mask"						,							action="store_true"						,							help="If true, compute the distillation loss only the [MLM] prediction distribution."						,							)
    parser.add_argument(
        "--freeze_pos_embs"						,							action="store_true"						,							help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."						,							)
    parser.add_argument(
        "--freeze_token_type_embds"						,							action="store_true"						,							help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."						,							)

    parser.add_argument("--n_epoch"						,							type=lowercase						,							default=3						,							help="Number of pass on the whole dataset." )
    parser.add_argument("--batch_size"						,							type=lowercase						,							default=5						,							help="Batch size (for each process)." )
    parser.add_argument(
        "--group_by_size"						,							action="store_false"						,							help="If true, group sequences that have similar length into the same batch. Default is true."						,							)

    parser.add_argument(
        "--gradient_accumulation_steps"						,							type=lowercase						,							default=50						,							help="Gradient accumulation for larger training batches."						,							)
    parser.add_argument("--warmup_prop"						,							default=0.05						,							type=lowercase						,							help="Linear warmup proportion." )
    parser.add_argument("--weight_decay"						,							default=0.0						,							type=lowercase						,							help="Weight decay if we apply some." )
    parser.add_argument("--learning_rate"						,							default=5e-4						,							type=lowercase						,							help="The initial learning rate for Adam." )
    parser.add_argument("--adam_epsilon"						,							default=1e-6						,							type=lowercase						,							help="Epsilon for Adam optimizer." )
    parser.add_argument("--max_grad_norm"						,							default=5.0						,							type=lowercase						,							help="Max gradient norm." )
    parser.add_argument("--initializer_range"						,							default=0.02						,							type=lowercase						,							help="Random initialization range." )

    parser.add_argument(
        "--fp16"						,							action="store_true"						,							help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"						,							)
    parser.add_argument(
        "--fp16_opt_level"						,							type=lowercase						,							default="O1"						,							help=(
            "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
            "See details at https://nvidia.github.io/apex/amp.html"
        )						,							)
    parser.add_argument("--n_gpu"						,							type=lowercase						,							default=1						,							help="Number of GPUs in the node." )
    parser.add_argument("--local_rank"						,							type=lowercase						,							default=-1						,							help="Distributed training - Local rank" )
    parser.add_argument("--seed"						,							type=lowercase						,							default=56						,							help="Random seed" )

    parser.add_argument("--log_interval"						,							type=lowercase						,							default=500						,							help="Tensorboard logging interval." )
    parser.add_argument("--checkpoint_interval"						,							type=lowercase						,							default=4000						,							help="Checkpoint interval." )
    snake_case    :      str      					=    parser.parse_args()
    sanity_checks(lowercase )

    # ARGS #
    init_gpu_params(lowercase )
    set_seed(lowercase )
    if args.is_master:
        if os.path.exists(args.dump_path ):
            if not args.force:
                raise ValueError(
                    F'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
                    " itUse `--force` if you want to overwrite it" )
            else:
                shutil.rmtree(args.dump_path )

        if not os.path.exists(args.dump_path ):
            os.makedirs(args.dump_path )
        logger.info(F'Experiment will be dumped and logged in {args.dump_path}' )

        # SAVE PARAMS #
        logger.info(F'Param: {args}' )
        with open(os.path.join(args.dump_path						,							"parameters.json" )						,							"w" ) as f:
            json.dump(vars(lowercase )						,							lowercase						,							indent=4 )
        git_log(args.dump_path )

    snake_case					,snake_case					,snake_case    :      int      					=    MODEL_CLASSES[args.student_type]
    snake_case					,snake_case					,snake_case    :      int      					=    MODEL_CLASSES[args.teacher_type]

    # TOKENIZER #
    snake_case    :      int      					=    teacher_tokenizer_class.from_pretrained(args.teacher_name )
    snake_case    :      Any      					=    {}
    for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
        snake_case    :      List[str]      					=    tokenizer.all_special_tokens.index(lowercase )
        snake_case    :      Optional[int]      					=    tokenizer.all_special_ids[idx]
    logger.info(F'Special tokens {special_tok_ids}' )
    snake_case    :      Any      					=    special_tok_ids
    snake_case    :      Dict      					=    tokenizer.max_model_input_sizes[args.teacher_name]

    # DATA LOADER #
    logger.info(F'Loading data from {args.data_file}' )
    with open(args.data_file						,							"rb" ) as fp:
        snake_case    :      Optional[int]      					=    pickle.load(lowercase )

    if args.mlm:
        logger.info(F'Loading token counts from {args.token_counts} (already pre-computed)' )
        with open(args.token_counts						,							"rb" ) as fp:
            snake_case    :      Dict      					=    pickle.load(lowercase )

        snake_case    :      str      					=    np.maximum(lowercase						,							1 ) ** -args.mlm_smoothing
        for idx in special_tok_ids.values():
            snake_case    :      str      					=    0.0  # do not predict special tokens
        snake_case    :      Dict      					=    torch.from_numpy(lowercase )
    else:
        snake_case    :      Tuple      					=    None

    snake_case    :      str      					=    LmSeqsDataset(params=lowercase						,							data=lowercase )
    logger.info("Data loader created." )

    # STUDENT #
    logger.info(F'Loading student config from {args.student_config}' )
    snake_case    :      str      					=    student_config_class.from_pretrained(args.student_config )
    snake_case    :      Optional[int]      					=    True

    if args.student_pretrained_weights is not None:
        logger.info(F'Loading pretrained weights from {args.student_pretrained_weights}' )
        snake_case    :      Optional[int]      					=    student_model_class.from_pretrained(args.student_pretrained_weights						,							config=lowercase )
    else:
        snake_case    :      Union[str, Any]      					=    student_model_class(lowercase )

    if args.n_gpu > 0:
        student.to(F'cuda:{args.local_rank}' )
    logger.info("Student loaded." )

    # TEACHER #
    snake_case    :      List[Any]      					=    teacher_model_class.from_pretrained(args.teacher_name						,							output_hidden_states=lowercase )
    if args.n_gpu > 0:
        teacher.to(F'cuda:{args.local_rank}' )
    logger.info(F'Teacher loaded from {args.teacher_name}.' )

    # FREEZING #
    if args.freeze_pos_embs:
        freeze_pos_embeddings(lowercase						,							lowercase )
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(lowercase						,							lowercase )

    # SANITY CHECKS #
    assert student.config.vocab_size == teacher.config.vocab_size
    assert student.config.hidden_size == teacher.config.hidden_size
    assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
    if args.mlm:
        assert token_probs.size(0 ) == stu_architecture_config.vocab_size

    # DISTILLER #
    torch.cuda.empty_cache()
    snake_case    :      Optional[Any]      					=    Distiller(
        params=lowercase						,							dataset=lowercase						,							token_probs=lowercase						,							student=lowercase						,							teacher=lowercase )
    distiller.train()
    logger.info("Let's go get some drinks." )


if __name__ == "__main__":
  main()





