

"""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


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




def   _A  (  UpperCamelCase_					:	Optional[Any])     ->						str:
   '''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   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Tuple)     ->						List[Any]:
   '''simple docstring'''


   if args.student_type == "roberta":
      __lowercase								=						False
   elif args.student_type == "gpt2":
      __lowercase								=						False




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	List[str])     ->						Dict:
   '''simple docstring'''


   if args.student_type == "roberta":
      __lowercase								=						False




def   _A  (  )     ->						Tuple:
   '''simple docstring'''


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

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

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

   parser.add_argument("--temperature",      default=2.0,      type=UpperCamelCase_,      help="Temperature for the softmax temperature.")
   parser.add_argument(
       "--alpha_ce",      default=0.5,      type=UpperCamelCase_,      help="Linear weight for the distillation loss. Must be >=0.")
   parser.add_argument(
       "--alpha_mlm",      default=0.0,      type=UpperCamelCase_,      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=UpperCamelCase_,      help="Linear weight for the CLM loss. Must be >=0.")
   parser.add_argument("--alpha_mse",      default=0.0,      type=UpperCamelCase_,      help="Linear weight of the MSE loss. Must be >=0.")
   parser.add_argument(
       "--alpha_cos",      default=0.0,      type=UpperCamelCase_,      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=UpperCamelCase_,      help="Proportion of tokens for which we need to make a prediction.",      )
   parser.add_argument("--word_mask",      default=0.8,      type=UpperCamelCase_,      help="Proportion of tokens to mask out.")
   parser.add_argument("--word_keep",      default=0.1,      type=UpperCamelCase_,      help="Proportion of tokens to keep.")
   parser.add_argument("--word_rand",      default=0.1,      type=UpperCamelCase_,      help="Proportion of tokens to randomly replace.")
   parser.add_argument(
       "--mlm_smoothing",      default=0.7,      type=UpperCamelCase_,      help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).",      )
   parser.add_argument("--token_counts",      type=UpperCamelCase_,      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=UpperCamelCase_,      default=3,      help="Number of pass on the whole dataset.")
   parser.add_argument("--batch_size",      type=UpperCamelCase_,      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=UpperCamelCase_,      default=50,      help="Gradient accumulation for larger training batches.",      )
   parser.add_argument("--warmup_prop",      default=0.05,      type=UpperCamelCase_,      help="Linear warmup proportion.")
   parser.add_argument("--weight_decay",      default=0.0,      type=UpperCamelCase_,      help="Weight decay if we apply some.")
   parser.add_argument("--learning_rate",      default=5E-4,      type=UpperCamelCase_,      help="The initial learning rate for Adam.")
   parser.add_argument("--adam_epsilon",      default=1E-6,      type=UpperCamelCase_,      help="Epsilon for Adam optimizer.")
   parser.add_argument("--max_grad_norm",      default=5.0,      type=UpperCamelCase_,      help="Max gradient norm.")
   parser.add_argument("--initializer_range",      default=0.02,      type=UpperCamelCase_,      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=UpperCamelCase_,      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=UpperCamelCase_,      default=1,      help="Number of GPUs in the node.")
   parser.add_argument("--local_rank",      type=UpperCamelCase_,      default=-1,      help="Distributed training - Local rank")
   parser.add_argument("--seed",      type=UpperCamelCase_,      default=56,      help="Random seed")

   parser.add_argument("--log_interval",      type=UpperCamelCase_,      default=500,      help="Tensorboard logging interval.")
   parser.add_argument("--checkpoint_interval",      type=UpperCamelCase_,      default=4000,      help="Checkpoint interval.")
   __lowercase								=						parser.parse_args()
   sanity_checks(UpperCamelCase_)

   # ARGS #
   init_gpu_params(UpperCamelCase_)
   set_seed(UpperCamelCase_)
   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(UpperCamelCase_),      UpperCamelCase_,      indent=4)
      git_log(args.dump_path)

   __lowercase		,__lowercase		,__lowercase								=						MODEL_CLASSES[args.student_type]
   __lowercase		,__lowercase		,__lowercase								=						MODEL_CLASSES[args.teacher_type]

   # TOKENIZER #
   __lowercase								=						teacher_tokenizer_class.from_pretrained(args.teacher_name)
   __lowercase								=						{}
   for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
      __lowercase								=						tokenizer.all_special_tokens.index(UpperCamelCase_)
      __lowercase								=						tokenizer.all_special_ids[idx]
   logger.info(F"""Special tokens {special_tok_ids}""")
   __lowercase								=						special_tok_ids
   __lowercase								=						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:
      __lowercase								=						pickle.load(UpperCamelCase_)

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

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

   __lowercase								=						LmSeqsDataset(params=UpperCamelCase_,      data=UpperCamelCase_)
   logger.info("Data loader created.")

   # STUDENT #
   logger.info(F"""Loading student config from {args.student_config}""")
   __lowercase								=						student_config_class.from_pretrained(args.student_config)
   __lowercase								=						True

   if args.student_pretrained_weights is not None:
      logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}""")
      __lowercase								=						student_model_class.from_pretrained(args.student_pretrained_weights,      config=UpperCamelCase_)
   else:
      __lowercase								=						student_model_class(UpperCamelCase_)

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

   # TEACHER #
   __lowercase								=						teacher_model_class.from_pretrained(args.teacher_name,      output_hidden_states=UpperCamelCase_)
   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(UpperCamelCase_,      UpperCamelCase_)
   if args.freeze_token_type_embds:
      freeze_token_type_embeddings(UpperCamelCase_,      UpperCamelCase_)

   # 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()
   __lowercase								=						Distiller(
       params=UpperCamelCase_,      dataset=UpperCamelCase_,      token_probs=UpperCamelCase_,      student=UpperCamelCase_,      teacher=UpperCamelCase_)
   distiller.train()
   logger.info("Let's go get some drinks.")


if __name__ == "__main__":
   main()







"""simple docstring"""





from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor




