


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


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



def UpperCamelCase_				(				__a      )	->   List[Any]:
      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 UpperCamelCase_				(				__a			,				__a      )	->   int:
      if args.student_type == "roberta":
            a__	:  str     =   False
      elif args.student_type == "gpt2":
            a__	:  List[str]     =   False



def UpperCamelCase_				(				__a			,				__a      )	->   Optional[int]:
      if args.student_type == "roberta":
            a__	:  Union[str, Any]     =   False



def UpperCamelCase_				(				)	->   Any:
      a__	:  List[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=__a			,				required=__a			,				help="The output directory (log, checkpoints, parameters, etc.)"      )
      parser.add_argument(
          "--data_file"			,				type=__a			,				required=__a			,				help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."			,				)

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

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

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

      parser.add_argument("--log_interval"			,				type=__a			,				default=500			,				help="Tensorboard logging interval."      )
      parser.add_argument("--checkpoint_interval"			,				type=__a			,				default=4_000			,				help="Checkpoint interval."      )
      a__	:  List[str]     =   parser.parse_args()
      sanity_checks(__a      )

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

      a__,	a__,	a__	:  Dict     =   MODEL_CLASSES[args.student_type]
      a__,	a__,	a__	:  List[Any]     =   MODEL_CLASSES[args.teacher_type]

      # TOKENIZER #
      a__	:  Optional[int]     =   teacher_tokenizer_class.from_pretrained(args.teacher_name      )
      a__	:  Union[str, Any]     =   {}
      for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
            a__	:  Optional[Any]     =   tokenizer.all_special_tokens.index(__a      )
            a__	:  Union[str, Any]     =   tokenizer.all_special_ids[idx]
      logger.info(f'''Special tokens {special_tok_ids}'''      )
      a__	:  List[str]     =   special_tok_ids
      a__	:  Optional[Any]     =   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:
            a__	:  Tuple     =   pickle.load(__a      )

      if args.mlm:
            logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)'''      )
            with open(args.token_counts			,				"rb"      ) as fp:
                  a__	:  Optional[Any]     =   pickle.load(__a      )

            a__	:  Dict     =   np.maximum(__a			,				1      ) ** -args.mlm_smoothing
            for idx in special_tok_ids.values():
                  a__	:  List[Any]     =   0.0  # do not predict special tokens
            a__	:  List[str]     =   torch.from_numpy(__a      )
      else:
            a__	:  Any     =   None

      a__	:  Any     =   LmSeqsDataset(params=__a			,				data=__a      )
      logger.info("Data loader created."      )

      # STUDENT #
      logger.info(f'''Loading student config from {args.student_config}'''      )
      a__	:  List[str]     =   student_config_class.from_pretrained(args.student_config      )
      a__	:  Optional[Any]     =   True

      if args.student_pretrained_weights is not None:
            logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''      )
            a__	:  List[Any]     =   student_model_class.from_pretrained(args.student_pretrained_weights			,				config=__a      )
      else:
            a__	:  List[Any]     =   student_model_class(__a      )

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

      # TEACHER #
      a__	:  int     =   teacher_model_class.from_pretrained(args.teacher_name			,				output_hidden_states=__a      )
      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(__a			,				__a      )
      if args.freeze_token_type_embds:
            freeze_token_type_embeddings(__a			,				__a      )

      # 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()
      a__	:  List[Any]     =   Distiller(
          params=__a			,				dataset=__a			,				token_probs=__a			,				student=__a			,				teacher=__a      )
      distiller.train()
      logger.info("Let's go get some drinks."      )


if __name__ == "__main__":
    main()



"""simple docstring"""

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[str]				   =						"sew-d"




      def __init__(				self ,							lowercase_=3_2 ,							lowercase_=7_6_8 ,							lowercase_=1_2 ,							lowercase_=1_2 ,							lowercase_=3_0_7_2 ,							lowercase_=2 ,							lowercase_=5_1_2 ,							lowercase_=2_5_6 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=("p2c", "c2p") ,							lowercase_="layer_norm" ,							lowercase_="gelu_python" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=0.0 ,							lowercase_=0.1 ,							lowercase_=0.0_2 ,							lowercase_=1E-7 ,							lowercase_=1E-5 ,							lowercase_="group" ,							lowercase_="gelu" ,							lowercase_=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2) ,							lowercase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,							lowercase_=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,							lowercase_=False ,							lowercase_=1_2_8 ,							lowercase_=1_6 ,							lowercase_=True ,							lowercase_=0.0_5 ,							lowercase_=1_0 ,							lowercase_=2 ,							lowercase_=0.0 ,							lowercase_=1_0 ,							lowercase_=0 ,							lowercase_="mean" ,							lowercase_=False ,							lowercase_=False ,							lowercase_=2_5_6 ,							lowercase_=0 ,							lowercase_=1 ,							lowercase_=2 ,							**lowercase_ ,							)    ->       Dict:
          super().__init__(**lowercase_ ,							pad_token_id=lowercase_ ,							bos_token_id=lowercase_ ,							eos_token_id=lowercase_				)
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       feat_extract_norm
          UpperCAmelCase          =       feat_extract_activation
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       conv_bias
          UpperCAmelCase          =       num_conv_pos_embeddings
          UpperCAmelCase          =       num_conv_pos_embedding_groups
          UpperCAmelCase          =       len(self.conv_dim				)
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       squeeze_factor
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       position_buckets
          UpperCAmelCase          =       share_att_key
          UpperCAmelCase          =       relative_attention
          UpperCAmelCase          =       norm_rel_ebd
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_dropout
          UpperCAmelCase          =       attention_dropout
          UpperCAmelCase          =       activation_dropout
          UpperCAmelCase          =       feat_proj_dropout
          UpperCAmelCase          =       final_dropout
          UpperCAmelCase          =       layer_norm_eps
          UpperCAmelCase          =       feature_layer_norm_eps
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       vocab_size

          if (
              (len(self.conv_stride				) != self.num_feat_extract_layers)
              or (len(self.conv_kernel				) != self.num_feat_extract_layers)
              or (len(self.conv_dim				) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  'Configuration for convolutional layers is incorrect.'
                  'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                  F"but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)"
                  F"= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`."				)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          UpperCAmelCase          =       apply_spec_augment
          UpperCAmelCase          =       mask_time_prob
          UpperCAmelCase          =       mask_time_length
          UpperCAmelCase          =       mask_time_min_masks
          UpperCAmelCase          =       mask_feature_prob
          UpperCAmelCase          =       mask_feature_length
          UpperCAmelCase          =       mask_feature_min_masks

          # ctc loss
          UpperCAmelCase          =       ctc_loss_reduction
          UpperCAmelCase          =       ctc_zero_infinity

          # sequence classification
          UpperCAmelCase          =       use_weighted_layer_sum
          UpperCAmelCase          =       classifier_proj_size




      @property
      def      a_   (				self				)    ->       Tuple:
          return functools.reduce(operator.mul ,							self.conv_stride ,							1				)



