'''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


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





def       lowerCAmelCase_							(   snake_case_			:					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       lowerCAmelCase_							(   snake_case_			:					Optional[Any]   ,       snake_case_			:					Union[str, Any]     )       -> Optional[int]:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					Optional[int]   ,       snake_case_			:					List[Any]     )       -> Tuple:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   )       -> Optional[Any]:


     '''simple docstring'''




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

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

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

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

     parser.add_argument("--log_interval"   ,       type=snake_case_   ,       default=5_00   ,       help="Tensorboard logging interval."     )
     parser.add_argument("--checkpoint_interval"   ,       type=snake_case_   ,       default=40_00   ,       help="Checkpoint interval."     )
     UpperCAmelCase_			       =  parser.parse_args()
     sanity_checks(snake_case_     )

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

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

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

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

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

     UpperCAmelCase_			       =  LmSeqsDataset(params=snake_case_   ,       data=snake_case_     )
     logger.info("Data loader created."     )

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

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

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

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

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


if __name__ == "__main__":
    main()








"""simple docstring"""




import math
from typing import Optional

import numpy as np

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


__magic_name__						: List[str]    =				logging.get_logger(__name__)

__magic_name__						: Tuple    =				{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''encodec'''




							def __init__(     self:    Union[str, Any]     ,							A_:    Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,							A_:    Any=2_40_00     ,							A_:    List[Any]=1     ,							A_:    Optional[Any]=False     ,							A_:    Optional[int]=None     ,							A_:    int=None     ,							A_:    List[str]=1_28     ,							A_:    Union[str, Any]=32     ,							A_:    List[str]=1     ,							A_:    Dict=[8, 5, 4, 2]     ,							A_:    List[Any]="weight_norm"     ,							A_:    Any=7     ,							A_:    List[Any]=7     ,							A_:    Tuple=3     ,							A_:    List[str]=2     ,							A_:    Optional[Any]=True     ,							A_:    Optional[int]="reflect"     ,							A_:    Dict=2     ,							A_:    Union[str, Any]=2     ,							A_:    Union[str, Any]=1.0     ,							A_:    List[str]=10_24     ,							A_:    str=None     ,							A_:    List[str]=True     ,							**A_:    int     ,							):
										__lowerCamelCase          =      target_bandwidths
										__lowerCamelCase          =      sampling_rate
										__lowerCamelCase          =      audio_channels
										__lowerCamelCase          =      normalize
										__lowerCamelCase          =      chunk_length_s
										__lowerCamelCase          =      overlap
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_filters
										__lowerCamelCase          =      num_residual_layers
										__lowerCamelCase          =      upsampling_ratios
										__lowerCamelCase          =      norm_type
										__lowerCamelCase          =      kernel_size
										__lowerCamelCase          =      last_kernel_size
										__lowerCamelCase          =      residual_kernel_size
										__lowerCamelCase          =      dilation_growth_rate
										__lowerCamelCase          =      use_causal_conv
										__lowerCamelCase          =      pad_mode
										__lowerCamelCase          =      compress
										__lowerCamelCase          =      num_lstm_layers
										__lowerCamelCase          =      trim_right_ratio
										__lowerCamelCase          =      codebook_size
										__lowerCamelCase          =      codebook_dim if codebook_dim is not None else hidden_size
										__lowerCamelCase          =      use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
													raise ValueError(
													    f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'   )

										super().__init__(**A_   )




							@property
							def   __a   (     self:    Union[str, Any]   ):
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate   )




							@property
							def   __a   (     self:    List[Any]   ):
										if self.chunk_length_s is None or self.overlap is None:
													return None
										else:
													return max(1     ,							int((1.0 - self.overlap) * self.chunk_length   )   )




							@property
							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )




							@property
							def   __a   (     self:    List[Any]   ):
										return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )




