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    __lowercase     (						lowerCamelCase					:     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    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     List[Any]	):
 if args.student_type == "roberta":
  UpperCamelCase_					:						Any =					False
 elif args.student_type == "gpt2":
  UpperCamelCase_					:						str =					False





def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     int	):
 if args.student_type == "roberta":
  UpperCamelCase_					:						int =					False





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

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

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

 parser.add_argument('--temperature'  ,  default=2.0  ,  type=lowerCamelCase  ,  help='Temperature for the softmax temperature.'	)
 parser.add_argument(
     '--alpha_ce'  ,  default=0.5  ,  type=lowerCamelCase  ,  help='Linear weight for the distillation loss. Must be >=0.'	)
 parser.add_argument(
     '--alpha_mlm'  ,  default=0.0  ,  type=lowerCamelCase  ,  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=lowerCamelCase  ,  help='Linear weight for the CLM loss. Must be >=0.'	)
 parser.add_argument('--alpha_mse'  ,  default=0.0  ,  type=lowerCamelCase  ,  help='Linear weight of the MSE loss. Must be >=0.'	)
 parser.add_argument(
     '--alpha_cos'  ,  default=0.0  ,  type=lowerCamelCase  ,  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=0.1_5  ,  type=lowerCamelCase  ,  help='Proportion of tokens for which we need to make a prediction.'  ,  )
 parser.add_argument('--word_mask'  ,  default=0.8  ,  type=lowerCamelCase  ,  help='Proportion of tokens to mask out.'	)
 parser.add_argument('--word_keep'  ,  default=0.1  ,  type=lowerCamelCase  ,  help='Proportion of tokens to keep.'	)
 parser.add_argument('--word_rand'  ,  default=0.1  ,  type=lowerCamelCase  ,  help='Proportion of tokens to randomly replace.'	)
 parser.add_argument(
     '--mlm_smoothing'  ,  default=0.7  ,  type=lowerCamelCase  ,  help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'  ,  )
 parser.add_argument('--token_counts'  ,  type=lowerCamelCase  ,  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=lowerCamelCase  ,  default=3  ,  help='Number of pass on the whole dataset.'	)
 parser.add_argument('--batch_size'  ,  type=lowerCamelCase  ,  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=lowerCamelCase  ,  default=50  ,  help='Gradient accumulation for larger training batches.'  ,  )
 parser.add_argument('--warmup_prop'  ,  default=0.0_5  ,  type=lowerCamelCase  ,  help='Linear warmup proportion.'	)
 parser.add_argument('--weight_decay'  ,  default=0.0  ,  type=lowerCamelCase  ,  help='Weight decay if we apply some.'	)
 parser.add_argument('--learning_rate'  ,  default=5e-4  ,  type=lowerCamelCase  ,  help='The initial learning rate for Adam.'	)
 parser.add_argument('--adam_epsilon'  ,  default=1e-6  ,  type=lowerCamelCase  ,  help='Epsilon for Adam optimizer.'	)
 parser.add_argument('--max_grad_norm'  ,  default=5.0  ,  type=lowerCamelCase  ,  help='Max gradient norm.'	)
 parser.add_argument('--initializer_range'  ,  default=0.0_2  ,  type=lowerCamelCase  ,  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=lowerCamelCase  ,  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=lowerCamelCase  ,  default=1  ,  help='Number of GPUs in the node.'	)
 parser.add_argument('--local_rank'  ,  type=lowerCamelCase  ,  default=-1  ,  help='Distributed training - Local rank'	)
 parser.add_argument('--seed'  ,  type=lowerCamelCase  ,  default=56  ,  help='Random seed'	)

 parser.add_argument('--log_interval'  ,  type=lowerCamelCase  ,  default=500  ,  help='Tensorboard logging interval.'	)
 parser.add_argument('--checkpoint_interval'  ,  type=lowerCamelCase  ,  default=4000  ,  help='Checkpoint interval.'	)
 UpperCamelCase_					:						int =					parser.parse_args()
 sanity_checks(lowerCamelCase	)

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

 UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Optional[Any] =					MODEL_CLASSES[args.student_type]
 UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Any =					MODEL_CLASSES[args.teacher_type]

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

 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_					:						Any =					pickle.load(lowerCamelCase	)

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

 UpperCamelCase_					:						str =					LmSeqsDataset(params=lowerCamelCase  ,  data=lowerCamelCase	)
 logger.info('Data loader created.'	)

 # STUDENT #
 logger.info(F"Loading student config from {args.student_config}"	)
 UpperCamelCase_					:						Optional[int] =					student_config_class.from_pretrained(args.student_config	)
 UpperCamelCase_					:						List[Any] =					True

 if args.student_pretrained_weights is not None:
  logger.info(F"Loading pretrained weights from {args.student_pretrained_weights}"	)
  UpperCamelCase_					:						Optional[Any] =					student_model_class.from_pretrained(args.student_pretrained_weights  ,  config=lowerCamelCase	)
 else:
  UpperCamelCase_					:						List[str] =					student_model_class(lowerCamelCase	)

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

 # TEACHER #
 UpperCamelCase_					:						Optional[int] =					teacher_model_class.from_pretrained(args.teacher_name  ,  output_hidden_states=lowerCamelCase	)
 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(lowerCamelCase  ,  lowerCamelCase	)
 if args.freeze_token_type_embds:
  freeze_token_type_embeddings(lowerCamelCase  ,  lowerCamelCase	)

 # 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_					:						Optional[Any] =					Distiller(
     params=lowerCamelCase  ,  dataset=lowerCamelCase  ,  token_probs=lowerCamelCase  ,  student=lowerCamelCase  ,  teacher=lowerCamelCase	)
 distiller.train()
 logger.info('Let\'s go get some drinks.'	)


if __name__ == "__main__":
   main()




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_    					=       {
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['EfficientFormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'EfficientFormerForImageClassification',
       'EfficientFormerForImageClassificationWithTeacher',
       'EfficientFormerModel',
       'EfficientFormerPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFEfficientFormerForImageClassification',
       'TFEfficientFormerForImageClassificationWithTeacher',
       'TFEfficientFormerModel',
       'TFEfficientFormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

   a_    					=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



