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



def   __A		(			_A					):



 """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		(			_A							,				_A					):



 """simple docstring"""


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



def   __A		(			_A							,				_A					):



 """simple docstring"""


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



def   __A		(			):



 """simple docstring"""


 __a       =  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=4000							,				help="Checkpoint interval."					)
 __a       =  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       =  MODEL_CLASSES[args.student_type]
 __a       ,     __a       ,     __a       =  MODEL_CLASSES[args.teacher_type]

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

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

 __a       =  LmSeqsDataset(params=_A							,				data=_A					)
 logger.info("Data loader created."					)

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

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

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

 # TEACHER #
 __a       =  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       =  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()



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   def _delete(__SCREAMING_SNAKE_CASE :      TrieNode     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int       ) -> bool:
    if index == len(__SCREAMING_SNAKE_CASE       ):
     # If word does not exist
     if not curr.is_leaf:
      return False
     __a       =  False
     return len(curr.nodes       ) == 0
    __a       =  word[index]
    __a       =  curr.nodes.get(__SCREAMING_SNAKE_CASE       )
    # If char not in current trie node
    if not char_node:
     return False
    # Flag to check if node can be deleted
    __a       =  _delete(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							index + 1       )
    if delete_curr:
     del curr.nodes[char]
     return len(curr.nodes       ) == 0
    return delete_curr

   _delete(self     ,							__SCREAMING_SNAKE_CASE     ,							0       )



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

 for key, value in node.nodes.items():
  print_words(_A							,				word + key					)



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  TrieNode()
 root.insert_many(_A					)
 # print_words(root, "")
 assert all(root.find(_A					) for word in words					)
 assert root.find("banana"					)
 assert not root.find("bandanas"					)
 assert not root.find("apps"					)
 assert root.find("apple"					)
 assert root.find("all"					)
 root.delete("all"					)
 assert not root.find("all"					)
 root.delete("banana"					)
 assert not root.find("banana"					)
 assert root.find("bananas"					)
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


