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       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  Union[str, 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       __lowerCAmelCase      (						A_   : str						,	A_   : Tuple					)    ->  Union[str, Any]:
  if args.student_type == "roberta":
    __UpperCAmelCase						    =       False
  elif args.student_type == "gpt2":
    __UpperCAmelCase						    =       False




def       __lowerCAmelCase      (						A_   : Any						,	A_   : List[str]					)    ->  Tuple:
  if args.student_type == "roberta":
    __UpperCAmelCase						    =       False




def       __lowerCAmelCase      (						)    ->  Optional[Any]:
  __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=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=5_00						,	help="Tensorboard logging interval."					)
  parser.add_argument("--checkpoint_interval"						,	type=A_						,	default=40_00						,	help="Checkpoint interval."					)
  __UpperCAmelCase						    =       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					)

  __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(A_					)
    __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(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:
      __UpperCAmelCase						    =       pickle.load(A_					)

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

  __UpperCAmelCase						    =       LmSeqsDataset(params=A_						,	data=A_					)
  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=A_					)
  else:
    __UpperCAmelCase						    =       student_model_class(A_					)

  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=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()
  __UpperCAmelCase						    =       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()




def       __lowerCAmelCase      (						A_   : str					)    ->  bool:
  if not all(x.isalpha() for x in string					):
    raise ValueError("String must only contain alphabetic characters."					)

  __UpperCAmelCase						    =       sorted(string.lower()					)
  return len(A_					) == len(set(A_					)					)


if __name__ == "__main__":
       a_							=      input("""Enter a string """).strip()

       a_							=      is_isogram(input_str)
       print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")



