'''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


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


def __a (	__lowerCamelCase			:						str				)	->							List[Any]:
      '''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 (	__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						List[str]				)	->							Optional[Any]:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Optional[Any]				)	->							Optional[Any]:
      '''simple docstring'''


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


def __a (	)	->							str:
      '''simple docstring'''


      lowercase_     =      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=4_000 ,		help="Checkpoint interval."				)
      lowercase_     =      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				)

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

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

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

      lowercase_     =      LmSeqsDataset(params=__lowerCamelCase ,		data=__lowerCamelCase				)
      logger.info("Data loader created."				)

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

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

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

      # TEACHER #
      lowercase_     =      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()
      lowercase_     =      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()







'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def __a (	__lowerCamelCase			:						List[Any]				)	->							Tuple:
      '''simple docstring'''


      return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						str				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      {}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            lowercase_     =      key.replace("heads.cmd.mim_head.cls.predictions" ,		"mmm_image_head"				)
            lowercase_     =      key.replace("heads.cmd.mlm_head.cls.predictions" ,		"mmm_text_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.cls" ,		"itm_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.pooler" ,		"itm_head.pooler"				)
            lowercase_     =      key.replace("heads.cmd.clip_head.logit_scale" ,		"flava.logit_scale"				)
            lowercase_     =      key.replace("heads.fairseq_mlm.cls.predictions" ,		"mlm_head"				)
            lowercase_     =      key.replace("heads.imagenet.mim_head.cls.predictions" ,		"mim_head"				)
            lowercase_     =      key.replace("mm_text_projection" ,		"flava.text_to_mm_projection"				)
            lowercase_     =      key.replace("mm_image_projection" ,		"flava.image_to_mm_projection"				)
            lowercase_     =      key.replace("image_encoder.module" ,		"flava.image_model"				)
            lowercase_     =      key.replace("text_encoder.module" ,		"flava.text_model"				)
            lowercase_     =      key.replace("mm_encoder.module.encoder.cls_token" ,		"flava.multimodal_model.cls_token"				)
            lowercase_     =      key.replace("mm_encoder.module" ,		"flava.multimodal_model"				)
            lowercase_     =      key.replace("text_projection" ,		"flava.text_projection"				)
            lowercase_     =      key.replace("image_projection" ,		"flava.image_projection"				)

            lowercase_     =      value.float()

      for key, value in codebook_state_dict.items():
            lowercase_     =      value

      return upgrade


@torch.no_grad()
def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Tuple=None				)	->							Optional[int]:
      '''simple docstring'''


      if config_path is not None:
            lowercase_     =      FlavaConfig.from_pretrained(__lowerCamelCase				)
      else:
            lowercase_     =      FlavaConfig()

      lowercase_     =      FlavaForPreTraining(__lowerCamelCase				).eval()

      lowercase_     =      convert_dalle_checkpoint(__lowerCamelCase ,		__lowerCamelCase ,		save_checkpoint=__lowerCamelCase				)

      if os.path.exists(__lowerCamelCase				):
            lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)
      else:
            lowercase_     =      torch.hub.load_state_dict_from_url(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      upgrade_state_dict(__lowerCamelCase ,		__lowerCamelCase				)
      hf_model.load_state_dict(__lowerCamelCase				)
      lowercase_     =      hf_model.state_dict()
      lowercase_     =      count_parameters(__lowerCamelCase				)
      lowercase_     =      count_parameters(__lowerCamelCase				) + count_parameters(__lowerCamelCase				)

      assert torch.allclose(__lowerCamelCase ,		__lowerCamelCase ,		atol=1e-3				)

      hf_model.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[str]						  =      argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
       parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_ : List[Any]						  =      parser.parse_args()

       convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






