


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




def   snake_case		(     UpperCamelCase__       :						Union[str, Any]    )   ->      Tuple:
      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   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						str    )   ->      List[str]:
      if args.student_type == "roberta":
            lowerCamelCase					:					List[Any]        =	False
      elif args.student_type == "gpt2":
            lowerCamelCase					:					Optional[int]        =	False




def   snake_case		(     UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						str    )   ->      Optional[int]:
      if args.student_type == "roberta":
            lowerCamelCase					:					str        =	False




def   snake_case		(     )   ->      Optional[Any]:
      lowerCamelCase					:					Optional[Any]        =	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=UpperCamelCase__			,				required=UpperCamelCase__			,				help="""The output directory (log, checkpoints, parameters, etc.)"""    )
      parser.add_argument(
          """--data_file"""			,				type=UpperCamelCase__			,				required=UpperCamelCase__			,				help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""			,				)

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

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

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

      parser.add_argument("""--log_interval"""			,				type=UpperCamelCase__			,				default=500			,				help="""Tensorboard logging interval."""    )
      parser.add_argument("""--checkpoint_interval"""			,				type=UpperCamelCase__			,				default=4000			,				help="""Checkpoint interval."""    )
      lowerCamelCase					:					Union[str, Any]        =	parser.parse_args()
      sanity_checks(UpperCamelCase__    )

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

      lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					str        =	MODEL_CLASSES[args.student_type]
      lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					str        =	MODEL_CLASSES[args.teacher_type]

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

      if args.mlm:
            logger.info(F'Loading token counts from {args.token_counts} (already pre-computed)'    )
            with open(args.token_counts			,				"""rb"""    ) as fp:
                  lowerCamelCase					:					str        =	pickle.load(UpperCamelCase__    )

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

      lowerCamelCase					:					int        =	LmSeqsDataset(params=UpperCamelCase__			,				data=UpperCamelCase__    )
      logger.info("""Data loader created."""    )

      # STUDENT #
      logger.info(F'Loading student config from {args.student_config}'    )
      lowerCamelCase					:					List[Any]        =	student_config_class.from_pretrained(args.student_config    )
      lowerCamelCase					:					Union[str, Any]        =	True

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

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

      # TEACHER #
      lowerCamelCase					:					str        =	teacher_model_class.from_pretrained(args.teacher_name			,				output_hidden_states=UpperCamelCase__    )
      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(UpperCamelCase__			,				UpperCamelCase__    )
      if args.freeze_token_type_embds:
            freeze_token_type_embeddings(UpperCamelCase__			,				UpperCamelCase__    )

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


if __name__ == "__main__":
    main()








"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase      :Dict	    =		logging.get_logger()




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						LevitConfig			,				UpperCamelCase__       :						Path			,				UpperCamelCase__       :						bool = True    )   ->      Dict:
      print(F'Converting {name}...'    )

      with torch.no_grad():
            if hidden_sizes == 128:
                  if name[-1] == "S":
                        lowerCamelCase					:					Optional[Any]        =	timm.create_model("""levit_128s"""			,				pretrained=UpperCamelCase__    )
                  else:
                        lowerCamelCase					:					Dict        =	timm.create_model("""levit_128"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 192:
                  lowerCamelCase					:					Tuple        =	timm.create_model("""levit_192"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 256:
                  lowerCamelCase					:					Optional[int]        =	timm.create_model("""levit_256"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 384:
                  lowerCamelCase					:					Dict        =	timm.create_model("""levit_384"""			,				pretrained=UpperCamelCase__    )

            from_model.eval()
            lowerCamelCase					:					Optional[Any]        =	LevitForImageClassificationWithTeacher(UpperCamelCase__    ).eval()
            lowerCamelCase					:					Tuple        =	OrderedDict()

            lowerCamelCase					:					Optional[Any]        =	from_model.state_dict()
            lowerCamelCase					:					str        =	list(from_model.state_dict().keys()    )
            lowerCamelCase					:					List[Any]        =	list(our_model.state_dict().keys()    )
            print(len(UpperCamelCase__    )			,				len(UpperCamelCase__    )    )
            for i in range(len(UpperCamelCase__    )    ):
                  lowerCamelCase					:					str        =	weights[og_keys[i]]
            our_model.load_state_dict(UpperCamelCase__    )

            lowerCamelCase					:					int        =	torch.randn((2, 3, 224, 224)    )
            lowerCamelCase					:					Any        =	from_model(UpperCamelCase__    )
            lowerCamelCase					:					List[Any]        =	our_model(UpperCamelCase__    ).logits

      assert torch.allclose(UpperCamelCase__			,				UpperCamelCase__    ), "The model logits don't match the original one."

      lowerCamelCase					:					Dict        =	name
      print(UpperCamelCase__    )

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name    )
            lowerCamelCase					:					Optional[int]        =	LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name    )

            print(F'Pushed {checkpoint_name}'    )




def   snake_case		(     UpperCamelCase__       :						Path			,				UpperCamelCase__       :						str = None			,				UpperCamelCase__       :						bool = True    )   ->      Optional[int]:
      lowerCamelCase					:					Optional[Any]        =	"""imagenet-1k-id2label.json"""
      lowerCamelCase					:					List[Any]        =	1000
      lowerCamelCase					:					Dict        =	(1, num_labels)

      lowerCamelCase					:					List[Any]        =	"""huggingface/label-files"""
      lowerCamelCase					:					Optional[int]        =	num_labels
      lowerCamelCase					:					List[str]        =	json.load(open(hf_hub_download(UpperCamelCase__			,				UpperCamelCase__			,				repo_type="""dataset"""    )			,				"""r"""    )    )
      lowerCamelCase					:					Any        =	{int(UpperCamelCase__    ): v for k, v in idalabel.items()}

      lowerCamelCase					:					List[Any]        =	idalabel
      lowerCamelCase					:					str        =	{v: k for k, v in idalabel.items()}

      lowerCamelCase					:					Tuple        =	partial(UpperCamelCase__			,				num_labels=UpperCamelCase__			,				idalabel=UpperCamelCase__			,				labelaid=UpperCamelCase__    )

      lowerCamelCase					:					Optional[int]        =	{
          """levit-128S""": 128,
          """levit-128""": 128,
          """levit-192""": 192,
          """levit-256""": 256,
          """levit-384""": 384,
      }

      lowerCamelCase					:					List[Any]        =	{
          """levit-128S""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 6, 8]			,				depths=[2, 3, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-128""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 8, 12]			,				depths=[4, 4, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-192""": ImageNetPreTrainedConfig(
              hidden_sizes=[192, 288, 384]			,				num_attention_heads=[3, 5, 6]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-256""": ImageNetPreTrainedConfig(
              hidden_sizes=[256, 384, 512]			,				num_attention_heads=[4, 6, 8]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-384""": ImageNetPreTrainedConfig(
              hidden_sizes=[384, 512, 768]			,				num_attention_heads=[6, 9, 12]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0.1			,				),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				names_to_config[model_name]			,				UpperCamelCase__			,				UpperCamelCase__    )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      return config, expected_shape


if __name__ == "__main__":
    __lowerCamelCase      :Union[str, Any]	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default=None,
        type=str,
        help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default='levit-dump-folder/',
        type=Path,
        required=False,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
    parser.add_argument(
        '--no-push_to_hub',
        dest='push_to_hub',
        action='store_false',
        help='Do not push model and image processor to the hub',
    )

    __lowerCamelCase      :List[Any]	    =		parser.parse_args()
    __lowerCamelCase      :Path	    =		args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




