



'''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


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






def 		_lowerCAmelCase				(_lowercase			):

       """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 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

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






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

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






def 		_lowerCAmelCase				():

       """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=lowercase__   ,					required=lowercase__   ,					help="The output directory (log, checkpoints, parameters, etc.)"			)
       parser.add_argument(
           "--data_file"   ,					type=lowercase__   ,					required=lowercase__   ,					help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."   ,					)

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

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

       parser.add_argument("--temperature"   ,					default=2.0   ,					type=lowercase__   ,					help="Temperature for the softmax temperature."			)
       parser.add_argument(
           "--alpha_ce"   ,					default=0.5   ,					type=lowercase__   ,					help="Linear weight for the distillation loss. Must be >=0."			)
       parser.add_argument(
           "--alpha_mlm"   ,					default=0.0   ,					type=lowercase__   ,					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=lowercase__   ,					help="Linear weight for the CLM loss. Must be >=0."			)
       parser.add_argument("--alpha_mse"   ,					default=0.0   ,					type=lowercase__   ,					help="Linear weight of the MSE loss. Must be >=0."			)
       parser.add_argument(
           "--alpha_cos"   ,					default=0.0   ,					type=lowercase__   ,					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=lowercase__   ,					help="Proportion of tokens for which we need to make a prediction."   ,					)
       parser.add_argument("--word_mask"   ,					default=0.8   ,					type=lowercase__   ,					help="Proportion of tokens to mask out."			)
       parser.add_argument("--word_keep"   ,					default=0.1   ,					type=lowercase__   ,					help="Proportion of tokens to keep."			)
       parser.add_argument("--word_rand"   ,					default=0.1   ,					type=lowercase__   ,					help="Proportion of tokens to randomly replace."			)
       parser.add_argument(
           "--mlm_smoothing"   ,					default=0.7   ,					type=lowercase__   ,					help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."   ,					)
       parser.add_argument("--token_counts"   ,					type=lowercase__   ,					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=lowercase__   ,					default=3   ,					help="Number of pass on the whole dataset."			)
       parser.add_argument("--batch_size"   ,					type=lowercase__   ,					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=lowercase__   ,					default=50   ,					help="Gradient accumulation for larger training batches."   ,					)
       parser.add_argument("--warmup_prop"   ,					default=0.05   ,					type=lowercase__   ,					help="Linear warmup proportion."			)
       parser.add_argument("--weight_decay"   ,					default=0.0   ,					type=lowercase__   ,					help="Weight decay if we apply some."			)
       parser.add_argument("--learning_rate"   ,					default=5e-4   ,					type=lowercase__   ,					help="The initial learning rate for Adam."			)
       parser.add_argument("--adam_epsilon"   ,					default=1e-6   ,					type=lowercase__   ,					help="Epsilon for Adam optimizer."			)
       parser.add_argument("--max_grad_norm"   ,					default=5.0   ,					type=lowercase__   ,					help="Max gradient norm."			)
       parser.add_argument("--initializer_range"   ,					default=0.02   ,					type=lowercase__   ,					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=lowercase__   ,					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=lowercase__   ,					default=1   ,					help="Number of GPUs in the node."			)
       parser.add_argument("--local_rank"   ,					type=lowercase__   ,					default=-1   ,					help="Distributed training - Local rank"			)
       parser.add_argument("--seed"   ,					type=lowercase__   ,					default=56   ,					help="Random seed"			)

       parser.add_argument("--log_interval"   ,					type=lowercase__   ,					default=5_00   ,					help="Tensorboard logging interval."			)
       parser.add_argument("--checkpoint_interval"   ,					type=lowercase__   ,					default=40_00   ,					help="Checkpoint interval."			)
       a__								= parser.parse_args()
       sanity_checks(lowercase__			)

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

       a__								= MODEL_CLASSES[args.student_type]
       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(lowercase__			)
              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(lowercase__			)

       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(lowercase__			)

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

       a__								= LmSeqsDataset(params=lowercase__   ,					data=lowercase__			)
       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=lowercase__			)
       else:
              a__								= student_model_class(lowercase__			)

       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=lowercase__			)
       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(lowercase__   ,					lowercase__			)
       if args.freeze_token_type_embds:
              freeze_token_type_embeddings(lowercase__   ,					lowercase__			)

       # 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=lowercase__   ,					dataset=lowercase__   ,					token_probs=lowercase__   ,					student=lowercase__   ,					teacher=lowercase__			)
       distiller.train()
       logger.info("Let\'s go get some drinks."			)


if __name__ == "__main__":
    main()






"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
			from ...processing_utils import ProcessorMixin
			from ...utils import TensorType


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """layoutlmv3"""
			def __init__(    self    , lowercase=50265    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-5    , lowercase=1    , lowercase=0    , lowercase=2    , lowercase=1024    , lowercase=128    , lowercase=128    , lowercase=True    , lowercase=32    , lowercase=128    , lowercase=64    , lowercase=256    , lowercase=True    , lowercase=True    , lowercase=True    , lowercase=224    , lowercase=3    , lowercase=16    , lowercase=None    , **lowercase    , ):
							super().__init__(
							    vocab_size=lowercase    , hidden_size=lowercase    , num_hidden_layers=lowercase    , num_attention_heads=lowercase    , intermediate_size=lowercase    , hidden_act=lowercase    , hidden_dropout_prob=lowercase    , attention_probs_dropout_prob=lowercase    , max_position_embeddings=lowercase    , type_vocab_size=lowercase    , initializer_range=lowercase    , layer_norm_eps=lowercase    , pad_token_id=lowercase    , bos_token_id=lowercase    , eos_token_id=lowercase    , **lowercase    , )
							_lowerCamelCase	:     List[str]       =       max_ad_position_embeddings
							_lowerCamelCase	:     Optional[Any]       =       coordinate_size
							_lowerCamelCase	:     int       =       shape_size
							_lowerCamelCase	:     Optional[Any]       =       has_relative_attention_bias
							_lowerCamelCase	:     Dict       =       rel_pos_bins
							_lowerCamelCase	:     Tuple       =       max_rel_pos
							_lowerCamelCase	:     int       =       has_spatial_attention_bias
							_lowerCamelCase	:     Optional[int]       =       rel_ad_pos_bins
							_lowerCamelCase	:     List[Any]       =       max_rel_ad_pos
							_lowerCamelCase	:     Any       =       text_embed
							_lowerCamelCase	:     List[Any]       =       visual_embed
							_lowerCamelCase	:     str       =       input_size
							_lowerCamelCase	:     Optional[Any]       =       num_channels
							_lowerCamelCase	:     Tuple       =       patch_size
							_lowerCamelCase	:     Optional[int]       =       classifier_dropout






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     version.parse("""1.12"""		)
			@property
			def    A_       (    self    ):
							# The order of inputs is different for question answering and sequence classification
							if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]    )
							else:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
											    ]    )
			@property
			def    A_       (    self    ):
							return 1E-5
			@property
			def    A_       (    self    ):
							return 12






			def    A_       (    self    , lowercase    , lowercase = -1    , lowercase = -1    , lowercase = False    , lowercase = None    , lowercase = 3    , lowercase = 40    , lowercase = 40    , ):
							setattr(processor.image_processor    , 'apply_ocr'    , lowercase    )

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							_lowerCamelCase	:     Any       =       compute_effective_axis_dimension(
							    lowercase    , fixed_dimension=OnnxConfig.default_fixed_batch    , num_token_to_add=0    )
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							_lowerCamelCase	:     Tuple       =       processor.tokenizer.num_special_tokens_to_add(lowercase    )
							_lowerCamelCase	:     Any       =       compute_effective_axis_dimension(
							    lowercase    , fixed_dimension=OnnxConfig.default_fixed_sequence    , num_token_to_add=lowercase    )
							# Generate dummy inputs according to compute batch and sequence
							_lowerCamelCase	:     List[Any]       =       [[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							_lowerCamelCase	:     List[str]       =       [[[48, 84, 73, 128]]] * batch_size

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
							_lowerCamelCase	:     Tuple       =       self._generate_dummy_images(lowercase    , lowercase    , lowercase    , lowercase    )

							_lowerCamelCase	:     str       =       dict(
							    processor(
							        lowercase    , text=lowercase    , boxes=lowercase    , return_tensors=lowercase    , )    )

							return inputs