



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       (					__lowerCAmelCase      )   ->					int:


				"""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       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Optional[Any]:


				"""simple docstring"""

				if args.student_type == "roberta":
								snake_case__	:      List[Any]     		=	False
				elif args.student_type == "gpt2":
								snake_case__	:      str     		=	False



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Dict:


				"""simple docstring"""

				if args.student_type == "roberta":
								snake_case__	:      Union[str, Any]     		=	False



def 			_lowerCAmelCase       (					)   ->					List[str]:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	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.'''      )
				snake_case__	:      Any     		=	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      )

				snake_case__	:      str     		=	MODEL_CLASSES[args.student_type]
				snake_case__	:      List[Any]     		=	MODEL_CLASSES[args.teacher_type]

				# TOKENIZER #
				snake_case__	:      Tuple     		=	teacher_tokenizer_class.from_pretrained(args.teacher_name      )
				snake_case__	:      List[str]     		=	{}
				for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
								snake_case__	:      Dict     		=	tokenizer.all_special_tokens.index(__A      )
								snake_case__	:      str     		=	tokenizer.all_special_ids[idx]
				logger.info(f"""Special tokens {special_tok_ids}"""      )
				snake_case__	:      List[str]     		=	special_tok_ids
				snake_case__	:      Optional[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:
								snake_case__	:      int     		=	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:
												snake_case__	:      Dict     		=	pickle.load(__A      )

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

				snake_case__	:      List[Any]     		=	LmSeqsDataset(params=__A  ,  data=__A      )
				logger.info('''Data loader created.'''      )

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

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

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

				# TEACHER #
				snake_case__	:      int     		=	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()
				snake_case__	:      List[str]     		=	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()





import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_      : Optional[int]  					=       {
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase_      : List[str]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Any        =						'''mask2former'''
      snake_case__				:     Any        =						['''swin''']
      snake_case__				:     str        =						{'''hidden_size''': '''hidden_dim'''}
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict] = None     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_2_4     ,							SCREAMING_SNAKE_CASE__				:       str = "relu"     ,							SCREAMING_SNAKE_CASE__				:       int = 6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0     ,							SCREAMING_SNAKE_CASE__				:       int = 8     ,							SCREAMING_SNAKE_CASE__				:       float = 0.0     ,							SCREAMING_SNAKE_CASE__				:       int = 2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       int = 4     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.1     ,							SCREAMING_SNAKE_CASE__				:       float = 2.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       int = 1_2_5_4_4     ,							SCREAMING_SNAKE_CASE__				:       float = 3.0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.75     ,							SCREAMING_SNAKE_CASE__				:       float = 0.02     ,							SCREAMING_SNAKE_CASE__				:       float = 1.0     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       List[int] = [4, 8, 1_6, 3_2]     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							**SCREAMING_SNAKE_CASE__				:       int     ,							)		->			List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'     )
                    a_					:		Dict							=  CONFIG_MAPPING['swin'](
                        image_size=2_2_4     ,							in_channels=3     ,							patch_size=4     ,							embed_dim=9_6     ,							depths=[2, 2, 1_8, 2]     ,							num_heads=[3, 6, 1_2, 2_4]     ,							window_size=7     ,							drop_path_rate=0.3     ,							use_absolute_embeddings=SCREAMING_SNAKE_CASE__     ,							out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,							)

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Any							=  backbone_config.pop('model_type'     )
                    a_					:		Optional[Any]							=  CONFIG_MAPPING[backbone_model_type]
                    a_					:		List[str]							=  config_class.from_dict(SCREAMING_SNAKE_CASE__     )

             # verify that the backbone is supported
             if backbone_config.model_type not in self.backbones_supported:
                    logger.warning_once(
                        F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
                        F"""Supported model types: {",".join(self.backbones_supported     )}"""     )

             a_					:		Dict							=  backbone_config
             a_					:		List[str]							=  feature_size
             a_					:		List[str]							=  mask_feature_size
             a_					:		int							=  hidden_dim
             a_					:		Dict							=  encoder_feedforward_dim
             a_					:		str							=  activation_function
             a_					:		List[str]							=  encoder_layers
             a_					:		List[str]							=  decoder_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		str							=  dropout
             a_					:		Tuple							=  dim_feedforward
             a_					:		List[str]							=  pre_norm
             a_					:		Optional[int]							=  enforce_input_projection
             a_					:		Any							=  common_stride
             a_					:		Optional[int]							=  ignore_value
             a_					:		int							=  num_queries
             a_					:		Tuple							=  no_object_weight
             a_					:		Dict							=  class_weight
             a_					:		Optional[int]							=  mask_weight
             a_					:		Optional[int]							=  dice_weight
             a_					:		str							=  train_num_points
             a_					:		List[str]							=  oversample_ratio
             a_					:		List[Any]							=  importance_sample_ratio
             a_					:		Any							=  init_std
             a_					:		Union[str, Any]							=  init_xavier_std
             a_					:		Union[str, Any]							=  use_auxiliary_loss
             a_					:		Dict							=  feature_strides
             a_					:		List[str]							=  output_auxiliary_logits
             a_					:		Dict							=  decoder_layers

             super().__init__(**SCREAMING_SNAKE_CASE__     )
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       str     ,							SCREAMING_SNAKE_CASE__				:       PretrainedConfig     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			List[Any]:
             return cls(
                 backbone_config=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict[str, any]:
             a_					:		Optional[int]							=  copy.deepcopy(self.__dict__     )
             a_					:		List[Any]							=  self.backbone_config.to_dict()
             a_					:		Optional[Any]							=  self.__class__.model_type
             return output


