


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






def 					UpperCamelCase   (      __magic_name__	:				List[str]	)  ->    Union[str, 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 					UpperCamelCase   (      __magic_name__	:				int       ,       __magic_name__	:				str	)  ->    Optional[int]:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				Tuple       ,       __magic_name__	:				Tuple	)  ->    Tuple:
		"""simple docstring"""




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






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

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

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

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

		parser.add_argument("""--log_interval"""       ,       type=__magic_name__       ,       default=500       ,       help="""Tensorboard logging interval."""	)
		parser.add_argument("""--checkpoint_interval"""       ,       type=__magic_name__       ,       default=4000       ,       help="""Checkpoint interval."""	)
		lowercase__	     =						parser.parse_args()
		sanity_checks(__magic_name__	)

		# ARGS #
		init_gpu_params(__magic_name__	)
		set_seed(__magic_name__	)
		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(__magic_name__	)       ,       __magic_name__       ,       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(__magic_name__	)
				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(__magic_name__	)

		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(__magic_name__	)

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

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

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

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


if __name__ == "__main__":
	main()





from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					42
					A__     =					None
					A__     =					None






def 					UpperCamelCase   (      )  ->    Node | None:
		"""simple docstring"""




		lowercase__	     =						Node(1	)
		lowercase__	     =						Node(2	)
		lowercase__	     =						Node(3	)
		lowercase__	     =						Node(4	)
		lowercase__	     =						Node(5	)
		return tree






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [root.data, *preorder(root.left	), *preorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return postorder(root.left	) + postorder(root.right	) + [root.data] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [*inorder(root.left	), root.data, *inorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    int:
		"""simple docstring"""




		return (max(height(root.left	)       ,       height(root.right	)	) + 1) if root else 0






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		if root is None:
				return output

		lowercase__	     =						deque([root]	)

		while process_queue:
				lowercase__	     =						process_queue.popleft()
				output.append(node.data	)

				if node.left:
						process_queue.append(node.left	)
				if node.right:
						process_queue.append(node.right	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if not root:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.left       ,       level - 1	)
						populate_output(root.right       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if root is None:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.right       ,       level - 1	)
						populate_output(root.left       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None] | list[Any]:
		"""simple docstring"""




		if root is None:
				return []

		lowercase__	     =						[]

		lowercase__	     =						0
		lowercase__	     =						height(__magic_name__	)

		for h in range(1       ,       height_tree + 1	):
				if not flag:
						output.append(get_nodes_from_left_to_right(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						1
				else:
						output.append(get_nodes_from_right_to_left(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						0

		return output






def 					UpperCamelCase   (      )  ->    None:  # Main function for testing.
		"""simple docstring"""




		lowercase__	     =						make_tree()
		print(f'''In-order Traversal: {inorder(__magic_name__	)}'''	)
		print(f'''Pre-order Traversal: {preorder(__magic_name__	)}'''	)
		print(f'''Post-order Traversal: {postorder(__magic_name__	)}'''       ,       """\n"""	)

		print(f'''Height of Tree: {height(__magic_name__	)}'''       ,       """\n"""	)

		print("""Complete Level Order Traversal: """	)
		print(level_order(__magic_name__	)       ,       """\n"""	)

		print("""Level-wise order Traversal: """	)

		for level in range(1       ,       height(__magic_name__	) + 1	):
				print(f'''Level {level}:'''       ,       get_nodes_from_left_to_right(__magic_name__       ,       level=__magic_name__	)	)

		print("""\nZigZag order Traversal: """	)
		print(zigzag(__magic_name__	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

