
"""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


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



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		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				(				UpperCamelCase_  ,				UpperCamelCase_				):
		if args.student_type == "roberta":
				__SCREAMING_SNAKE_CASE            = False
		elif args.student_type == "gpt2":
				__SCREAMING_SNAKE_CASE            = False



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		if args.student_type == "roberta":
				__SCREAMING_SNAKE_CASE            = False



def 						_lowerCAmelCase				(				):
		__SCREAMING_SNAKE_CASE            = 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=0.15  ,				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=0.05  ,				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=0.02  ,				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=4000  ,				help="""Checkpoint interval."""				)
		__SCREAMING_SNAKE_CASE            = 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				)

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

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

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

		__SCREAMING_SNAKE_CASE            = LmSeqsDataset(params=_lowerCamelCase  ,				data=_lowerCamelCase				)
		logger.info("""Data loader created."""				)

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

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

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

		# TEACHER #
		__SCREAMING_SNAKE_CASE            = 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()
		__SCREAMING_SNAKE_CASE            = 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'''

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)



