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

def lowercase_						(     _snake_case		):
							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 lowercase_						(     _snake_case     ,_snake_case		):
							if args.student_type == "roberta":
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     False
							elif args.student_type == "gpt2":
														SCREAMING_SNAKE_CASE__		: int     =     False

def lowercase_						(     _snake_case     ,_snake_case		):
							if args.student_type == "roberta":
														SCREAMING_SNAKE_CASE__		: List[Any]     =     False

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

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

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

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

							parser.add_argument("""--log_interval"""     ,type=_snake_case     ,default=500     ,help="""Tensorboard logging interval."""		)
							parser.add_argument("""--checkpoint_interval"""     ,type=_snake_case     ,default=4_000     ,help="""Checkpoint interval."""		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     parser.parse_args()
							sanity_checks(_snake_case		)

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

							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[str]     =     MODEL_CLASSES[args.student_type]
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: int     =     MODEL_CLASSES[args.teacher_type]

							# TOKENIZER #
							SCREAMING_SNAKE_CASE__		: Dict     =     teacher_tokenizer_class.from_pretrained(args.teacher_name		)
							SCREAMING_SNAKE_CASE__		: str     =     {}
							for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
														SCREAMING_SNAKE_CASE__		: int     =     tokenizer.all_special_tokens.index(_snake_case		)
														SCREAMING_SNAKE_CASE__		: Dict     =     tokenizer.all_special_ids[idx]
							logger.info(f'''Special tokens {special_tok_ids}'''		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     special_tok_ids
							SCREAMING_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:
														SCREAMING_SNAKE_CASE__		: str     =     pickle.load(_snake_case		)

							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__		: int     =     pickle.load(_snake_case		)

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

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

							# STUDENT #
							logger.info(f'''Loading student config from {args.student_config}'''		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     student_config_class.from_pretrained(args.student_config		)
							SCREAMING_SNAKE_CASE__		: str     =     True

							if args.student_pretrained_weights is not None:
														logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}'''		)
														SCREAMING_SNAKE_CASE__		: List[str]     =     student_model_class.from_pretrained(args.student_pretrained_weights     ,config=_snake_case		)
							else:
														SCREAMING_SNAKE_CASE__		: Any     =     student_model_class(_snake_case		)

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

							# TEACHER #
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     teacher_model_class.from_pretrained(args.teacher_name     ,output_hidden_states=_snake_case		)
							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(_snake_case     ,_snake_case		)
							if args.freeze_token_type_embds:
														freeze_token_type_embeddings(_snake_case     ,_snake_case		)

							# 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__		: Optional[int]     =     Distiller(
							    params=_snake_case     ,dataset=_snake_case     ,token_probs=_snake_case     ,student=_snake_case     ,teacher=_snake_case		)
							distiller.train()
							logger.info("""Let's go get some drinks."""		)


if __name__ == "__main__":
					main()






"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def lowercase_						(     _snake_case     ,_snake_case=7		):
							SCREAMING_SNAKE_CASE__		: Dict     =     None
							if token is not None:
														SCREAMING_SNAKE_CASE__		: str     =     {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

							# The id of a workflow (not of a workflow run)
							SCREAMING_SNAKE_CASE__		: List[str]     =     """636036"""

							SCREAMING_SNAKE_CASE__		: Any     =     f'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
							# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
							url += f'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''

							SCREAMING_SNAKE_CASE__		: str     =     requests.get(_snake_case     ,headers=_snake_case		).json()

							return result["workflow_runs"]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     get_daily_ci_runs(_snake_case		)
							SCREAMING_SNAKE_CASE__		: int     =     None
							for workflow_run in workflow_runs:
														if workflow_run["status"] == "completed":
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     workflow_run["""id"""]
																					break

							return workflow_run_id

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     get_last_daily_ci_runs(_snake_case		)
							if workflow_run_id is not None:
														SCREAMING_SNAKE_CASE__		: Tuple     =     get_artifacts_links(worflow_run_id=_snake_case     ,token=_snake_case		)
														for artifact_name in artifact_names:
																					if artifact_name in artifacts_links:
																												SCREAMING_SNAKE_CASE__		: Tuple     =     artifacts_links[artifact_name]
																												download_artifact(
																												    artifact_name=_snake_case     ,artifact_url=_snake_case     ,output_dir=_snake_case     ,token=_snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							get_last_daily_ci_artifacts(_snake_case     ,_snake_case     ,_snake_case		)

							SCREAMING_SNAKE_CASE__		: List[str]     =     {}
							for artifact_name in artifact_names:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     os.path.join(_snake_case     ,f'''{artifact_name}.zip'''		)
														if os.path.isfile(_snake_case		):
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     {}
																					with zipfile.ZipFile(_snake_case		) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(_snake_case		):
																																										# read the file
																																										with z.open(_snake_case		) as f:
																																																	SCREAMING_SNAKE_CASE__		: List[Any]     =     f.read().decode("""UTF-8"""		)

							return results




