





"""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


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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[Any]:
				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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if args.student_type == "roberta":
								SCREAMING_SNAKE_CASE__		  =				False
				elif args.student_type == "gpt2":
								SCREAMING_SNAKE_CASE__		  =				False




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				if args.student_type == "roberta":
								SCREAMING_SNAKE_CASE__		  =				False




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

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

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

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

				parser.add_argument("--log_interval"  ,					type=__UpperCAmelCase  ,					default=500  ,					help="Tensorboard logging interval."       )
				parser.add_argument("--checkpoint_interval"  ,					type=__UpperCAmelCase  ,					default=4_000  ,					help="Checkpoint interval."       )
				SCREAMING_SNAKE_CASE__		  =				parser.parse_args()
				sanity_checks(__UpperCAmelCase       )

				# ARGS #
				init_gpu_params(__UpperCAmelCase       )
				set_seed(__UpperCAmelCase       )
				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(__UpperCAmelCase       )  ,					__UpperCAmelCase  ,					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(__UpperCAmelCase       )
								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(__UpperCAmelCase       )

				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(__UpperCAmelCase       )

								SCREAMING_SNAKE_CASE__		  =				np.maximum(__UpperCAmelCase  ,					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(__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				None

				SCREAMING_SNAKE_CASE__		  =				LmSeqsDataset(params=__UpperCAmelCase  ,					data=__UpperCAmelCase       )
				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=__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				student_model_class(__UpperCAmelCase       )

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

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


if __name__ == "__main__":
						main()





"""simple docstring"""






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_A   					=    'https://www.indeed.co.in/jobs?q=mobile+app+development&l='




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase = "mumbai"       )							->   Generator[tuple[str, str], None, None]:
				SCREAMING_SNAKE_CASE__		  =				BeautifulSoup(requests.get(url + location       ).content  ,					"html.parser"       )
				# This attribute finds out all the specifics listed in a job
				for job in soup.find_all("div"  ,					attrs={"data-tn-component": "organicJob"}       ):
								SCREAMING_SNAKE_CASE__		  =				job.find("a"  ,					attrs={"data-tn-element": "jobTitle"}       ).text.strip()
								SCREAMING_SNAKE_CASE__		  =				job.find("span"  ,					{"class": "company"}       ).text.strip()
								yield job_title, company_name


if __name__ == "__main__":
						for i, job in enumerate(fetch_jobs('Bangalore'), 1):
												print(F'Job {i:>2} is {job[0]} at {job[1]}')




