
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


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






def   lowerCAmelCase_						(					_snake_case      :  Dict   )				->		Dict:







	'''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_						(					_snake_case      :  Tuple      ,   _snake_case      :  Optional[Any]   )				->		Optional[int]:







	'''simple docstring'''


	if args.student_type == "roberta":
		__magic_name__		:		str	   =     False
	elif args.student_type == "gpt2":
		__magic_name__		:		Union[str, Any]	   =     False






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Optional[int]   )				->		List[Any]:







	'''simple docstring'''


	if args.student_type == "roberta":
		__magic_name__		:		Tuple	   =     False






def   lowerCAmelCase_						(					)				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     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=4000      ,   help="Checkpoint interval."   )
	__magic_name__		:		Any	   =     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   )

	__magic_name__   ,    __magic_name__   ,    __magic_name__		:		int	   =     MODEL_CLASSES[args.student_type]
	__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Tuple	   =     MODEL_CLASSES[args.teacher_type]

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

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

	__magic_name__		:		List[Any]	   =     LmSeqsDataset(params=_snake_case      ,   data=_snake_case   )
	logger.info("Data loader created."   )

	# STUDENT #
	logger.info(F'''Loading student config from {args.student_config}'''   )
	__magic_name__		:		Optional[int]	   =     student_config_class.from_pretrained(args.student_config   )
	__magic_name__		:		int	   =     True

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

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

	# TEACHER #
	__magic_name__		:		Tuple	   =     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()
	__magic_name__		:		List[str]	   =     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()


from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def   lowerCAmelCase_						(					_snake_case      :  str = "laptop"   )				->		DataFrame:







	'''simple docstring'''


	__magic_name__		:		Tuple	   =     F'''https://www.amazon.in/laptop/s?k={product}'''
	__magic_name__		:		Dict	   =     {
	    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
	    "Accept-Language": "en-US, en;q=0.5",
	}
	__magic_name__		:		Tuple	   =     BeautifulSoup(requests.get(_snake_case      ,   headers=_snake_case   ).text   )
	# Initialize a Pandas dataframe with the column titles
	__magic_name__		:		int	   =     DataFrame(
	    columns=[
	        "Product Title",
	        "Product Link",
	        "Current Price of the product",
	        "Product Rating",
	        "MRP of the product",
	        "Discount",
	    ]   )
	# Loop through each entry and store them in the dataframe
	for item, _ in zip_longest(
	    soup.find_all(
	        "div"      ,   attrs={"class": "s-result-item", "data-component-type": "s-search-result"}      ,   )      ,   soup.find_all("div"      ,   attrs={"class": "a-row a-size-base a-color-base"}   )      ,   ):
		try:
			__magic_name__		:		Dict	   =     item.ha.text
			__magic_name__		:		Optional[int]	   =     "https://www.amazon.in/" + item.ha.a["href"]
			__magic_name__		:		Optional[Any]	   =     item.find("span"      ,   attrs={"class": "a-offscreen"}   ).text
			try:
				__magic_name__		:		Union[str, Any]	   =     item.find("span"      ,   attrs={"class": "a-icon-alt"}   ).text
			except AttributeError:
				__magic_name__		:		Dict	   =     "Not available"
			try:
				__magic_name__		:		Optional[int]	   =     (
				    "₹"
				    + item.find(
				        "span"      ,   attrs={"class": "a-price a-text-price"}   ).text.split("₹"   )[1]
				)
			except AttributeError:
				__magic_name__		:		List[str]	   =     ""
			try:
				__magic_name__		:		int	   =     float(
				    (
				        (
				            float(product_mrp.strip("₹"   ).replace(","      ,   ""   )   )
				            - float(product_price.strip("₹"   ).replace(","      ,   ""   )   )
				        )
				        / float(product_mrp.strip("₹"   ).replace(","      ,   ""   )   )
				    )
				    * 100   )
			except ValueError:
				__magic_name__		:		str	   =     float("nan"   )
		except AttributeError:
			pass
		__magic_name__		:		Optional[int]	   =     [
		    product_title,
		    product_link,
		    product_price,
		    product_rating,
		    product_mrp,
		    discount,
		]
	__magic_name__		:		Optional[Any]	   =     " "
	__magic_name__		:		str	   =     " "
	data_frame.index += 1
	return data_frame


if __name__ == "__main__":
				snake_case :		Any							      =  "headphones"
				get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")
