



"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__snake_case							:   str      =    logging.getLogger(__name__)



@dataclass
class 			A__   :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}							)
	SCREAMING_SNAKE_CASE          =	field(
	    default=__SCREAMING_SNAKE_CASE  ,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}							)
	SCREAMING_SNAKE_CASE          =	field(
	    default='NER'  ,		metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}							)
	SCREAMING_SNAKE_CASE          =	field(
	    default=__SCREAMING_SNAKE_CASE  ,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}							)
	SCREAMING_SNAKE_CASE          =	field(default=__SCREAMING_SNAKE_CASE  ,		metadata={'help': 'Set this flag to use fast tokenization.'}							)
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	SCREAMING_SNAKE_CASE          =	field(
	    default=__SCREAMING_SNAKE_CASE  ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,		)






@dataclass
class 			A__   :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	field(
	    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}							)
	SCREAMING_SNAKE_CASE          =	field(
	    default=__SCREAMING_SNAKE_CASE  ,		metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}  ,		)
	SCREAMING_SNAKE_CASE          =	field(
	    default=1_2_8  ,		metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }  ,		)
	SCREAMING_SNAKE_CASE          =	field(
	    default=__SCREAMING_SNAKE_CASE  ,		metadata={'help': 'Overwrite the cached training and evaluation sets'}							)






def 			_lowercase				(  )						->   Optional[int]:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			__lowerCAmelCase      :  Any												=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
			if len(sys.argv	) == 2 and sys.argv[1].endswith(".json"	):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						__lowerCAmelCase						,	__lowerCAmelCase						,	__lowerCAmelCase      :  List[Any]												=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
			else:
						__lowerCAmelCase						,	__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir	)
			    and os.listdir(training_args.output_dir	)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
						    " --overwrite_output_dir to overcome."	)

			__lowerCAmelCase      :  List[str]												=       import_module("tasks"	)
			try:
						__lowerCAmelCase      :  List[Any]												=       getattr(__snake_case					,model_args.task_type	)
						__lowerCAmelCase      :  TokenClassificationTask												=       token_classification_task_clazz()
			except AttributeError:
						raise ValueError(
						    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
						    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""	)

			# Setup logging
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,datefmt="%m/%d/%Y %H:%M:%S"					,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,)
			logger.warning(
			    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,training_args.local_rank					,training_args.device					,training_args.n_gpu					,bool(training_args.local_rank != -1	)					,training_args.fpaa					,)
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank	):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info("Training/evaluation parameters %s"					,__snake_case	)

			# Set seed
			set_seed(training_args.seed	)

			# Prepare CONLL-2003 task
			__lowerCAmelCase      :  Union[str, Any]												=       token_classification_task.get_labels(data_args.labels	)
			__lowerCAmelCase      :  Dict[int, str]												=       dict(enumerate(__snake_case	)	)
			__lowerCAmelCase      :  List[str]												=       len(__snake_case	)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			__lowerCAmelCase      :  Any												=       AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,num_labels=__snake_case					,idalabel=__snake_case					,labelaid={label: i for i, label in enumerate(__snake_case	)}					,cache_dir=model_args.cache_dir					,)
			__lowerCAmelCase      :  List[Any]												=       AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,cache_dir=model_args.cache_dir					,use_fast=model_args.use_fast					,)
			__lowerCAmelCase      :  Tuple												=       AutoModelForTokenClassification.from_pretrained(
			    model_args.model_name_or_path					,from_tf=bool(".ckpt" in model_args.model_name_or_path	)					,config=__snake_case					,cache_dir=model_args.cache_dir					,)

			# Get datasets
			__lowerCAmelCase      :  str												=       (
			    TokenClassificationDataset(
			        token_classification_task=__snake_case					,data_dir=data_args.data_dir					,tokenizer=__snake_case					,labels=__snake_case					,model_type=config.model_type					,max_seq_length=data_args.max_seq_length					,overwrite_cache=data_args.overwrite_cache					,mode=Split.train					,)
			    if training_args.do_train
			    else None
			)
			__lowerCAmelCase      :  int												=       (
			    TokenClassificationDataset(
			        token_classification_task=__snake_case					,data_dir=data_args.data_dir					,tokenizer=__snake_case					,labels=__snake_case					,model_type=config.model_type					,max_seq_length=data_args.max_seq_length					,overwrite_cache=data_args.overwrite_cache					,mode=Split.dev					,)
			    if training_args.do_eval
			    else None
			)

			def align_predictions(__snake_case					,__snake_case	) -> Tuple[List[int], List[int]]:
						__lowerCAmelCase      :  List[Any]												=       np.argmax(__snake_case					,axis=2	)

						__lowerCAmelCase						,	__lowerCAmelCase      :  str												=       preds.shape

						__lowerCAmelCase      :  int												=       [[] for _ in range(__snake_case	)]
						__lowerCAmelCase      :  Dict												=       [[] for _ in range(__snake_case	)]

						for i in range(__snake_case	):
									for j in range(__snake_case	):
												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
															out_label_list[i].append(label_map[label_ids[i][j]]	)
															preds_list[i].append(label_map[preds[i][j]]	)

						return preds_list, out_label_list

			def compute_metrics(__snake_case	) -> Dict:
						__lowerCAmelCase						,	__lowerCAmelCase      :  str												=       align_predictions(p.predictions					,p.label_ids	)
						return {
						    "accuracy_score": accuracy_score(__snake_case					,__snake_case	),
						    "precision": precision_score(__snake_case					,__snake_case	),
						    "recall": recall_score(__snake_case					,__snake_case	),
						    "f1": fa_score(__snake_case					,__snake_case	),
						}

			# Data collator
			__lowerCAmelCase      :  Dict												=       DataCollatorWithPadding(__snake_case					,pad_to_multiple_of=8	) if training_args.fpaa else None

			# Initialize our Trainer
			__lowerCAmelCase      :  int												=       Trainer(
			    model=__snake_case					,args=__snake_case					,train_dataset=__snake_case					,eval_dataset=__snake_case					,compute_metrics=__snake_case					,data_collator=__snake_case					,)

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_process_zero():
									tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
			__lowerCAmelCase      :  Union[str, Any]												=       {}
			if training_args.do_eval:
						logger.info("*** Evaluate ***"	)

						__lowerCAmelCase      :  Tuple												=       trainer.evaluate()

						__lowerCAmelCase      :  str												=       os.path.join(training_args.output_dir					,"eval_results.txt"	)
						if trainer.is_world_process_zero():
									with open(__snake_case					,"w"	) as writer:
												logger.info("***** Eval results *****"	)
												for key, value in result.items():
															logger.info("  %s = %s"					,__snake_case					,__snake_case	)
															writer.write("%s = %s\n" % (key, value)	)

									results.update(__snake_case	)

    # Predict
			if training_args.do_predict:
						__lowerCAmelCase      :  List[Any]												=       TokenClassificationDataset(
						    token_classification_task=__snake_case					,data_dir=data_args.data_dir					,tokenizer=__snake_case					,labels=__snake_case					,model_type=config.model_type					,max_seq_length=data_args.max_seq_length					,overwrite_cache=data_args.overwrite_cache					,mode=Split.test					,)

						__lowerCAmelCase						,	__lowerCAmelCase						,	__lowerCAmelCase      :  str												=       trainer.predict(__snake_case	)
						__lowerCAmelCase						,	__lowerCAmelCase      :  Union[str, Any]												=       align_predictions(__snake_case					,__snake_case	)

						__lowerCAmelCase      :  int												=       os.path.join(training_args.output_dir					,"test_results.txt"	)
						if trainer.is_world_process_zero():
									with open(__snake_case					,"w"	) as writer:
												for key, value in metrics.items():
															logger.info("  %s = %s"					,__snake_case					,__snake_case	)
															writer.write("%s = %s\n" % (key, value)	)

        # Save predictions
						__lowerCAmelCase      :  str												=       os.path.join(training_args.output_dir					,"test_predictions.txt"	)
						if trainer.is_world_process_zero():
									with open(__snake_case					,"w"	) as writer:
												with open(os.path.join(data_args.data_dir					,"test.txt"	)					,"r"	) as f:
															token_classification_task.write_predictions_to_file(__snake_case					,__snake_case					,__snake_case	)

			return results


def 			_lowercase				(  __snake_case	)						->   Union[str, Any]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()




"""simple docstring"""






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def 			_lowercase				(  __snake_case = "laptop"	)						->   DataFrame:
			__lowerCAmelCase      :  str												=       F"""https://www.amazon.in/laptop/s?k={product}"""
			__lowerCAmelCase      :  Union[str, Any]												=       {
			    "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",
			}
			__lowerCAmelCase      :  List[str]												=       BeautifulSoup(requests.get(__snake_case					,headers=__snake_case	).text	)
			# Initialize a Pandas dataframe with the column titles
			__lowerCAmelCase      :  Dict												=       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:
									__lowerCAmelCase      :  Any												=       item.ha.text
									__lowerCAmelCase      :  Union[str, Any]												=       "https://www.amazon.in/" + item.ha.a["href"]
									__lowerCAmelCase      :  Any												=       item.find("span"					,attrs={"class": "a-offscreen"}	).text
									try:
												__lowerCAmelCase      :  Union[str, Any]												=       item.find("span"					,attrs={"class": "a-icon-alt"}	).text
									except AttributeError:
												__lowerCAmelCase      :  Optional[Any]												=       "Not available"
									try:
												__lowerCAmelCase      :  Union[str, Any]												=       (
												    "₹"
												    + item.find(
												        "span"					,attrs={"class": "a-price a-text-price"}	).text.split("₹"	)[1]
												)
									except AttributeError:
												__lowerCAmelCase      :  Dict												=       ""
									try:
												__lowerCAmelCase      :  str												=       float(
												    (
												        (
												            float(product_mrp.strip("₹"	).replace(","					,""	)	)
												            - float(product_price.strip("₹"	).replace(","					,""	)	)
												        )
												        / float(product_mrp.strip("₹"	).replace(","					,""	)	)
												    )
												    * 100	)
									except ValueError:
												__lowerCAmelCase      :  List[str]												=       float("nan"	)
						except AttributeError:
									pass
						__lowerCAmelCase      :  int												=       [
						    product_title,
						    product_link,
						    product_price,
						    product_rating,
						    product_mrp,
						    discount,
						]
			__lowerCAmelCase      :  Union[str, Any]												=       " "
			__lowerCAmelCase      :  Union[str, Any]												=       " "
			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""")