



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    Optional[Any]				,   __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    Tuple = None				,   )  ->						Union[str, Any]:
					__lowerCAmelCase	   ={}

					if train_file is not None:
										__lowerCAmelCase	   =[train_file]
					if eval_file is not None:
										__lowerCAmelCase	   =[eval_file]
					if test_file is not None:
										__lowerCAmelCase	   =[test_file]

					__lowerCAmelCase	   =datasets.load_dataset("""csv"""				,   data_files=_SCREAMING_SNAKE_CASE	)
					__lowerCAmelCase	   =list(ds[list(files.keys()	)[0]].features.keys()	)
					__lowerCAmelCase	   =features_name.pop(_SCREAMING_SNAKE_CASE	)
					__lowerCAmelCase	   =list(set(ds[list(files.keys()	)[0]][label_name]	)	)
					__lowerCAmelCase	   ={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE	)}
					__lowerCAmelCase	   =tokenizer.model_input_names
					__lowerCAmelCase	   ={}

					if len(_SCREAMING_SNAKE_CASE	) == 1:
										for k in files.keys():
															__lowerCAmelCase	   =ds[k].map(
															    lambda __lowerCamelCase	: tokenizer.batch_encode_plus(
															        example[features_name[0]]				,   truncation=_SCREAMING_SNAKE_CASE				,   max_length=_SCREAMING_SNAKE_CASE				,   padding="""max_length"""	)				,   batched=_SCREAMING_SNAKE_CASE				,   )
					elif len(_SCREAMING_SNAKE_CASE	) == 2:
										for k in files.keys():
															__lowerCAmelCase	   =ds[k].map(
															    lambda __lowerCamelCase	: tokenizer.batch_encode_plus(
															        (example[features_name[0]], example[features_name[1]])				,   truncation=_SCREAMING_SNAKE_CASE				,   max_length=_SCREAMING_SNAKE_CASE				,   padding="""max_length"""				,   )				,   batched=_SCREAMING_SNAKE_CASE				,   )

					def gen_train():
										for ex in transformed_ds[datasets.Split.TRAIN]:
															__lowerCAmelCase	   ={k: v for k, v in ex.items() if k in input_names}
															__lowerCAmelCase	   =labelaid[ex[label_name]]
															yield (d, label)

					def gen_val():
										for ex in transformed_ds[datasets.Split.VALIDATION]:
															__lowerCAmelCase	   ={k: v for k, v in ex.items() if k in input_names}
															__lowerCAmelCase	   =labelaid[ex[label_name]]
															yield (d, label)

					def gen_test():
										for ex in transformed_ds[datasets.Split.TEST]:
															__lowerCAmelCase	   ={k: v for k, v in ex.items() if k in input_names}
															__lowerCAmelCase	   =labelaid[ex[label_name]]
															yield (d, label)

					__lowerCAmelCase	   =(
					    tf.data.Dataset.from_generator(
					        _SCREAMING_SNAKE_CASE				,   ({k: tf.intaa for k in input_names}, tf.intaa)				,   ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,   )
					    if datasets.Split.TRAIN in transformed_ds
					    else None
					)

					if train_ds is not None:
										__lowerCAmelCase	   =train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

					__lowerCAmelCase	   =(
					    tf.data.Dataset.from_generator(
					        _SCREAMING_SNAKE_CASE				,   ({k: tf.intaa for k in input_names}, tf.intaa)				,   ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,   )
					    if datasets.Split.VALIDATION in transformed_ds
					    else None
					)

					if val_ds is not None:
										__lowerCAmelCase	   =val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

					__lowerCAmelCase	   =(
					    tf.data.Dataset.from_generator(
					        _SCREAMING_SNAKE_CASE				,   ({k: tf.intaa for k in input_names}, tf.intaa)				,   ({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,   )
					    if datasets.Split.TEST in transformed_ds
					    else None
					)

					if test_ds is not None:
										__lowerCAmelCase	   =test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

					return train_ds, val_ds, test_ds, labelaid


lowercase_	       =			logging.getLogger(__name__)






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       field(metadata={"help": "Which column contains the label"}				)
	SCREAMING_SNAKE_CASE       =       field(default=snake_case__	,    metadata={"help": "The path of the training file"}				)
	SCREAMING_SNAKE_CASE       =       field(default=snake_case__	,    metadata={"help": "The path of the development file"}				)
	SCREAMING_SNAKE_CASE       =       field(default=snake_case__	,    metadata={"help": "The path of the test file"}				)
	SCREAMING_SNAKE_CASE       =       field(
	    default=128	,    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=snake_case__	,    metadata={"help": "Overwrite the cached training and evaluation sets"}				)






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}				)
	SCREAMING_SNAKE_CASE       =       field(
	    default=snake_case__	,    metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
	SCREAMING_SNAKE_CASE       =       field(
	    default=snake_case__	,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
	SCREAMING_SNAKE_CASE       =       field(default=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=snake_case__	,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,    )






def 					__lowerCAmelCase		(  )  ->						Union[str, Any]:
					# 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	   =HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
					__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =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."""	)

					# Setup logging
					logging.basicConfig(
					    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,   datefmt="""%m/%d/%Y %H:%M:%S"""				,   level=logging.INFO				,   )
					logger.info(
					    f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1	)}, """
					    f"""16-bits training: {training_args.fpaa}"""	)
					logger.info(f"""Training/evaluation parameters {training_args}"""	)

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

					__lowerCAmelCase	   =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				,   )

					__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =get_tfds(
					    train_file=data_args.train_file				,   eval_file=data_args.dev_file				,   test_file=data_args.test_file				,   tokenizer=_SCREAMING_SNAKE_CASE				,   label_column_id=data_args.label_column_id				,   max_seq_length=data_args.max_seq_length				,   )

					__lowerCAmelCase	   =AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,   num_labels=len(_SCREAMING_SNAKE_CASE	)				,   labelaid=_SCREAMING_SNAKE_CASE				,   idalabel={id: label for label, id in labelaid.items()}				,   finetuning_task="""text-classification"""				,   cache_dir=model_args.cache_dir				,   )

					with training_args.strategy.scope():
										__lowerCAmelCase	   =TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path				,   from_pt=bool(""".bin""" in model_args.model_name_or_path	)				,   config=_SCREAMING_SNAKE_CASE				,   cache_dir=model_args.cache_dir				,   )

					def compute_metrics(__lowerCamelCase	:    List[str]	) -> Dict:
										__lowerCAmelCase	   =np.argmax(p.predictions				,   axis=1	)

										return {"acc": (preds == p.label_ids).mean()}

					# Initialize our Trainer
					__lowerCAmelCase	   =TFTrainer(
					    model=_SCREAMING_SNAKE_CASE				,   args=_SCREAMING_SNAKE_CASE				,   train_dataset=_SCREAMING_SNAKE_CASE				,   eval_dataset=_SCREAMING_SNAKE_CASE				,   compute_metrics=_SCREAMING_SNAKE_CASE				,   )

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										tokenizer.save_pretrained(training_args.output_dir	)

					# Evaluation
					__lowerCAmelCase	   ={}
					if training_args.do_eval:
										logger.info("""*** Evaluate ***"""	)
										__lowerCAmelCase	   =trainer.evaluate()
										__lowerCAmelCase	   =os.path.join(training_args.output_dir				,   """eval_results.txt"""	)

										with open(_SCREAMING_SNAKE_CASE				,   """w"""	) as writer:
															logger.info("""***** Eval results *****"""	)

															for key, value in result.items():
																				logger.info(f"""  {key} = {value}"""	)
																				writer.write(f"""{key} = {value}\n"""	)

															results.update(_SCREAMING_SNAKE_CASE	)

					return results


if __name__ == "__main__":
				main()






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str = "laptop"	)  ->						DataFrame:
					__lowerCAmelCase	   =f"""https://www.amazon.in/laptop/s?k={product}"""
					__lowerCAmelCase	   ={
					    """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
					    """Accept-Language""": """en-US, en;q=0.5""",
					}
					__lowerCAmelCase	   =BeautifulSoup(requests.get(__lowerCamelCase				,   headers=__lowerCamelCase	).text	)
					# Initialize a Pandas dataframe with the column titles
					__lowerCAmelCase	   =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	   =item.ha.text
															__lowerCAmelCase	   ="""https://www.amazon.in/""" + item.ha.a["""href"""]
															__lowerCAmelCase	   =item.find("""span"""				,   attrs={"""class""": """a-offscreen"""}	).text
															try:
																				__lowerCAmelCase	   =item.find("""span"""				,   attrs={"""class""": """a-icon-alt"""}	).text
															except AttributeError:
																				__lowerCAmelCase	   ="""Not available"""
															try:
																				__lowerCAmelCase	   =(
																				    """₹"""
																				    + item.find(
																				        """span"""				,   attrs={"""class""": """a-price a-text-price"""}	).text.split("""₹"""	)[1]
																				)
															except AttributeError:
																				__lowerCAmelCase	   =""""""
															try:
																				__lowerCAmelCase	   =float(
																				    (
																				        (
																				            float(product_mrp.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				            - float(product_price.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				        )
																				        / float(product_mrp.strip("""₹"""	).replace(""","""				,   """"""	)	)
																				    )
																				    * 100	)
															except ValueError:
																				__lowerCAmelCase	   =float("""nan"""	)
										except AttributeError:
															pass
										__lowerCAmelCase	   =[
										    product_title,
										    product_link,
										    product_price,
										    product_rating,
										    product_mrp,
										    discount,
										]
					__lowerCAmelCase	   =""" """
					__lowerCAmelCase	   =""" """
					data_frame.index += 1
					return data_frame


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




