





"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


_snake_case     :		Any       						= Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

_snake_case     :		List[str]       						= {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

_snake_case     :		Dict       						= 'zero2'
_snake_case     :		str       						= 'zero3'
_snake_case     :		Dict       						= [ZEROa, ZEROa]






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							# customize the test name generator function as we want both params to appear in the sub-test
							# name, as by default it shows only the first param
							A		=  parameterized.to_safe_name("_".join(str(UpperCamelCase       ) for x in param.args       )       )
							return F"{func.__name__}_{param_based_name}"


# Cartesian-product of zero stages with models to test
_snake_case     :		List[str]       						= list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 					_UpperCAmelCase   (	lowercase_ ):


					@parameterized.expand(__UpperCamelCase     ,       name_func=__UpperCamelCase		)
					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Optional[Any]		):
												self.run_and_check(
												    stage=__UpperCamelCase     ,       model=__UpperCamelCase     ,       distributed=__UpperCamelCase     ,       fpaa=__UpperCamelCase     ,       )


					@require_torch_multi_gpu
					@parameterized.expand(__UpperCamelCase     ,       name_func=__UpperCamelCase		)
					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:List[Any]     ,       __UpperCamelCase			:List[Any]		):
												self.run_and_check(
												    stage=__UpperCamelCase     ,       model=__UpperCamelCase     ,       distributed=__UpperCamelCase     ,       fpaa=__UpperCamelCase     ,       )


					@parameterized.expand(__UpperCamelCase     ,       name_func=__UpperCamelCase		)
					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Dict		):
												self.run_and_check(
												    stage=__UpperCamelCase     ,       model=__UpperCamelCase     ,       distributed=__UpperCamelCase     ,       fpaa=__UpperCamelCase     ,       )


					@require_torch_multi_gpu
					@parameterized.expand(__UpperCamelCase     ,       name_func=__UpperCamelCase		)
					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:Optional[Any]     ,       __UpperCamelCase			:int		):
												self.run_and_check(
												    stage=__UpperCamelCase     ,       model=__UpperCamelCase     ,       distributed=__UpperCamelCase     ,       fpaa=__UpperCamelCase     ,       )


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:Optional[int]		):
												# XXX: run_asr is premature and doesn't save any results
												# so all we check for now is that the process didn't fail
												pass


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:int = 10     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:bool = True     ,       ):
												A		=  models[model]

												A		=  self.run_trainer(
												    stage=__UpperCamelCase     ,       model_name=__UpperCamelCase     ,       eval_steps=__UpperCamelCase     ,       num_train_epochs=1     ,       distributed=__UpperCamelCase     ,       fpaa=__UpperCamelCase     ,       )

												self.do_checks(__UpperCamelCase		)

												return output_dir


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:int = 10     ,       __UpperCamelCase			:int = 1     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:bool = True     ,       ):
												A		=  self.get_auto_remove_tmp_dir("./xxx"     ,       after=__UpperCamelCase		)
												A		=  f"\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(__UpperCamelCase		)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        ".split()

												if fpaa:
																			args.extend(["--fp16"]		)

												# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
												# hence the separate config files
												A		=  f"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
												A		=  [f"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
												A		=  self.get_launcher(__UpperCamelCase		)

												A		=  launcher + script + args + ds_args
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												execute_subprocess_async(__UpperCamelCase     ,       env=self.get_env()		)

												return output_dir


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Optional[Any]=False		):
												# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
												# - it won't be able to handle that
												# 2. for now testing with just 2 gpus max (since some quality tests may give different
												# results with mode gpus because we use very little data)
												A		=  min(2     ,       get_gpu_count()		) if distributed else 1
												return f"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()









"""simple docstring"""
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






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


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= 'headphones'
		get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")


