
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__magic_name__            =  logging.get_logger(__name__)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				R"\w+[.]\d+"
    snake_case__						=				re.findall(__lowerCAmelCase    ,		__lowerCAmelCase							)
    for pat in pats:
        snake_case__						=				key.replace(__lowerCAmelCase    ,		"_".join(pat.split("."							)							)							)
    return key





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				pt_tuple_key[:-1] + ("scale",)
    if (
        any("norm" in str_ for str_ in pt_tuple_key							)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        snake_case__						=				pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        snake_case__						=				pt_tuple_key[:-1] + ("scale",)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        snake_case__						=				pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    snake_case__						=				pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        snake_case__						=				pt_tensor.transpose(2    ,		3    ,		1    ,		0							)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    snake_case__						=				pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight":
        snake_case__						=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    snake_case__						=				pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    snake_case__						=				pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=42							):
    # Step 1: Convert pytorch tensor to numpy
    snake_case__						=				{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    snake_case__						=				flax_model.init_weights(PRNGKey(__lowerCAmelCase							)							)

    snake_case__						=				flatten_dict(__lowerCAmelCase							)
    snake_case__						=				{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        snake_case__						=				rename_key(__lowerCAmelCase							)
        snake_case__						=				tuple(renamed_pt_key.split("."							)							)

        # Correctly rename weight parameters
        snake_case__						,					snake_case__						=				rename_key_and_reshape_tensor(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""							)

        # also add unexpected weight so that warning is thrown
        snake_case__						=				jnp.asarray(__lowerCAmelCase							)

    return unflatten_dict(__lowerCAmelCase							)





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





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


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



