
import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        snake_case__						=				flax_key_tuple[:-1] + ("weight",)
        snake_case__						=				torch.permute(__lowerCAmelCase    ,		(0, 2, 1)							)
    elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase							):
        # linear layer
        snake_case__						=				flax_key_tuple[:-1] + ("weight",)
        snake_case__						=				flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        snake_case__						=				flax_key_tuple[:-1] + ("weight",)

    return flax_key_tuple, flax_tensor





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if "metadata" in layer:
        snake_case__						=				layer.split("metadata"							)
        snake_case__						=				"".join(split_layer[0]							)[:-1]
        snake_case__						=				[tuple(("metadata" + split_layer[1]).split("/"							)							)]
    elif "kvstore" in layer:
        snake_case__						=				layer.split("kvstore"							)
        snake_case__						=				"".join(split_layer[0]							)[:-1]
        snake_case__						=				[tuple(("kvstore" + split_layer[1]).split("/"							)							)]

    else:
        snake_case__						=				layer.split("/"							)
        snake_case__						=				"/".join(split_layer[:-1]							)
        snake_case__						=				(split_layer[-1],)

    if "kvstore/path" in layer:
        snake_case__						=				F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
    elif "kvstore/driver" in layer:
        snake_case__						=				"file"
    else:
        snake_case__						=				checkpoint_info[layer]

    return curr_real_layer_name, split_layer, content





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				rename_keys(__lowerCAmelCase							)
    snake_case__						=				{}
    for k, v in current_block.items():
        snake_case__						=				v
    snake_case__						=				new_current_block
    torch.save(__lowerCAmelCase    ,		__lowerCAmelCase							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase = WEIGHTS_NAME							):
    snake_case__						=				convert_file_size_to_int(__lowerCAmelCase							)
    snake_case__						=				[]
    snake_case__						=				{}
    snake_case__						=				0
    snake_case__						=				0

    os.makedirs(__lowerCAmelCase    ,		exist_ok=__lowerCAmelCase							)
    with gfile.GFile(switch_checkpoint_path + "/checkpoint"    ,		"rb"							) as fp:
        snake_case__						=				serialization.msgpack_restore(fp.read()							)["optimizer"]["target"]
        snake_case__						=				flatten_dict(__lowerCAmelCase    ,		sep="/"							)

    snake_case__						=				{}
    for layer in checkpoint_info.keys():
        snake_case__						,					snake_case__						,					snake_case__						=				get_key_and_tensorstore_dict(
            __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)
        if curr_real_layer_name in all_layers:
            snake_case__						=				content
        else:
            snake_case__						=				{split_layer[-1]: content}

    for key in all_layers.keys():
        # open tensorstore file
        snake_case__						=				ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
        snake_case__						=				torch.tensor(__lowerCAmelCase							)
        snake_case__						=				raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

        # use the renaming pattern from the small conversion scripts
        snake_case__						,					snake_case__						=				rename_base_flax_keys(tuple(key.split("/"							)							)    ,		__lowerCAmelCase							)
        snake_case__						=				"/".join(__lowerCAmelCase							)

        # If this weight is going to tip up over the maximal size, we split.
        if current_block_size + weight_size > max_shard_size:
            snake_case__						=				os.path.join(
                __lowerCAmelCase    ,		weights_name.replace(".bin"    ,		F"""-{len(__lowerCAmelCase							)+1:05d}-of-???.bin"""							)							)
            rename_and_save_block(__lowerCAmelCase    ,		__lowerCAmelCase							)
            sharded_state_dicts.append(current_block.keys()							)
            del current_block
            snake_case__						=				{}
            snake_case__						=				0

        snake_case__						=				raw_weights.to(getattr(__lowerCAmelCase    ,		__lowerCAmelCase							)							)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    snake_case__						=				os.path.join(__lowerCAmelCase    ,		weights_name.replace(".bin"    ,		F"""-{len(__lowerCAmelCase							)+1:05d}-of-???.bin"""							)							)
    rename_and_save_block(__lowerCAmelCase    ,		__lowerCAmelCase							)
    sharded_state_dicts.append(current_block.keys()							)

    # If we only have one shard, we return it
    if len(__lowerCAmelCase							) == 1:
        return {weights_name: sharded_state_dicts[0]}, None

    # Otherwise, let's build the index
    snake_case__						=				{}
    snake_case__						=				{}
    for idx, shard in enumerate(__lowerCAmelCase							):
        snake_case__						=				weights_name.replace(
            ".bin"    ,		F"""-{idx+1:05d}-of-{len(__lowerCAmelCase							):05d}.bin"""							)  # len(sharded_state_dicts):05d}
        snake_case__						=				os.path.join(__lowerCAmelCase    ,		weights_name.replace(".bin"    ,		F"""-{idx+1:05d}-of-???.bin"""							)							)
        os.rename(__lowerCAmelCase    ,		os.path.join(__lowerCAmelCase    ,		__lowerCAmelCase							)							)
        snake_case__						=				shard
        for key in shard:
            snake_case__						=				shard_file

    # Add the metadata
    snake_case__						=				{"total_size": total_size}
    snake_case__						=				{"metadata": metadata, "weight_map": weight_map}

    with open(os.path.join(__lowerCAmelCase    ,		__lowerCAmelCase							)    ,		"w"    ,		encoding="utf-8"							) as f:
        snake_case__						=				json.dumps(__lowerCAmelCase    ,		indent=2    ,		sort_keys=__lowerCAmelCase							) + "\n"
        f.write(__lowerCAmelCase							)

    return metadata, index


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--switch_t5x_checkpoint_path''',
         default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
         type=str,
         required=False,
         help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
     )
     parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
     parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
         type=str,
         required=False,
         help='''Path to the output pytorch model.''',
     )
     __magic_name__            =  parser.parse_args()
     shard_on_the_fly(
         args.switch_tax_checkpoint_path,
         args.pytorch_dump_folder_path,
         args.max_shard_size,
         args.dtype,
     )





def 	SCREAMING_SNAKE_CASE__    (      ):
    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

    snake_case__						=				SwitchTransformersConfig.from_pretrained("google/switch-base-8"							)
    config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"							)
    snake_case__						=				SwitchTransformersForConditionalGeneration.from_pretrained(
        "/home/arthur_huggingface_co/transformers/switch_converted"    ,		device_map="auto"							)

    snake_case__						=				TaTokenizer.from_pretrained("t5-small"							)
    snake_case__						=				"A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

    snake_case__						=				tokenizer(__lowerCAmelCase    ,		return_tensors="pt"							).input_ids
    snake_case__						=				model.generate(__lowerCAmelCase    ,		decoder_start_token_id=0							)
    print(tokenizer.decode(out[0]							)							)





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''')



