
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):




				for attribute in key.split("."							):
								snake_case__						=				getattr(__lowerCAmelCase    ,		__lowerCAmelCase							)

				if weight_type is not None:
								snake_case__						=				getattr(__lowerCAmelCase    ,		__lowerCAmelCase							).shape
				else:
								snake_case__						=				hf_pointer.shape

				assert hf_shape == value.shape, (
				    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
				    F""" {value.shape} for {full_name}"""
				)

				if weight_type == "weight":
								snake_case__						=				value
				elif weight_type == "weight_g":
								snake_case__						=				value
				elif weight_type == "weight_v":
								snake_case__						=				value
				elif weight_type == "bias":
								snake_case__						=				value
				else:
								snake_case__						=				value

				logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
				snake_case__						=				[]
				snake_case__						=				fairseq_model.state_dict()

				snake_case__						=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								snake_case__						=				False
								if "conv_layers" in name:
												load_conv_layer(
												    __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		hf_model.config.feat_extract_norm == "group"    ,		)
												snake_case__						=				True
								else:
												for key, mapped_key in MAPPING.items():
																snake_case__						=				"hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

																if key in name or (key.split("w2v_model."							)[-1] == name.split("."							)[0] and not is_finetuned):
																				snake_case__						=				True
																				if "*" in mapped_key:
																								snake_case__						=				name.split(__lowerCAmelCase							)[0].split("."							)[-2]
																								snake_case__						=				mapped_key.replace("*"    ,		__lowerCAmelCase							)
																				if "weight_g" in name:
																								snake_case__						=				"weight_g"
																				elif "weight_v" in name:
																								snake_case__						=				"weight_v"
																				elif "weight" in name:
																								snake_case__						=				"weight"
																				elif "bias" in name:
																								snake_case__						=				"bias"
																				else:
																								snake_case__						=				None
																				set_recursively(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)
																continue
								if not is_used:
												unused_weights.append(__lowerCAmelCase							)

				logger.warning(F"""Unused weights: {unused_weights}"""							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
				snake_case__						=				full_name.split("conv_layers."							)[-1]
				snake_case__						=				name.split("."							)
				snake_case__						=				int(items[0]							)
				snake_case__						=				int(items[1]							)

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
												)
												snake_case__						=				value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												snake_case__						=				value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
												    " found."
												)
												snake_case__						=				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
												)
												snake_case__						=				value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
				else:
								unused_weights.append(__lowerCAmelCase							)





@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase=True							):




				if config_path is not None:
								snake_case__						=				HubertConfig.from_pretrained(__lowerCAmelCase							)
				else:
								snake_case__						=				HubertConfig()

				if is_finetuned:
								if dict_path:
												snake_case__						=				Dictionary.load(__lowerCAmelCase							)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												snake_case__						=				target_dict.pad_index
												snake_case__						=				target_dict.bos_index
												snake_case__						=				target_dict.eos_index
												snake_case__						=				len(target_dict.symbols							)
												snake_case__						=				os.path.join(__lowerCAmelCase    ,		"vocab.json"							)
												if not os.path.isdir(__lowerCAmelCase							):
																logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__lowerCAmelCase							)							)
																return
												os.makedirs(__lowerCAmelCase    ,		exist_ok=__lowerCAmelCase							)
												with open(__lowerCAmelCase    ,		"w"    ,		encoding="utf-8"							) as vocab_handle:
																json.dump(target_dict.indices    ,		__lowerCAmelCase							)
												snake_case__						=				WavaVecaCTCTokenizer(
												    __lowerCAmelCase    ,		unk_token=target_dict.unk_word    ,		pad_token=target_dict.pad_word    ,		bos_token=target_dict.bos_word    ,		eos_token=target_dict.eos_word    ,		word_delimiter_token="|"    ,		do_lower_case=__lowerCAmelCase    ,		)
												snake_case__						=				True if config.feat_extract_norm == "layer" else False
												snake_case__						=				WavaVecaFeatureExtractor(
												    feature_size=1    ,		sampling_rate=16_000    ,		padding_value=0    ,		do_normalize=__lowerCAmelCase    ,		return_attention_mask=__lowerCAmelCase    ,		)
												snake_case__						=				WavaVecaProcessor(feature_extractor=__lowerCAmelCase    ,		tokenizer=__lowerCAmelCase							)
												processor.save_pretrained(__lowerCAmelCase							)

								snake_case__						=				HubertForCTC(__lowerCAmelCase							)
				else:
								snake_case__						=				HubertModel(__lowerCAmelCase							)

				if is_finetuned:
								snake_case__						,					snake_case__						,					snake_case__						=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]    ,		arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
				else:
								snake_case__						,					snake_case__						,					snake_case__						=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

				snake_case__						=				model[0].eval()

				recursively_load_weights(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)

				hf_wavavec.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
					__magic_name__            =  argparse.ArgumentParser()
					parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
					parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					parser.add_argument(
					    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
					)
					__magic_name__            =  parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)





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''')



