import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    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.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
__magic_name__    							=							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 							UpperCAmelCase__(					__UpperCAmelCase			:     List[Any]     ,				__UpperCAmelCase			:     Dict     ,				__UpperCAmelCase			:     Optional[Any]     ,				__UpperCAmelCase			:     List[Any]     ,				__UpperCAmelCase			:     Optional[Any]       ):



   for attribute in key.split('.'       ):
      __snake_case   :			Any										=						getattr(UpperCAmelCase__     ,				UpperCAmelCase__       )

   if weight_type is not None:
      __snake_case   :			str										=						getattr(UpperCAmelCase__     ,				UpperCAmelCase__       ).shape
   else:
      __snake_case   :			Tuple										=						hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          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   :			List[Any]										=						value
   elif weight_type == "weight_g":
      __snake_case   :			List[str]										=						value
   elif weight_type == "weight_v":
      __snake_case   :			Tuple										=						value
   elif weight_type == "bias":
      __snake_case   :			Optional[Any]										=						value
   elif weight_type == "running_mean":
      __snake_case   :			List[str]										=						value
   elif weight_type == "running_var":
      __snake_case   :			Union[str, Any]										=						value
   elif weight_type == "num_batches_tracked":
      __snake_case   :			int										=						value
   elif weight_type == "inv_freq":
      __snake_case   :			Optional[int]										=						value
   else:
      __snake_case   :			List[Any]										=						value

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






def 							UpperCAmelCase__(					__UpperCAmelCase			:     Tuple     ,				__UpperCAmelCase			:     Any     ,				__UpperCAmelCase			:     Optional[int]       ):
   __snake_case   :			Dict										=						[]
   __snake_case   :			Optional[int]										=						fairseq_model.state_dict()

   __snake_case   :			Optional[Any]										=						hf_model.wavaveca_conformer.feature_extractor

   for name, value in fairseq_dict.items():
      __snake_case   :			str										=						False
      if "conv_layers" in name:
         load_conv_layer(
             UpperCAmelCase__     ,				UpperCAmelCase__     ,				UpperCAmelCase__     ,				UpperCAmelCase__     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
         __snake_case   :			Optional[Any]										=						True
      else:
         for key, mapped_key in MAPPING.items():
            __snake_case   :			List[Any]										=						'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
               __snake_case   :			Tuple										=						True
               if "*" in mapped_key:
                  __snake_case   :			Tuple										=						name.split(UpperCAmelCase__       )[0].split('.'       )[-2]
                  __snake_case   :			List[str]										=						mapped_key.replace('*'     ,				UpperCAmelCase__       )
               if "pos_bias_u" in name:
                  __snake_case   :			List[str]										=						None
               elif "pos_bias_v" in name:
                  __snake_case   :			int										=						None
               elif "weight_g" in name:
                  __snake_case   :			Tuple										=						'weight_g'
               elif "weight_v" in name:
                  __snake_case   :			Any										=						'weight_v'
               elif "bias" in name:
                  __snake_case   :			str										=						'bias'
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  __snake_case   :			int										=						'weight'
               elif "running_mean" in name:
                  __snake_case   :			Optional[Any]										=						'running_mean'
               elif "inv_freq" in name:
                  __snake_case   :			int										=						'inv_freq'
               elif "running_var" in name:
                  __snake_case   :			Any										=						'running_var'
               elif "num_batches_tracked" in name:
                  __snake_case   :			Optional[int]										=						'num_batches_tracked'
               else:
                  __snake_case   :			str										=						None
               set_recursively(UpperCAmelCase__     ,				UpperCAmelCase__     ,				UpperCAmelCase__     ,				UpperCAmelCase__     ,				UpperCAmelCase__       )
            continue
      if not is_used:
         unused_weights.append(UpperCAmelCase__       )

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






def 							UpperCAmelCase__(					__UpperCAmelCase			:     str     ,				__UpperCAmelCase			:     Optional[int]     ,				__UpperCAmelCase			:     Union[str, Any]     ,				__UpperCAmelCase			:     int     ,				__UpperCAmelCase			:     Optional[int]       ):
   __snake_case   :			Optional[int]										=						full_name.split('conv_layers.'       )[-1]
   __snake_case   :			Optional[Any]										=						name.split('.'       )
   __snake_case   :			Tuple										=						int(items[0]       )
   __snake_case   :			str										=						int(items[1]       )

   if type_id == 0:
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
            raise ValueError(
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""       )
         __snake_case   :			Tuple										=						value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
            raise ValueError(
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""       )
         __snake_case   :			Optional[int]										=						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:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
            raise ValueError(
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""       )
         __snake_case   :			Union[str, Any]										=						value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
            raise ValueError(
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""       )
         __snake_case   :			Optional[int]										=						value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
   else:
      unused_weights.append(UpperCAmelCase__       )






@torch.no_grad()
def 							UpperCAmelCase__(					__UpperCAmelCase			:     Any     ,				__UpperCAmelCase			:     int     ,				__UpperCAmelCase			:     Optional[int]=None     ,				__UpperCAmelCase			:     Union[str, Any]=None     ,				__UpperCAmelCase			:     int=True       ):



   if config_path is not None:
      __snake_case   :			Optional[int]										=						WavaVecaConformerConfig.from_pretrained(UpperCAmelCase__     ,				hidden_act='swish'       )
   else:
      __snake_case   :			str										=						WavaVecaConformerConfig()

   if "rope" in checkpoint_path:
      __snake_case   :			int										=						'rotary'

   if is_finetuned:
      if dict_path:
         __snake_case   :			List[Any]										=						Dictionary.load(UpperCAmelCase__       )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __snake_case   :			Union[str, Any]										=						target_dict.pad_index
         __snake_case   :			str										=						target_dict.bos_index
         __snake_case   :			Tuple										=						target_dict.eos_index
         __snake_case   :			List[Any]										=						len(target_dict.symbols       )
         __snake_case   :			Optional[int]										=						os.path.join(UpperCAmelCase__     ,				'vocab.json'       )
         if not os.path.isdir(UpperCAmelCase__       ):
            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCAmelCase__       )       )
            return
         os.makedirs(UpperCAmelCase__     ,				exist_ok=UpperCAmelCase__       )
         __snake_case   :			Dict										=						target_dict.indices

         # fairseq has the <pad> and <s> switched
         __snake_case   :			int										=						0
         __snake_case   :			Any										=						1
         with open(UpperCAmelCase__     ,				'w'     ,				encoding='utf-8'       ) as vocab_handle:
            json.dump(UpperCAmelCase__     ,				UpperCAmelCase__       )
         __snake_case   :			Dict										=						WavaVecaCTCTokenizer(
             UpperCAmelCase__     ,				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=UpperCAmelCase__     ,				)
         __snake_case   :			Union[str, Any]										=						True if config.feat_extract_norm == 'layer' else False
         __snake_case   :			Dict										=						WavaVecaFeatureExtractor(
             feature_size=1     ,				sampling_rate=1_60_00     ,				padding_value=0     ,				do_normalize=UpperCAmelCase__     ,				return_attention_mask=UpperCAmelCase__     ,				)
         __snake_case   :			Tuple										=						WavaVecaProcessor(feature_extractor=UpperCAmelCase__     ,				tokenizer=UpperCAmelCase__       )
         processor.save_pretrained(UpperCAmelCase__       )

      __snake_case   :			Optional[Any]										=						WavaVecaConformerForCTC(UpperCAmelCase__       )
   else:
      __snake_case   :			List[Any]										=						WavaVecaConformerForPreTraining(UpperCAmelCase__       )

   if is_finetuned:
      __snake_case						,       __snake_case						,       __snake_case   :			Optional[Any]										=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]     ,				arg_overrides={'data': '/'.join(dict_path.split('/'       )[:-1]       )}       )
   else:
      __snake_case   :			Any										=						argparse.Namespace(task='audio_pretraining'       )
      __snake_case   :			Dict										=						fairseq.tasks.setup_task(UpperCAmelCase__       )

      __snake_case						,       __snake_case						,       __snake_case   :			str										=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,				task=UpperCAmelCase__       )

   __snake_case   :			Any										=						model[0].eval()

   recursively_load_weights(UpperCAmelCase__     ,				UpperCAmelCase__     ,				not is_finetuned       )

   hf_wavavec.save_pretrained(UpperCAmelCase__       )


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_wavaveca_conformer_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      __lowercase(						UpperCAmelCase__ = "laptop"			):


      """simple docstring"""



      lowerCamelCase             =		F"""https://www.amazon.in/laptop/s?k={product}"""
      lowerCamelCase             =		{
          "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",
      }
      lowerCamelCase             =		BeautifulSoup(requests.get(UpperCAmelCase__ , headers=UpperCAmelCase__			).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__":
     a_    :  Dict						  =		'headphones'
     get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")