


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			int      				=   {
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     for attribute in key.split('''.'''    ):
          snake_case					=    getattr(UpperCamelCase_							,UpperCamelCase_    )

     if weight_type is not None:
          snake_case					=    getattr(UpperCamelCase_							,UpperCamelCase_    ).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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     snake_case					=    fairseq_model.state_dict()

     snake_case					=    hf_model.sew.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(
                   UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,hf_model.config.feat_extract_norm == '''group'''							,)
               snake_case					=    True
          else:
               for key, mapped_key in MAPPING.items():
                    snake_case					=    '''sew.''' + 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]:
                         snake_case					=    True
                         if "*" in mapped_key:
                              snake_case					=    name.split(UpperCamelCase_    )[0].split('''.'''    )[-2]
                              snake_case					=    mapped_key.replace('''*'''							,UpperCamelCase_    )
                         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(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
                    continue
          if not is_used:
               unused_weights.append(UpperCamelCase_    )

     logger.warning(F'''Unused weights: {unused_weights}'''    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     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(UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    SEWConfig()
     if is_finetuned:
          snake_case					=    model.wav_encoder.wav_model.cfg
     else:
          snake_case					=    model.cfg

     snake_case					=    fs_config.conv_bias
     snake_case					=    eval(fs_config.conv_feature_layers    )
     snake_case					=    [x[0] for x in conv_layers]
     snake_case					=    [x[1] for x in conv_layers]
     snake_case					=    [x[2] for x in conv_layers]
     snake_case					=    '''gelu'''
     snake_case					=    '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
     snake_case					=    0.0
     snake_case					=    fs_config.activation_fn.name
     snake_case					=    fs_config.encoder_embed_dim
     snake_case					=    0.02
     snake_case					=    fs_config.encoder_ffn_embed_dim
     snake_case					=    1e-5
     snake_case					=    fs_config.encoder_layerdrop
     snake_case					=    fs_config.encoder_attention_heads
     snake_case					=    fs_config.conv_pos_groups
     snake_case					=    fs_config.conv_pos
     snake_case					=    len(UpperCamelCase_    )
     snake_case					=    fs_config.encoder_layers
     snake_case					=    fs_config.squeeze_factor

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          snake_case					=    model.cfg
          snake_case					=    fs_config.final_dropout
          snake_case					=    fs_config.layerdrop
     snake_case					=    fs_config.activation_dropout
     snake_case					=    fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     snake_case					=    fs_config.attention_dropout
     snake_case					=    fs_config.dropout_input
     snake_case					=    fs_config.dropout
     snake_case					=    fs_config.mask_channel_length
     snake_case					=    fs_config.mask_channel_prob
     snake_case					=    fs_config.mask_length
     snake_case					=    fs_config.mask_prob

     snake_case					=    '''Wav2Vec2FeatureExtractor'''
     snake_case					=    '''Wav2Vec2CTCTokenizer'''

     return config
@torch.no_grad()
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True    ):



     """simple docstring"""






     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]    )

     if config_path is not None:
          snake_case					=    SEWConfig.from_pretrained(UpperCamelCase_    )
     else:
          snake_case					=    convert_config(model[0]							,UpperCamelCase_    )
     snake_case					=    model[0].eval()

     snake_case					=    True if config.feat_extract_norm == '''layer''' else False
     snake_case					=    WavaVecaFeatureExtractor(
         feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)

     if is_finetuned:
          if dict_path:
               snake_case					=    Dictionary.load(UpperCamelCase_    )

               # 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.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(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_    )
               with open(UpperCamelCase_							,'''w'''							,encoding='''utf-8'''    ) as vocab_handle:
                    json.dump(target_dict.indices							,UpperCamelCase_    )
               snake_case					=    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					=    WavaVecaProcessor(feature_extractor=UpperCamelCase_							,tokenizer=UpperCamelCase_    )
               processor.save_pretrained(UpperCamelCase_    )

          snake_case					=    SEWForCTC(UpperCamelCase_    )
     else:
          snake_case					=    SEWModel(UpperCamelCase_    )
          feature_extractor.save_pretrained(UpperCamelCase_    )

     recursively_load_weights(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

     hf_model.save_pretrained(UpperCamelCase_    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Any      				=   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(
     "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 _SCREAMING_SNAKE_CASE      :			int      				=   parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )








from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_SCREAMING_SNAKE_CASE      :			str      				=   "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def       UpperCAmelCase__						(UpperCamelCase_ = "mumbai"    ):



     """simple docstring"""






     snake_case					=    BeautifulSoup(requests.get(url + location    ).content							,'''html.parser'''    )
     # This attribute finds out all the specifics listed in a job
     for job in soup.find_all('''div'''							,attrs={'''data-tn-component''': '''organicJob'''}    ):
          snake_case					=    job.find('''a'''							,attrs={'''data-tn-element''': '''jobTitle'''}    ).text.strip()
          snake_case					=    job.find('''span'''							,{'''class''': '''company'''}    ).text.strip()
          yield job_title, company_name


if __name__ == "__main__":
 for i, job in enumerate(fetch_jobs("Bangalore"), 1):
  print(f'''Job {i:>2} is {job[0]} at {job[1]}''')




