


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "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",
    "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",
}
_SCREAMING_SNAKE_CASE      :			str      				=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
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_    ):



     """simple docstring"""






     snake_case					=    []
     snake_case					=    fairseq_model.state_dict()

     snake_case					=    hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     snake_case					=    None

     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
          elif name.split('''.'''    )[0] == "proj":
               snake_case					=    fairseq_model.proj
               snake_case					=    True
          else:
               for key, mapped_key in MAPPING.items():
                    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 "bias" in name:
                              snake_case					=    '''bias'''
                         elif "weight" in name:
                              snake_case					=    '''weight'''
                         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}'''    )

     return proj_weight
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_    ):



     """simple docstring"""






     snake_case			,  snake_case					=    emb.weight.shape
     snake_case					=    nn.Linear(UpperCamelCase_							,UpperCamelCase_							,bias=UpperCamelCase_    )
     snake_case					=    emb.weight.data
     return lin_layer
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     with open(UpperCamelCase_							,'''r'''							,encoding='''utf-8'''    ) as f:
          snake_case					=    f.readlines()
          snake_case					=    [line.split(''' '''    )[0] for line in lines]

     snake_case					=    len(UpperCamelCase_    )

     snake_case					=    {
         '''<s>''': 0,
         '''<pad>''': 1,
         '''</s>''': 2,
         '''<unk>''': 3,
     }

     vocab_dict.update(dict(zip(UpperCamelCase_							,range(4							,num_words + 4    )    )    )    )
     return vocab_dict
@torch.no_grad()
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,):



     """simple docstring"""






     snake_case					=    WavaVecaConfig.from_pretrained(UpperCamelCase_    )
     snake_case					=    SpeechaTextaConfig.from_pretrained(
         UpperCamelCase_							,vocab_size=UpperCamelCase_							,decoder_layers=UpperCamelCase_							,do_stable_layer_norm=UpperCamelCase_    )

     snake_case					=    WavaVecaFeatureExtractor(
         feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)

     snake_case			,  snake_case			,  snake_case					=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]							,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''    )[:-1]    )}    )
     snake_case					=    model[0].eval()

     # set weights for wav2vec2 encoder
     snake_case					=    WavaVecaModel(UpperCamelCase_    )
     snake_case					=    recursively_load_weights_wavaveca(model.encoder							,UpperCamelCase_    )

     snake_case					=    SpeechaTextaForCausalLM(UpperCamelCase_    )
     snake_case			,  snake_case					=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,strict=UpperCamelCase_    )

     # set output linear layer
     unexpected_keys.remove('''embed_out'''    )
     snake_case					=    nn.Parameter(model.decoder.embed_out.detach()    )

     # layer norm is init to identity matrix so leaving it is fine
     logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''    )
     logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''    )

     snake_case					=    SpeechEncoderDecoderModel(encoder=UpperCamelCase_							,decoder=UpperCamelCase_    )
     snake_case					=    False

     # add projection layer
     snake_case					=    nn.Parameter(projection_layer.weight    )
     snake_case					=    nn.Parameter(projection_layer.bias    )

     snake_case					=    create_vocab_dict(UpperCamelCase_    )

     with open(os.path.join(UpperCamelCase_							,'''vocab.json'''    )							,'''w'''    ) as fp:
          json.dump(UpperCamelCase_							,UpperCamelCase_    )

     snake_case					=    SpeechaTextaTokenizer(os.path.join(UpperCamelCase_							,'''vocab.json'''    )    )
     tokenizer.save_pretrained(UpperCamelCase_    )

     snake_case					=    hf_wavavec.config.to_dict()
     snake_case					=    tokenizer.pad_token_id
     snake_case					=    tokenizer.bos_token_id
     snake_case					=    tokenizer.eos_token_id
     snake_case					=    '''speech_to_text_2'''
     snake_case					=    '''wav2vec2'''

     snake_case					=    SpeechEncoderDecoderConfig.from_dict(UpperCamelCase_    )

     hf_wavavec.save_pretrained(UpperCamelCase_    )
     feature_extractor.save_pretrained(UpperCamelCase_    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Dict      				=   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(
     "--encoder_config_path",
     default="facebook/wav2vec2-large-lv60",
     type=str,
     help="Path to hf encoder wav2vec2 checkpoint config",
 )
 parser.add_argument(
     "--decoder_config_path",
     default="facebook/s2t-small-mustc-en-fr-st",
     type=str,
     help="Path to hf decoder s2t checkpoint config",
 )
 parser.add_argument("--vocab_size", default=1_02_24, type=int, help="Vocab size of decoder")
 parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

 _SCREAMING_SNAKE_CASE      :			List[str]      				=   parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     vocab_size=args.vocab_size,
     num_decoder_layers=args.num_decoder_layers,
 )








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]}''')




