






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__snake_case      :      Union[str, Any]             =       logging.get_logger(__name__)

__snake_case      :      Dict             =       {
    """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""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """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""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
__snake_case      :      Dict             =       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Any						)					->				List[str]:

      """simple docstring"""



      for attribute in key.split('.'						):
            lowerCAmelCase__        =  getattr(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE						)

      if weight_type is not None:
            lowerCAmelCase__        =  getattr(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE						).shape
      else:
            lowerCAmelCase__        =  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":
            lowerCAmelCase__        =  value
      elif weight_type == "weight_g":
            lowerCAmelCase__        =  value
      elif weight_type == "weight_v":
            lowerCAmelCase__        =  value
      elif weight_type == "bias":
            lowerCAmelCase__        =  value
      else:
            lowerCAmelCase__        =  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						)					->				int:

      """simple docstring"""



      lowerCAmelCase__        =  []
      lowerCAmelCase__        =  fairseq_model.state_dict()

      lowerCAmelCase__        =  hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            lowerCAmelCase__        =  False
            if "conv_layers" in name:
                  load_conv_layer(
                      _SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    hf_model.config.feat_extract_norm == 'group'      ,    )
                  lowerCAmelCase__        =  True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowerCAmelCase__        =  "unispeech_sat." + 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]:
                              if "layer_norm_for_extract" in name and (".".join(name.split('.'						)[:-1]						) != key):
                                    # special case since naming is very similar
                                    continue
                              lowerCAmelCase__        =  True
                              if "*" in mapped_key:
                                    lowerCAmelCase__        =  name.split(_SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
                                    lowerCAmelCase__        =  mapped_key.replace('*'      ,    _SCREAMING_SNAKE_CASE						)
                              if "weight_g" in name:
                                    lowerCAmelCase__        =  "weight_g"
                              elif "weight_v" in name:
                                    lowerCAmelCase__        =  "weight_v"
                              elif "bias" in name:
                                    lowerCAmelCase__        =  "bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    lowerCAmelCase__        =  "weight"
                              else:
                                    lowerCAmelCase__        =  None
                              set_recursively(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE						)
                        continue
            if not is_used:
                  unused_weights.append(_SCREAMING_SNAKE_CASE						)

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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Optional[int]						)					->				List[Any]:

      """simple docstring"""



      lowerCAmelCase__        =  full_name.split('conv_layers.'						)[-1]
      lowerCAmelCase__        =  name.split('.'						)
      lowerCAmelCase__        =  int(items[0]						)
      lowerCAmelCase__        =  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."						)
                  lowerCAmelCase__        =  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."						)
                  lowerCAmelCase__        =  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[layer_id].layer_norm.bias.data.shape} was found."						)
                  lowerCAmelCase__        =  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[layer_id].layer_norm.weight.data.shape} was found."						)
                  lowerCAmelCase__        =  value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
      else:
            unused_weights.append(_SCREAMING_SNAKE_CASE						)







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   List[str]=None      ,    UpperCamelCase_    :   int=None      ,    UpperCamelCase_    :   str=True						)					->				Tuple:

      """simple docstring"""



      if config_path is not None:
            lowerCAmelCase__        =  UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE						)
      else:
            lowerCAmelCase__        =  UniSpeechSatConfig()

      lowerCAmelCase__        =  ""

      if is_finetuned:
            lowerCAmelCase__        =  UniSpeechSatForCTC(_SCREAMING_SNAKE_CASE						)
      else:
            lowerCAmelCase__        =  UniSpeechSatForPreTraining(_SCREAMING_SNAKE_CASE						)

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

      recursively_load_weights(_SCREAMING_SNAKE_CASE      ,    _SCREAMING_SNAKE_CASE						)

      hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     __snake_case      :      str             =       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"""
     )
     __snake_case      :      Optional[Any]             =       parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )













from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

__snake_case      :      Any             =       """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""







def 		_UpperCamelCase				(      UpperCamelCase_    :   str = "mumbai"						)					->				Generator[tuple[str, str], None, None]:

						"""simple docstring"""



						lowerCAmelCase__        =  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'}						):
												lowerCAmelCase__        =  job.find('a'      ,    attrs={'data-tn-element': 'jobTitle'}						).text.strip()
												lowerCAmelCase__        =  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]}')





