





import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


snake_case__					:							Union[str, Any]													=     logging.getLogger(__name__)





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = """token-classification"""




   def __init__(self	:Dict    ,   _UpperCamelCase	:List[str] )->					Dict:
         if type(_UpperCamelCase ) == dict:
               __A          =				Namespace(**_UpperCamelCase )
         __A          =				import_module('''tasks''' )
         try:
               __A          =				getattr(_UpperCamelCase    ,   hparams.task_type )
               __A          =				token_classification_task_clazz()
         except AttributeError:
               raise ValueError(
                   f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
                   f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
         __A          =				self.token_classification_task.get_labels(hparams.labels )
         __A          =				CrossEntropyLoss().ignore_index
         super().__init__(_UpperCamelCase    ,   len(self.labels )    ,   self.mode )




   def _lowerCAmelCase      (self	:Dict    ,   **_UpperCamelCase	:Optional[int] )->					Optional[Any]:
         return self.model(**_UpperCamelCase )




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Optional[int] )->					Dict:
         __A          =				{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
         if self.config.model_type != "distilbert":
               __A          =				(
                   batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
               )  # XLM and RoBERTa don"t use token_type_ids

         __A          =				self(**_UpperCamelCase )
         __A          =				outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}




   def _lowerCAmelCase      (self	:List[Any] )->					Optional[int]:
         __A          =				self.hparams
         for mode in ["train", "dev", "test"]:
               __A          =				self._feature_file(_UpperCamelCase )
               if os.path.exists(_UpperCamelCase ) and not args.overwrite_cache:
                     logger.info('''Loading features from cached file %s'''    ,   _UpperCamelCase )
                     __A          =				torch.load(_UpperCamelCase )
               else:
                     logger.info('''Creating features from dataset file at %s'''    ,   args.data_dir )
                     __A          =				self.token_classification_task.read_examples_from_file(args.data_dir    ,   _UpperCamelCase )
                     __A          =				self.token_classification_task.convert_examples_to_features(
                         _UpperCamelCase    ,   self.labels    ,   args.max_seq_length    ,   self.tokenizer    ,   cls_token_at_end=bool(self.config.model_type in ['''xlnet'''] )    ,   cls_token=self.tokenizer.cls_token    ,   cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0    ,   sep_token=self.tokenizer.sep_token    ,   sep_token_extra=_UpperCamelCase    ,   pad_on_left=bool(self.config.model_type in ['''xlnet'''] )    ,   pad_token=self.tokenizer.pad_token_id    ,   pad_token_segment_id=self.tokenizer.pad_token_type_id    ,   pad_token_label_id=self.pad_token_label_id    ,   )
                     logger.info('''Saving features into cached file %s'''    ,   _UpperCamelCase )
                     torch.save(_UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:bool = False )->					DataLoader:
         __A          =				self._feature_file(_UpperCamelCase )
         logger.info('''Loading features from cached file %s'''    ,   _UpperCamelCase )
         __A          =				torch.load(_UpperCamelCase )
         __A          =				torch.tensor([f.input_ids for f in features]    ,   dtype=torch.long )
         __A          =				torch.tensor([f.attention_mask for f in features]    ,   dtype=torch.long )
         if features[0].token_type_ids is not None:
               __A          =				torch.tensor([f.token_type_ids for f in features]    ,   dtype=torch.long )
         else:
               __A          =				torch.tensor([0 for f in features]    ,   dtype=torch.long )
               # HACK(we will not use this anymore soon)
         __A          =				torch.tensor([f.label_ids for f in features]    ,   dtype=torch.long )
         return DataLoader(
             TensorDataset(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )    ,   batch_size=_UpperCamelCase )




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Any )->					Optional[Any]:
         """Compute validation""" ""
         __A          =				{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
         if self.config.model_type != "distilbert":
               __A          =				(
                   batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
               )  # XLM and RoBERTa don"t use token_type_ids
         __A          =				self(**_UpperCamelCase )
         __A   ,						__A          =				outputs[:2]
         __A          =				logits.detach().cpu().numpy()
         __A          =				inputs['''labels'''].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Optional[int] )->					Optional[int]:
         __A          =				torch.stack([x['''val_loss'''] for x in outputs] ).mean()
         __A          =				np.concatenate([x['''pred'''] for x in outputs]    ,   axis=0 )
         __A          =				np.argmax(_UpperCamelCase    ,   axis=2 )
         __A          =				np.concatenate([x['''target'''] for x in outputs]    ,   axis=0 )

         __A          =				dict(enumerate(self.labels ) )
         __A          =				[[] for _ in range(out_label_ids.shape[0] )]
         __A          =				[[] for _ in range(out_label_ids.shape[0] )]

         for i in range(out_label_ids.shape[0] ):
               for j in range(out_label_ids.shape[1] ):
                     if out_label_ids[i, j] != self.pad_token_label_id:
                           out_label_list[i].append(label_map[out_label_ids[i][j]] )
                           preds_list[i].append(label_map[preds[i][j]] )

         __A          =				{
             '''val_loss''': val_loss_mean,
             '''accuracy_score''': accuracy_score(_UpperCamelCase    ,   _UpperCamelCase ),
             '''precision''': precision_score(_UpperCamelCase    ,   _UpperCamelCase ),
             '''recall''': recall_score(_UpperCamelCase    ,   _UpperCamelCase ),
             '''f1''': fa_score(_UpperCamelCase    ,   _UpperCamelCase ),
         }

         __A          =				dict(results.items() )
         __A          =				results
         return ret, preds_list, out_label_list




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Dict )->					List[str]:
         # when stable
         __A   ,						__A   ,						__A          =				self._eval_end(_UpperCamelCase )
         __A          =				ret['''log''']
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         # updating to test_epoch_end instead of deprecated test_end
         __A   ,						__A   ,						__A          =				self._eval_end(_UpperCamelCase )

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         __A          =				ret['''log''']
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






   @staticmethod
   def _lowerCAmelCase      (_UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:List[str] )->					str:
         # Add NER specific options
         BaseTransformer.add_model_specific_args(_UpperCamelCase    ,   _UpperCamelCase )
         parser.add_argument(
             '''--task_type'''    ,   default='''NER'''    ,   type=_UpperCamelCase    ,   help='''Task type to fine tune in training (e.g. NER, POS, etc)''' )
         parser.add_argument(
             '''--max_seq_length'''    ,   default=128    ,   type=_UpperCamelCase    ,   help=(
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )    ,   )

         parser.add_argument(
             '''--labels'''    ,   default=''''''    ,   type=_UpperCamelCase    ,   help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''    ,   )
         parser.add_argument(
             '''--gpus'''    ,   default=0    ,   type=_UpperCamelCase    ,   help='''The number of GPUs allocated for this, it is by default 0 meaning none'''    ,   )

         parser.add_argument(
             '''--overwrite_cache'''    ,   action='''store_true'''    ,   help='''Overwrite the cached training and evaluation sets''' )

         return parser


if __name__ == "__main__":
       snake_case__					:							Optional[int]													=     argparse.ArgumentParser()
       add_generic_args(parser, os.getcwd())
       snake_case__					:							Dict													=     NERTransformer.add_model_specific_args(parser, os.getcwd())
       snake_case__					:							int													=     parser.parse_args()
       snake_case__					:							str													=     NERTransformer(args)
       snake_case__					:							List[str]													=     generic_train(model, args)

       if args.do_predict:
              # See https://github.com/huggingface/transformers/issues/3159
              # pl use this default format to create a checkpoint:
              # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
              # /pytorch_lightning/callbacks/model_checkpoint.py#L322
              snake_case__					:							Optional[Any]													=     sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
              snake_case__					:							Union[str, Any]													=     model.load_from_checkpoint(checkpoints[-1])
              trainer.test(model)












import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case__					:							Dict													=     logging.get_logger(__name__)

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',
    '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__					:							Optional[int]													=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]



def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   int )	->						List[str]:







      '''simple docstring'''






      for attribute in key.split('''.''' ):
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase )

      if weight_type is not None:
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase ).shape
      else:
            __A          =				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":
            __A          =				value
      elif weight_type == "weight_g":
            __A          =				value
      elif weight_type == "weight_v":
            __A          =				value
      elif weight_type == "bias":
            __A          =				value
      else:
            __A          =				value

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



def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






      __A          =				[]
      __A          =				fairseq_model.state_dict()

      __A          =				hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            __A          =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				hf_model.config.feat_extract_norm == '''group'''					,				)
                  __A          =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        __A          =				'''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
                              __A          =				True
                              if "*" in mapped_key:
                                    __A          =				name.split(lowerCamelCase )[0].split('''.''' )[-2]
                                    __A          =				mapped_key.replace('''*'''					,				lowerCamelCase )
                              if "weight_g" in name:
                                    __A          =				'''weight_g'''
                              elif "weight_v" in name:
                                    __A          =				'''weight_v'''
                              elif "bias" in name:
                                    __A          =				'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    __A          =				'''weight'''
                              else:
                                    __A          =				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 _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Any					,				lowerCamelCase:   int					,				lowerCamelCase:   int					,				lowerCamelCase:   List[str] )	->						Union[str, Any]:







      '''simple docstring'''






      __A          =				full_name.split('''conv_layers.''' )[-1]
      __A          =				name.split('''.''' )
      __A          =				int(items[0] )
      __A          =				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.""" )
                  __A          =				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.""" )
                  __A          =				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.""" )
                  __A          =				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.""" )
                  __A          =				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 _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   int					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[int]=True )	->						List[Any]:







      '''simple docstring'''






      if config_path is not None:
            __A          =				UniSpeechSatConfig.from_pretrained(lowerCamelCase )
      else:
            __A          =				UniSpeechSatConfig()

      __A          =				''''''

      if is_finetuned:
            __A          =				UniSpeechSatForCTC(lowerCamelCase )
      else:
            __A          =				UniSpeechSatForPreTraining(lowerCamelCase )

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

      recursively_load_weights(lowerCamelCase					,				lowerCamelCase )

      hf_wavavec.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
       snake_case__					:							Tuple													=     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__					:							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
       )





