
"""simple docstring"""







import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


_UpperCamelCase					      =  logging.getLogger(__name__)







class  lowerCamelCase__ (	_UpperCamelCase			):
    SCREAMING_SNAKE_CASE		=      'summarization'
    SCREAMING_SNAKE_CASE		=      ['loss']
    SCREAMING_SNAKE_CASE		=      ROUGE_KEYS
    SCREAMING_SNAKE_CASE		=      'rouge2'






    def __init__(			self     ,A     ,**A ):
          if hparams.sortish_sampler and hparams.gpus > 1:
                UpperCAmelCase          =	False
          elif hparams.max_tokens_per_batch is not None:
                if hparams.gpus > 1:
                      raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
                if hparams.sortish_sampler:
                      raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )

          super().__init__(_SCREAMING_SNAKE_CASE     ,num_labels=_SCREAMING_SNAKE_CASE     ,mode=self.mode     ,**_SCREAMING_SNAKE_CASE )
          use_task_specific_params(self.model     ,"""summarization""" )
          save_git_info(self.hparams.output_dir )
          UpperCAmelCase          =	Path(self.output_dir ) / """metrics.json"""
          UpperCAmelCase          =	Path(self.output_dir ) / """hparams.pkl"""
          pickle_save(self.hparams     ,self.hparams_save_path )
          UpperCAmelCase          =	0
          UpperCAmelCase          =	defaultdict(_SCREAMING_SNAKE_CASE )
          UpperCAmelCase          =	self.config.model_type
          UpperCAmelCase          =	self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

          UpperCAmelCase          =	{
              """data_dir""": self.hparams.data_dir,
              """max_source_length""": self.hparams.max_source_length,
              """prefix""": self.model.config.prefix or """""",
          }
          UpperCAmelCase          =	{
              """train""": self.hparams.n_train,
              """val""": self.hparams.n_val,
              """test""": self.hparams.n_test,
          }
          UpperCAmelCase          =	{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

          UpperCAmelCase          =	{
              """train""": self.hparams.max_target_length,
              """val""": self.hparams.val_max_target_length,
              """test""": self.hparams.test_max_target_length,
          }
          assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
          assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
          if self.hparams.freeze_embeds:
                freeze_embeds(self.model )
          if self.hparams.freeze_encoder:
                freeze_params(self.model.get_encoder() )
                assert_all_frozen(self.model.get_encoder() )

          UpperCAmelCase          =	get_git_info()["""repo_sha"""]
          UpperCAmelCase          =	hparams.num_workers
          UpperCAmelCase          =	None  # default to config
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer     ,_SCREAMING_SNAKE_CASE ):
                UpperCAmelCase          =	self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                UpperCAmelCase          =	self.decoder_start_token_id
          UpperCAmelCase          =	(
              SeqaSeqDataset if hasattr(self.tokenizer     ,"""prepare_seq2seq_batch""" ) else LegacySeqaSeqDataset
          )
          UpperCAmelCase          =	False
          UpperCAmelCase          =	self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
          if self.hparams.eval_max_gen_length is not None:
                UpperCAmelCase          =	self.hparams.eval_max_gen_length
          else:
                UpperCAmelCase          =	self.model.config.max_length
          UpperCAmelCase          =	self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	{
              k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
          }
          save_json(_SCREAMING_SNAKE_CASE     ,Path(self.output_dir ) / """text_batch.json""" )
          save_json({k: v.tolist() for k, v in batch.items()}     ,Path(self.output_dir ) / """tok_batch.json""" )

          UpperCAmelCase          =	True
          return readable_batch






    def     _UpperCamelCase							(			self     ,A     ,**A ):
          return self.model(_SCREAMING_SNAKE_CASE     ,**_SCREAMING_SNAKE_CASE )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.tokenizer.batch_decode(
              _SCREAMING_SNAKE_CASE     ,skip_special_tokens=_SCREAMING_SNAKE_CASE     ,clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE )
          return lmap(str.strip     ,_SCREAMING_SNAKE_CASE )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.tokenizer.pad_token_id
          UpperCAmelCase      , UpperCAmelCase          =	batch["""input_ids"""], batch["""attention_mask"""]
          UpperCAmelCase          =	batch["""labels"""]
          if isinstance(self.model     ,_SCREAMING_SNAKE_CASE ):
                UpperCAmelCase          =	self.model._shift_right(_SCREAMING_SNAKE_CASE )
          else:
                UpperCAmelCase          =	shift_tokens_right(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE )
          if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                UpperCAmelCase          =	decoder_input_ids
                self.save_readable_batch(_SCREAMING_SNAKE_CASE )

          UpperCAmelCase          =	self(_SCREAMING_SNAKE_CASE     ,attention_mask=_SCREAMING_SNAKE_CASE     ,decoder_input_ids=_SCREAMING_SNAKE_CASE     ,use_cache=_SCREAMING_SNAKE_CASE )
          UpperCAmelCase          =	outputs["""logits"""]
          if self.hparams.label_smoothing == 0:
                # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                UpperCAmelCase          =	nn.CrossEntropyLoss(ignore_index=_SCREAMING_SNAKE_CASE )

                assert lm_logits.shape[-1] == self.vocab_size
                UpperCAmelCase          =	ce_loss_fct(lm_logits.view(-1     ,lm_logits.shape[-1] )     ,tgt_ids.view(-1 ) )
          else:
                UpperCAmelCase          =	nn.functional.log_softmax(_SCREAMING_SNAKE_CASE     ,dim=-1 )
                UpperCAmelCase      , UpperCAmelCase          =	label_smoothed_nll_loss(
                    _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,self.hparams.label_smoothing     ,ignore_index=_SCREAMING_SNAKE_CASE )
          return (loss,)






    @property
    def     _UpperCamelCase							(			self ):
          return self.tokenizer.pad_token_id






    def     _UpperCamelCase							(			self     ,A     ,A ):
          UpperCAmelCase          =	self._step(_SCREAMING_SNAKE_CASE )

          UpperCAmelCase          =	dict(zip(self.loss_names     ,_SCREAMING_SNAKE_CASE ) )
          # tokens per batch
          UpperCAmelCase          =	batch["""input_ids"""].ne(self.pad ).sum() + batch["""labels"""].ne(self.pad ).sum()
          UpperCAmelCase          =	batch["""input_ids"""].shape[0]
          UpperCAmelCase          =	batch["""input_ids"""].eq(self.pad ).sum()
          UpperCAmelCase          =	batch["""input_ids"""].eq(self.pad ).float().mean()
          # TODO(SS): make a wandb summary metric for this
          return {"loss": loss_tensors[0], "log": logs}






    def     _UpperCamelCase							(			self     ,A     ,A ):
          return self._generative_step(_SCREAMING_SNAKE_CASE )






    def     _UpperCamelCase							(			self     ,A     ,A="val" ):
          self.step_count += 1
          UpperCAmelCase          =	{k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
          UpperCAmelCase          =	losses["""loss"""]
          UpperCAmelCase          =	{
              k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
          }
          UpperCAmelCase          =	(
              generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
          )
          UpperCAmelCase          =	torch.tensor(_SCREAMING_SNAKE_CASE ).type_as(_SCREAMING_SNAKE_CASE )
          generative_metrics.update({k: v.item() for k, v in losses.items()} )
          losses.update(_SCREAMING_SNAKE_CASE )
          UpperCAmelCase          =	{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
          UpperCAmelCase          =	self.step_count
          self.metrics[prefix].append(_SCREAMING_SNAKE_CASE )  # callback writes this to self.metrics_save_path
          UpperCAmelCase          =	flatten_list([x["""preds"""] for x in outputs] )
          return {
              "log": all_metrics,
              "preds": preds,
              F'''{prefix}_loss''': loss,
              F'''{prefix}_{self.val_metric}''': metric_tensor,
          }






    def     _UpperCamelCase							(			self     ,A     ,A ):
          return calculate_rouge(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	time.time()

          # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
          UpperCAmelCase          =	self.model.generate(
              batch["""input_ids"""]     ,attention_mask=batch["""attention_mask"""]     ,use_cache=_SCREAMING_SNAKE_CASE     ,decoder_start_token_id=self.decoder_start_token_id     ,num_beams=self.eval_beams     ,max_length=self.eval_max_length     ,)
          UpperCAmelCase          =	(time.time() - ta) / batch["""input_ids"""].shape[0]
          UpperCAmelCase          =	self.ids_to_clean_text(_SCREAMING_SNAKE_CASE )
          UpperCAmelCase          =	self.ids_to_clean_text(batch["""labels"""] )
          UpperCAmelCase          =	self._step(_SCREAMING_SNAKE_CASE )
          UpperCAmelCase          =	dict(zip(self.loss_names     ,_SCREAMING_SNAKE_CASE ) )
          UpperCAmelCase          =	self.calc_generative_metrics(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE )
          UpperCAmelCase          =	np.mean(lmap(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE ) )
          base_metrics.update(gen_time=_SCREAMING_SNAKE_CASE     ,gen_len=_SCREAMING_SNAKE_CASE     ,preds=_SCREAMING_SNAKE_CASE     ,target=_SCREAMING_SNAKE_CASE     ,**_SCREAMING_SNAKE_CASE )
          return base_metrics






    def     _UpperCamelCase							(			self     ,A     ,A ):
          return self._generative_step(_SCREAMING_SNAKE_CASE )






    def     _UpperCamelCase							(			self     ,A ):
          return self.validation_epoch_end(_SCREAMING_SNAKE_CASE     ,prefix="""test""" )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.n_obs[type_path]
          UpperCAmelCase          =	self.target_lens[type_path]
          UpperCAmelCase          =	self.dataset_class(
              self.tokenizer     ,type_path=_SCREAMING_SNAKE_CASE     ,n_obs=_SCREAMING_SNAKE_CASE     ,max_target_length=_SCREAMING_SNAKE_CASE     ,**self.dataset_kwargs     ,)
          return dataset






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = False ):
          UpperCAmelCase          =	self.get_dataset(_SCREAMING_SNAKE_CASE )

          if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                UpperCAmelCase          =	dataset.make_sortish_sampler(_SCREAMING_SNAKE_CASE     ,distributed=self.hparams.gpus > 1 )
                return DataLoader(
                    _SCREAMING_SNAKE_CASE     ,batch_size=_SCREAMING_SNAKE_CASE     ,collate_fn=dataset.collate_fn     ,shuffle=_SCREAMING_SNAKE_CASE     ,num_workers=self.num_workers     ,sampler=_SCREAMING_SNAKE_CASE     ,)

          elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
                UpperCAmelCase          =	dataset.make_dynamic_sampler(
                    self.hparams.max_tokens_per_batch     ,distributed=self.hparams.gpus > 1 )
                return DataLoader(
                    _SCREAMING_SNAKE_CASE     ,batch_sampler=_SCREAMING_SNAKE_CASE     ,collate_fn=dataset.collate_fn     ,num_workers=self.num_workers     ,)
          else:
                return DataLoader(
                    _SCREAMING_SNAKE_CASE     ,batch_size=_SCREAMING_SNAKE_CASE     ,collate_fn=dataset.collate_fn     ,shuffle=_SCREAMING_SNAKE_CASE     ,num_workers=self.num_workers     ,sampler=_SCREAMING_SNAKE_CASE     ,)






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.get_dataloader("""train"""     ,batch_size=self.hparams.train_batch_size     ,shuffle=_SCREAMING_SNAKE_CASE )
          return dataloader






    def     _UpperCamelCase							(			self ):
          return self.get_dataloader("""val"""     ,batch_size=self.hparams.eval_batch_size )






    def     _UpperCamelCase							(			self ):
          return self.get_dataloader("""test"""     ,batch_size=self.hparams.eval_batch_size )






    @staticmethod
    def     _UpperCamelCase							(			A     ,A ):
          BaseTransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE )
          add_generic_args(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE )
          parser.add_argument(
              """--max_source_length"""     ,default=1_024     ,type=_SCREAMING_SNAKE_CASE     ,help=(
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )     ,)
          parser.add_argument(
              """--max_target_length"""     ,default=56     ,type=_SCREAMING_SNAKE_CASE     ,help=(
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )     ,)
          parser.add_argument(
              """--val_max_target_length"""     ,default=142     ,type=_SCREAMING_SNAKE_CASE     ,help=(
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )     ,)
          parser.add_argument(
              """--test_max_target_length"""     ,default=142     ,type=_SCREAMING_SNAKE_CASE     ,help=(
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )     ,)
          parser.add_argument("""--freeze_encoder"""     ,action="""store_true""" )
          parser.add_argument("""--freeze_embeds"""     ,action="""store_true""" )
          parser.add_argument("""--sortish_sampler"""     ,action="""store_true"""     ,default=_SCREAMING_SNAKE_CASE )
          parser.add_argument("""--overwrite_output_dir"""     ,action="""store_true"""     ,default=_SCREAMING_SNAKE_CASE )
          parser.add_argument("""--max_tokens_per_batch"""     ,type=_SCREAMING_SNAKE_CASE     ,default=_SCREAMING_SNAKE_CASE )
          parser.add_argument("""--logger_name"""     ,type=_SCREAMING_SNAKE_CASE     ,choices=["""default""", """wandb""", """wandb_shared"""]     ,default="""default""" )
          parser.add_argument("""--n_train"""     ,type=_SCREAMING_SNAKE_CASE     ,default=-1     ,required=_SCREAMING_SNAKE_CASE     ,help="""# examples. -1 means use all.""" )
          parser.add_argument("""--n_val"""     ,type=_SCREAMING_SNAKE_CASE     ,default=500     ,required=_SCREAMING_SNAKE_CASE     ,help="""# examples. -1 means use all.""" )
          parser.add_argument("""--n_test"""     ,type=_SCREAMING_SNAKE_CASE     ,default=-1     ,required=_SCREAMING_SNAKE_CASE     ,help="""# examples. -1 means use all.""" )
          parser.add_argument(
              """--task"""     ,type=_SCREAMING_SNAKE_CASE     ,default="""summarization"""     ,required=_SCREAMING_SNAKE_CASE     ,help="""# examples. -1 means use all.""" )
          parser.add_argument("""--label_smoothing"""     ,type=_SCREAMING_SNAKE_CASE     ,default=0.0     ,required=_SCREAMING_SNAKE_CASE )
          parser.add_argument("""--src_lang"""     ,type=_SCREAMING_SNAKE_CASE     ,default=""""""     ,required=_SCREAMING_SNAKE_CASE )
          parser.add_argument("""--tgt_lang"""     ,type=_SCREAMING_SNAKE_CASE     ,default=""""""     ,required=_SCREAMING_SNAKE_CASE )
          parser.add_argument("""--eval_beams"""     ,type=_SCREAMING_SNAKE_CASE     ,default=_SCREAMING_SNAKE_CASE     ,required=_SCREAMING_SNAKE_CASE )
          parser.add_argument(
              """--val_metric"""     ,type=_SCREAMING_SNAKE_CASE     ,default=_SCREAMING_SNAKE_CASE     ,required=_SCREAMING_SNAKE_CASE     ,choices=["""bleu""", """rouge2""", """loss""", None] )
          parser.add_argument("""--eval_max_gen_length"""     ,type=_SCREAMING_SNAKE_CASE     ,default=_SCREAMING_SNAKE_CASE     ,help="""never generate more than n tokens""" )
          parser.add_argument("""--save_top_k"""     ,type=_SCREAMING_SNAKE_CASE     ,default=1     ,required=_SCREAMING_SNAKE_CASE     ,help="""How many checkpoints to save""" )
          parser.add_argument(
              """--early_stopping_patience"""     ,type=_SCREAMING_SNAKE_CASE     ,default=-1     ,required=_SCREAMING_SNAKE_CASE     ,help=(
                  """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
                  """ val_check_interval will effect it."""
              )     ,)
          return parser







class  lowerCamelCase__ (	_UpperCamelCase			):
    SCREAMING_SNAKE_CASE		=      'translation'
    SCREAMING_SNAKE_CASE		=      ['loss']
    SCREAMING_SNAKE_CASE		=      ['bleu']
    SCREAMING_SNAKE_CASE		=      'bleu'






    def __init__(			self     ,A     ,**A ):
          super().__init__(_SCREAMING_SNAKE_CASE     ,**_SCREAMING_SNAKE_CASE )
          UpperCAmelCase          =	hparams.src_lang
          UpperCAmelCase          =	hparams.tgt_lang






    def     _UpperCamelCase							(			self     ,A     ,A ):
          return calculate_bleu(_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE )







def _a			(		_snake_case				,	_snake_case=None							):







      """simple docstring"""






      Path(args.output_dir							).mkdir(exist_ok=_UpperCamelCase							)
      check_output_dir(_UpperCamelCase				,	expected_items=3							)

      if model is None:
            if "summarization" in args.task:
                  UpperCAmelCase          =	SummarizationModule(_UpperCamelCase							)
            else:
                  UpperCAmelCase          =	TranslationModule(_UpperCamelCase							)
      UpperCAmelCase          =	Path(args.data_dir							).name
      if (
          args.logger_name == "default"
          or args.fast_dev_run
          or str(args.output_dir							).startswith("""/tmp"""							)
          or str(args.output_dir							).startswith("""/var"""							)
      ):
            UpperCAmelCase          =	True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            UpperCAmelCase          =	os.environ.get("""WANDB_PROJECT"""				,	_UpperCamelCase							)
            UpperCAmelCase          =	WandbLogger(name=model.output_dir.name				,	project=_UpperCamelCase							)

      elif args.logger_name == "wandb_shared":
            from pytorch_lightning.loggers import WandbLogger

            UpperCAmelCase          =	WandbLogger(name=model.output_dir.name				,	project=F'''hf_{dataset}'''							)

      if args.early_stopping_patience >= 0:
            UpperCAmelCase          =	get_early_stopping_callback(model.val_metric				,	args.early_stopping_patience							)
      else:
            UpperCAmelCase          =	False

      UpperCAmelCase          =	args.val_metric == """loss"""
      UpperCAmelCase          =	generic_train(
          _UpperCamelCase				,	_UpperCamelCase				,	logging_callback=SeqaSeqLoggingCallback()				,	checkpoint_callback=get_checkpoint_callback(
              args.output_dir				,	model.val_metric				,	args.save_top_k				,	_UpperCamelCase							)				,	early_stopping_callback=_UpperCamelCase				,	logger=_UpperCamelCase				,	)
      pickle_save(model.hparams				,	model.output_dir / """hparams.pkl"""							)
      if not args.do_predict:
            return model

      UpperCAmelCase          =	""""""
      UpperCAmelCase          =	sorted(glob.glob(os.path.join(args.output_dir				,	"""*.ckpt"""							)				,	recursive=_UpperCamelCase							)							)
      if checkpoints:
            UpperCAmelCase          =	checkpoints[-1]
            UpperCAmelCase          =	checkpoints[-1]
      trainer.logger.log_hyperparams(model.hparams							)

      # test() without a model tests using the best checkpoint automatically
      trainer.test()
      return model


if __name__ == "__main__":
  _UpperCamelCase					      =  argparse.ArgumentParser()
  _UpperCamelCase					      =  pl.Trainer.add_argparse_args(parser)
  _UpperCamelCase					      =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

  _UpperCamelCase					      =  parser.parse_args()

  main(args)











"""simple docstring"""







import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  {
    """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""",
}
_UpperCamelCase					      =  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      for attribute in key.split("""."""							):
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							)

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

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

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      UpperCAmelCase          =	fairseq_model.state_dict()

      UpperCAmelCase          =	hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase          =	False
            if "conv_layers" in name:
                  load_conv_layer(
                      _snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	hf_model.config.feat_extract_norm == """group"""				,	)
                  UpperCAmelCase          =	True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase          =	"""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
                              UpperCAmelCase          =	True
                              if "*" in mapped_key:
                                    UpperCAmelCase          =	name.split(_snake_case							)[0].split("""."""							)[-2]
                                    UpperCAmelCase          =	mapped_key.replace("""*"""				,	_snake_case							)
                              if "weight_g" in name:
                                    UpperCAmelCase          =	"""weight_g"""
                              elif "weight_v" in name:
                                    UpperCAmelCase          =	"""weight_v"""
                              elif "bias" in name:
                                    UpperCAmelCase          =	"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCAmelCase          =	"""weight"""
                              else:
                                    UpperCAmelCase          =	None
                              set_recursively(_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							)
                        continue
            if not is_used:
                  unused_weights.append(_snake_case							)

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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case=None				,	_snake_case=None				,	_snake_case=True							):







      """simple docstring"""






      if config_path is not None:
            UpperCAmelCase          =	UniSpeechSatConfig.from_pretrained(_snake_case							)
      else:
            UpperCAmelCase          =	UniSpeechSatConfig()

      UpperCAmelCase          =	""""""

      if is_finetuned:
            UpperCAmelCase          =	UniSpeechSatForCTC(_snake_case							)
      else:
            UpperCAmelCase          =	UniSpeechSatForPreTraining(_snake_case							)

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

      recursively_load_weights(_snake_case				,	_snake_case							)

      hf_wavavec.save_pretrained(_snake_case							)


if __name__ == "__main__":
  _UpperCamelCase					      =  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"""
  )
  _UpperCamelCase					      =  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
  )



