






'''simple docstring'''





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 # Load configuration defined in the metadata file
 with open(lowercase__) as metadata_file:
  lowerCamelCase__  =     json.load(lowercase__)
 lowerCamelCase__  =     LukeConfig(use_entity_aware_attention=lowercase__				,     **metadata["model_config"])

 # Load in the weights from the checkpoint_path
 lowerCamelCase__  =     torch.load(lowercase__				,     map_location="cpu")

 # Load the entity vocab file
 lowerCamelCase__  =     load_entity_vocab(lowercase__)

 lowerCamelCase__  =     RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"])

 # Add special tokens to the token vocabulary for downstream tasks
 lowerCamelCase__  =     AddedToken("<ent>"				,     lstrip=lowercase__				,     rstrip=lowercase__)
 lowerCamelCase__  =     AddedToken("<ent2>"				,     lstrip=lowercase__				,     rstrip=lowercase__)
 tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]})
 config.vocab_size += 2

 print(F'''Saving tokenizer to {pytorch_dump_folder_path}''')
 tokenizer.save_pretrained(lowercase__)
 with open(os.path.join(lowercase__				,     LukeTokenizer.vocab_files_names["entity_vocab_file"])				,     "w") as f:
  json.dump(lowercase__				,     lowercase__)

 lowerCamelCase__  =     LukeTokenizer.from_pretrained(lowercase__)

 # Initialize the embeddings of the special tokens
 lowerCamelCase__  =     state_dict["embeddings.word_embeddings.weight"]
 lowerCamelCase__  =     word_emb[tokenizer.convert_tokens_to_ids(["@"])[0]].unsqueeze(0)
 lowerCamelCase__  =     word_emb[tokenizer.convert_tokens_to_ids(["#"])[0]].unsqueeze(0)
 lowerCamelCase__  =     torch.cat([word_emb, ent_emb, enta_emb])

 # Initialize the query layers of the entity-aware self-attention mechanism
 for layer_index in range(config.num_hidden_layers):
  for matrix_name in ["query.weight", "query.bias"]:
   lowerCamelCase__  =     F'''encoder.layer.{layer_index}.attention.self.'''
   lowerCamelCase__  =     state_dict[prefix + matrix_name]
   lowerCamelCase__  =     state_dict[prefix + matrix_name]
   lowerCamelCase__  =     state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 lowerCamelCase__  =     state_dict["entity_embeddings.entity_embeddings.weight"]
 lowerCamelCase__  =     entity_emb[entity_vocab["[MASK]"]]

 lowerCamelCase__  =     LukeModel(config=lowercase__).eval()

 lowerCamelCase__  ,	lowerCamelCase__  =     model.load_state_dict(lowercase__				,     strict=lowercase__)
 if not (len(lowercase__) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(F'''Missing keys {", ".join(lowercase__)}. Expected only missing embeddings.position_ids''')
 if not (all(key.startswith("entity_predictions") or key.startswith("lm_head") for key in unexpected_keys)):
  raise ValueError(
      "Unexpected keys"
      F''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions") or key.startswith("lm_head"))])}''')

 # Check outputs
 lowerCamelCase__  =     LukeTokenizer.from_pretrained(lowercase__				,     task="entity_classification")

 lowerCamelCase__  =     (
     "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
     " new world number one avoid a humiliating second- round exit at Wimbledon ."
 )
 lowerCamelCase__  =     (39, 42)
 lowerCamelCase__  =     tokenizer(lowercase__				,     entity_spans=[span]				,     add_prefix_space=lowercase__				,     return_tensors="pt")

 lowerCamelCase__  =     model(**lowercase__)

 # Verify word hidden states
 if model_size == "large":
  lowerCamelCase__  =     torch.Size((1, 42, 1024))
  lowerCamelCase__  =     torch.tensor(
      [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]])
 else:  # base
  lowerCamelCase__  =     torch.Size((1, 42, 768))
  lowerCamelCase__  =     torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]])

 if not (outputs.last_hidden_state.shape == expected_shape):
  raise ValueError(
      F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''')
 if not torch.allclose(outputs.last_hidden_state[0, :3, :3]				,     lowercase__				,     atol=1E-4):
  raise ValueError

 # Verify entity hidden states
 if model_size == "large":
  lowerCamelCase__  =     torch.Size((1, 1, 1024))
  lowerCamelCase__  =     torch.tensor([[0.0_466, -0.0_106, -0.0_179]])
 else:  # base
  lowerCamelCase__  =     torch.Size((1, 1, 768))
  lowerCamelCase__  =     torch.tensor([[0.1_457, 0.1_044, 0.0_174]])

 if not (outputs.entity_last_hidden_state.shape != expected_shape):
  raise ValueError(
      F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
      F''' {expected_shape}''')
 if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]				,     lowercase__				,     atol=1E-4):
  raise ValueError

 # Finally, save our PyTorch model and tokenizer
 print("Saving PyTorch model to {}".format(lowercase__))
 model.save_pretrained(lowercase__)






def    lowerCamelCase_     (    lowercase__):
 lowerCamelCase__  =     {}
 with open(lowercase__				,     "r"				,     encoding="utf-8") as f:
  for index, line in enumerate(lowercase__):
   lowerCamelCase__  ,	lowerCamelCase__  =     line.rstrip().split("\t")
   lowerCamelCase__  =     index

 return entity_vocab


if __name__ == "__main__":
 __A							:							List[str]  							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
 parser.add_argument(
     """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
 )
 parser.add_argument(
     """--entity_vocab_path""",
     default=None,
     type=str,
     help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
 )
 parser.add_argument(
     """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
 )
 __A							:							Optional[Any]  							=		parser.parse_args()
 convert_luke_checkpoint(
     args.checkpoint_path,
     args.metadata_path,
     args.entity_vocab_path,
     args.pytorch_dump_folder_path,
     args.model_size,
 )









'''simple docstring'''





import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
 from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
 __A							:							List[Any]  							=		True
 from torch.cuda.amp import autocast


__A							:							List[Any]  							=		logging.getLogger(__name__)
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,       )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Whether to freeze the feature extractor layers of the model."}      )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Whether to log verbose messages or not."} ,       )
    lowerCAmelCase__      				=							field(
        default=2.0 ,       metadata={"help": "Maximum temperature for gumbel softmax."}      )
    lowerCAmelCase__      				=							field(
        default=0.5 ,       metadata={"help": "Minimum temperature for gumbel softmax."}      )
    lowerCAmelCase__      				=							field(
        default=0.999995 ,       metadata={"help": "Decay of gumbel temperature during training."}      )






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,     datefmt="%m/%d/%Y %H:%M:%S"				,     handlers=[logging.StreamHandler(sys.stdout)]				,     )
 lowerCamelCase__  =     logging.WARNING
 if model_args.verbose_logging:
  lowerCamelCase__  =     logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank):
  lowerCamelCase__  =     logging.INFO
 logger.setLevel(lowercase__)
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "The name of the dataset to use (via the datasets library)."}      )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
    lowerCAmelCase__      				=							field(
        default="train" ,       metadata={
            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
        } ,       )
    lowerCAmelCase__      				=							field(
        default="validation" ,       metadata={
            "help": (
                "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
            )
        } ,       )
    lowerCAmelCase__      				=							field(
        default="file" ,       metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} ,       )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Overwrite the cached preprocessed datasets or not."}      )
    lowerCAmelCase__      				=							field(
        default=1 ,       metadata={
            "help": "The percentage of the train set used as validation set in case there's no validation split"
        } ,       )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "The number of processes to use for the preprocessing."} ,       )
    lowerCAmelCase__      				=							field(
        default=20.0 ,       metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}      )
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							42
    lowerCAmelCase__      				=							42
    lowerCAmelCase__      				=							"longest"
    lowerCAmelCase__      				=							None
    lowerCAmelCase__      				=							None

    def __call__(					self     :     Optional[int]				,     __lowerCamelCase     :     List[Dict[str, Union[List[int], torch.Tensor]]]     )							->							Dict[str, torch.Tensor]:

     '''simple docstring'''




     lowerCamelCase__  =     self.feature_extractor.pad(
         __lowerCamelCase				,     max_length=self.max_length				,     padding=self.padding				,     pad_to_multiple_of=self.pad_to_multiple_of				,     return_tensors="pt"				,     )
     lowerCamelCase__  =     self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]     )

     lowerCamelCase__  =     batch["input_values"].shape[0]

     # make sure that no loss is computed on padded inputs
     if batch["attention_mask"] is not None:
      # compute real output lengths according to convolution formula
      lowerCamelCase__  =     self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1     )     ).to(
          torch.long     )

      lowerCamelCase__  =     torch.zeros(
          (batch_size, mask_indices_seq_length)				,     dtype=torch.long				,     device=batch["input_values"].device     )

      # these two operations makes sure that all values
      # before the output lengths indices are attended to
      lowerCamelCase__  =     1
      lowerCamelCase__  =     attention_mask.flip([-1]     ).cumsum(-1     ).flip([-1]     ).bool()

     # sample randomly masked indices
     lowerCamelCase__  =     _compute_mask_indices(
         (batch_size, mask_indices_seq_length)				,     self.model.config.mask_time_prob				,     self.model.config.mask_time_length				,     attention_mask=__lowerCamelCase				,     min_masks=2				,     )

     return batch
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def __init__(					self     :     str				,     *__lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     int=1				,     __lowerCamelCase     :     Optional[Any]=0				,     __lowerCamelCase     :     str=1.0				,     **__lowerCamelCase     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     super().__init__(*__lowerCamelCase				,     **__lowerCamelCase     )
     lowerCamelCase__  =     0
     lowerCamelCase__  =     max_gumbel_temp
     lowerCamelCase__  =     min_gumbel_temp
     lowerCamelCase__  =     gumbel_temp_decay




    def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     nn.Module				,     __lowerCamelCase     :     Dict[str, Union[torch.Tensor, Any]]     )							->							torch.Tensor:

     '''simple docstring'''




     model.train()
     lowerCamelCase__  =     self._prepare_inputs(__lowerCamelCase     )

     if self.use_amp:
      with autocast():
       lowerCamelCase__  =     self.compute_loss(__lowerCamelCase				,     __lowerCamelCase     )
     else:
      lowerCamelCase__  =     self.compute_loss(__lowerCamelCase				,     __lowerCamelCase     )

     if self.args.n_gpu > 1 or self.deepspeed:
      if model.module.config.ctc_loss_reduction == "mean":
       lowerCamelCase__  =     loss.mean()
      elif model.module.config.ctc_loss_reduction == "sum":
       lowerCamelCase__  =     loss.sum() / (inputs["mask_time_indices"]).sum()
      else:
       raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''     )

     if self.args.gradient_accumulation_steps > 1:
      lowerCamelCase__  =     loss / self.args.gradient_accumulation_steps

     if self.use_amp:
      self.scaler.scale(__lowerCamelCase     ).backward()
     elif self.use_apex:
      with amp.scale_loss(__lowerCamelCase				,     self.optimizer     ) as scaled_loss:
       scaled_loss.backward()
     elif self.deepspeed:
      self.deepspeed.backward(__lowerCamelCase     )
     else:
      loss.backward()

     self.num_update_step += 1
     # make sure gumbel softmax temperature is decayed
     if self.args.n_gpu > 1 or self.deepspeed:
      model.module.set_gumbel_temperature(
          max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step				,     self.min_gumbel_temp     )     )
     else:
      model.set_gumbel_temperature(
          max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step				,     self.min_gumbel_temp     )     )

     return loss.detach()






def    lowerCamelCase_     (    ):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 lowerCamelCase__  =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))

 lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     parser.parse_args_into_dataclasses()
 configure_logger(lowercase__				,     lowercase__)

 # Downloading and loading a dataset from the hub.
 lowerCamelCase__  =     load_dataset(data_args.dataset_name				,     data_args.dataset_config_name				,     cache_dir=model_args.cache_dir)

 if "validation" not in datasets.keys():
  # make sure only "validation" and "train" keys remain"
  lowerCamelCase__  =     DatasetDict()
  lowerCamelCase__  =     load_dataset(
      data_args.dataset_name				,     data_args.dataset_config_name				,     split=F'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'''				,     cache_dir=model_args.cache_dir				,     )
  lowerCamelCase__  =     load_dataset(
      data_args.dataset_name				,     data_args.dataset_config_name				,     split=F'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'''				,     cache_dir=model_args.cache_dir				,     )
 else:
  # make sure only "validation" and "train" keys remain"
  lowerCamelCase__  =     DatasetDict()
  lowerCamelCase__  =     load_dataset(
      data_args.dataset_name				,     data_args.dataset_config_name				,     split="validation"				,     cache_dir=model_args.cache_dir				,     )
  lowerCamelCase__  =     load_dataset(
      data_args.dataset_name				,     data_args.dataset_config_name				,     split=F'''{data_args.train_split_name}'''				,     cache_dir=model_args.cache_dir				,     )

 # only normalized-inputs-training is supported
 lowerCamelCase__  =     WavaVecaFeatureExtractor.from_pretrained(
     model_args.model_name_or_path				,     cache_dir=model_args.cache_dir				,     do_normalize=lowercase__)

 def prepare_dataset(lowercase__):
  # check that all files have the correct sampling rate
  lowerCamelCase__  ,	lowerCamelCase__  =     librosa.load(batch[data_args.speech_file_column]				,     sr=feature_extractor.sampling_rate)
  return batch

 # load audio files into numpy arrays
 lowerCamelCase__  =     datasets.map(
     lowercase__				,     num_proc=data_args.preprocessing_num_workers				,     remove_columns=datasets["train"].column_names)

 # filter audio files that are too long
 lowerCamelCase__  =     vectorized_datasets.filter(
     lambda lowercase__: len(data["speech"]) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate))

 def normalize(lowercase__):
  return feature_extractor(batch["speech"]				,     sampling_rate=feature_extractor.sampling_rate)

 # normalize and transform to `BatchFeatures`
 lowerCamelCase__  =     vectorized_datasets.map(
     lowercase__				,     batched=lowercase__				,     num_proc=data_args.preprocessing_num_workers				,     load_from_cache_file=not data_args.overwrite_cache				,     remove_columns=vectorized_datasets["train"].column_names				,     )

 # pretraining is only supported for "newer" stable layer norm architecture
 # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
 lowerCamelCase__  =     WavaVecaConfig.from_pretrained(
     model_args.model_name_or_path				,     cache_dir=model_args.cache_dir				,     gradient_checkpointing=training_args.gradient_checkpointing				,     )

 if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
  raise ValueError(
      "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
      " ``config.feat_extract_norm='layer'")

 lowerCamelCase__  =     WavaVecaForPreTraining(lowercase__)

 lowerCamelCase__  =     DataCollatorForWavaVecaPretraining(model=lowercase__				,     feature_extractor=lowercase__)

 lowerCamelCase__  =     WavaVecaPreTrainer(
     model=lowercase__				,     data_collator=lowercase__				,     args=lowercase__				,     train_dataset=vectorized_datasets["train"]				,     eval_dataset=vectorized_datasets["validation"]				,     tokenizer=lowercase__				,     max_gumbel_temp=model_args.max_gumbel_temperature				,     min_gumbel_temp=model_args.min_gumbel_temperature				,     gumbel_temp_decay=model_args.gumbel_temperature_decay				,     )
 trainer.train()


if __name__ == "__main__":
 main()

