




import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			List[str]			,							snake_case       :			int			,							snake_case       :			List[Any]			,							snake_case       :			Any=False			,							snake_case       :			Tuple=1024			,							snake_case       :			str=1024			,							snake_case       :			str=3.6			,							snake_case       :			Union[str, Any]=False			,							):
          __UpperCamelCase			=    tokenizer
          __UpperCamelCase			=    tokenizer.bos_token_id
          __UpperCamelCase			=    dataset
          __UpperCamelCase			=    seq_length
          __UpperCamelCase			=    0
          __UpperCamelCase			=    infinite
          __UpperCamelCase			=    0
          __UpperCamelCase			=    tokenized

          if self.tokenized:
              __UpperCamelCase			=    seq_length * num_of_sequences
              __UpperCamelCase			=    '''input_ids'''
          else:
              __UpperCamelCase			=    seq_length * chars_per_token * num_of_sequences
              __UpperCamelCase			=    '''content'''







      def __iter__(  self       :			Optional[int]					):
          __UpperCamelCase			=    iter(self.dataset					)
          __UpperCamelCase			=    True
          while more_examples:
              __UpperCamelCase		,   __UpperCamelCase			=    [], 0
              while True:
                  if buffer_len >= self.max_buffer_size:
                      break
                  try:
                      buffer.append(next(snake_case					)[self.content_field]					)
                      buffer_len += len(buffer[-1]					)
                  except StopIteration:
                      if self.infinite:
                          __UpperCamelCase			=    iter(self.dataset					)
                          self.epoch += 1
                          logger.info(F"Dataset epoch: {self.epoch}"					)
                      else:
                          __UpperCamelCase			=    False
                          break
              if self.tokenized:
                  __UpperCamelCase			=    buffer
              else:
                  __UpperCamelCase			=    self.tokenizer(snake_case			,							truncation=snake_case					)['''input_ids''']
              __UpperCamelCase			=    []
              for tokenized_input in tokenized_inputs:
                  all_token_ids.extend(tokenized_input + [self.concat_token_id]					)
              for i in range(0			,							len(snake_case					)			,							self.seq_length					):
                  __UpperCamelCase			=    all_token_ids[i : i + self.seq_length]
                  if len(snake_case					) == self.seq_length:
                      self.current_size += 1
                      yield torch.tensor(snake_case					)







      def    snake_case						(  self       :			Any			,							snake_case       :			Dict=1000					):
          return ShufflerIterDataPipe(self			,							buffer_size=snake_case					)







def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase			=    args.model_ckpt.split('''/'''   )[-1]
    __UpperCamelCase			=    logging.getLogger(__name__   )
    __UpperCamelCase			=    Path(args.save_dir   ) / '''log/'''
    log_dir.mkdir(exist_ok=lowercase_   )
    __UpperCamelCase			=    F"debug_{accelerator.process_index}.log"
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,  datefmt='''%m/%d/%Y %H:%M:%S'''       ,  level=logging.INFO       ,  handlers=[logging.FileHandler(log_dir / filename   ), logging.StreamHandler()]       ,  )
    if accelerator.is_main_process:  # we only want to setup logging once
        accelerator.init_trackers(lowercase_       ,  vars(lowercase_   )   )
        __UpperCamelCase			=    accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO   )
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
    else:
        __UpperCamelCase			=    ''''''
        logger.setLevel(logging.ERROR   )
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    return logger, run_name




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    {'''streaming''': True}
    __UpperCamelCase			=    load_dataset(args.dataset_name_train       ,  split='''train'''       ,  **lowercase_   )
    __UpperCamelCase			=    train_data.shuffle(buffer_size=args.shuffle_buffer       ,  seed=args.seed   )
    __UpperCamelCase			=    load_dataset(args.dataset_name_valid       ,  split='''train'''       ,  **lowercase_   )
    __UpperCamelCase			=    ConstantLengthDataset(
        lowercase_       ,  lowercase_       ,  infinite=lowercase_       ,  seq_length=args.seq_length       ,  tokenized=args.tokenized   )
    __UpperCamelCase			=    ConstantLengthDataset(
        lowercase_       ,  lowercase_       ,  infinite=lowercase_       ,  seq_length=args.seq_length       ,  tokenized=args.tokenized   )
    __UpperCamelCase			=    train_dataset.shuffle(buffer_size=args.shuffle_buffer   )
    __UpperCamelCase			=    DataLoader(lowercase_       ,  batch_size=args.train_batch_size       ,  shuffle=lowercase_   )
    __UpperCamelCase			=    DataLoader(lowercase_       ,  batch_size=args.valid_batch_size   )
    return train_dataloader, eval_dataloader




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    [], []
    for n, p in model.named_parameters():
        if any(nd in n for nd in no_decay   ):
            params_without_wd.append(lowercase_   )
        else:
            params_with_wd.append(lowercase_   )
    return [
        {"params": params_with_wd, "weight_decay": args.weight_decay},
        {"params": params_without_wd, "weight_decay": 0.0},
    ]




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Any:

    """simple docstring"""


    logger.info(F"Step {step}: {metrics}"   )
    if accelerator.is_main_process:
        accelerator.log(lowercase_       ,  lowercase_   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Tuple:

    """simple docstring"""


    __UpperCamelCase			=    accelerator.unwrap_model(lowercase_   ).config
    __UpperCamelCase			=    4 if args.gradient_checkpointing else 3
    __UpperCamelCase			=    args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    __UpperCamelCase			=    24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    __UpperCamelCase			=    factor * (
        1.0
        + (args.seq_length / (6.0 * config_model.n_embd))
        + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
    )
    __UpperCamelCase			=    flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						int:

    """simple docstring"""


    model.eval()
    __UpperCamelCase			=    []
    for step, batch in enumerate(lowercase_   ):
        with torch.no_grad():
            __UpperCamelCase			=    model(lowercase_       ,  labels=lowercase_   )
        __UpperCamelCase			=    outputs.loss.repeat(args.valid_batch_size   )
        losses.append(accelerator.gather(lowercase_   )   )
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    __UpperCamelCase			=    torch.cat(lowercase_   )
    __UpperCamelCase			=    losses[: eval_dataloader.dataset.current_size].mean()
    try:
        __UpperCamelCase			=    torch.exp(lowercase_   )
    except OverflowError:
        __UpperCamelCase			=    float('''inf'''   )
    return loss.item(), perplexity.item()


# Settings
a_ 		=  HfArgumentParser(TrainingArguments)
a_ 		=  parser.parse_args()

# Accelerator
a_ 		=  ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
a_ 		=  Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
a_ 		=  {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

a_ 		=  Namespace(**vars(args), **acc_state)
a_ 		=  accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
 a_ 		=  Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
a_		,     a_ 		=  setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
 hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
a_ 		=  AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
 model.gradient_checkpointing_enable()
a_ 		=  AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
a_		,     a_ 		=  create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
a_ 		=  AdamW(get_grouped_params(model, args), lr=args.learning_rate)
a_ 		=  get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)




def        __SCREAMING_SNAKE_CASE (	)							->						List[str]:

    """simple docstring"""


    return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
a_		,     a_		,     a_		,     a_ 		=  accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
 if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
  accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}""")
  accelerator.load_state(args.resume_from_checkpoint)
  a_ 		=  os.path.basename(args.resume_from_checkpoint)
 else:
  # Get the most recent checkpoint
  a_ 		=  [f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
  dirs.sort(key=os.path.getctime)
  a_ 		=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
 # Extract the step of the checkpoint to continue from there
 a_ 		=  os.path.splitext(path)[0]
 a_ 		=  int(training_difference.replace("step_", ""))

# Train model
model.train()
a_ 		=  0
a_ 		=  time.time()
a_ 		=  0
for step, batch in enumerate(train_dataloader, start=1):
 if args.resume_from_checkpoint and step < resume_step:
  continue  # we need to skip steps until we reach the resumed step
 a_ 		=  model(batch, labels=batch, use_cache=False).loss
 a_ 		=  accelerator.gather(loss.repeat(args.train_batch_size)).mean()
 loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
 log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
 a_ 		=  loss / args.gradient_accumulation_steps
 if step % args.gradient_accumulation_steps != 0:
  # Prevent backward from doing gradient all_reduce in every step
  if accelerator.distributed_type == DistributedType.MULTI_GPU:
   with model.no_sync():
    accelerator.backward(loss)
  else:
   accelerator.backward(loss)
 else:
  a_ 		=  get_lr()
  accelerator.backward(loss)
  accelerator.clip_grad_norm_(model.parameters(), 1.0)
  optimizer.step()
  lr_scheduler.step()
  optimizer.zero_grad()
  a_ 		=  time.time() - t_start
  a_ 		=  compute_tflops(elapsed_time, accelerator, args)
  log_metrics(
      step,
      {
          "steps": completed_steps,
          "loss/train": loss_tracking,
          "lr": lr,
          "tflops": tflops,
          "time_per_iteration": elapsed_time,
      },
  )
  a_ 		=  time.time()
  a_ 		=  0
  completed_steps += 1
 if step % args.save_checkpoint_steps == 0:
  logger.info("Evaluating and saving model checkpoint")
  a_		,     a_ 		=  evaluate(args)
  log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
  accelerator.wait_for_everyone()
  a_ 		=  os.path.join(args.save_dir, f"""step_{step}""")
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
   hf_repo.push_to_hub(commit_message=f"""step {step}""")
  model.train()
 if completed_steps >= args.max_train_steps:
  break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
a_		,     a_ 		=  evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
a_ 		=  accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
a_ 		=  os.path.join(args.save_dir, f"""step_{step}""")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
 hf_repo.push_to_hub(commit_message="final model")












import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

a_ 		=  {
    "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",
    "mask_emb": "masked_spec_embed",
}




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Any:

    """simple docstring"""


    for attribute in key.split('''.'''   ):
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   )

    if weight_type is not None:
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   ).shape
    else:
        __UpperCamelCase			=    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":
        __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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase			=    []
    __UpperCamelCase			=    fairseq_model.state_dict()

    __UpperCamelCase			=    hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase			=    False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  hf_model.config.feat_extract_norm == '''group'''       ,  )
            __UpperCamelCase			=    True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase			=    mapped_key

                if key in name:
                    __UpperCamelCase			=    True
                    if "*" in mapped_key:
                        __UpperCamelCase			=    name.split(lowercase_   )[0].split('''.'''   )[-2]
                        __UpperCamelCase			=    mapped_key.replace('''*'''       ,  lowercase_   )
                    if "weight_g" in name:
                        __UpperCamelCase			=    '''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase			=    '''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase			=    '''weight'''
                    elif "bias" in name:
                        __UpperCamelCase			=    '''bias'''
                    else:
                        __UpperCamelCase			=    None
                    set_recursively(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )
                continue
        if not is_used:
            unused_weights.append(lowercase_   )

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """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:
            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."
            )
            __UpperCamelCase			=    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."
            )
            __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:
            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."
            )
            __UpperCamelCase			=    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."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
    else:
        unused_weights.append(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    HubertConfig()
    __UpperCamelCase			=    model.config

    __UpperCamelCase			=    fs_config.activation_dropout
    __UpperCamelCase			=    False
    __UpperCamelCase			=    fs_config.attention_dropout
    __UpperCamelCase			=    False
    __UpperCamelCase			=    eval(fs_config.extractor_conv_feature_layers   )
    __UpperCamelCase			=    [x[0] for x in conv_layers]
    __UpperCamelCase			=    [x[1] for x in conv_layers]
    __UpperCamelCase			=    [x[2] for x in conv_layers]
    __UpperCamelCase			=    '''gelu'''
    __UpperCamelCase			=    '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    __UpperCamelCase			=    False
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    fs_config.activation_fn
    __UpperCamelCase			=    fs_config.dropout
    __UpperCamelCase			=    fs_config.encoder_embed_dim
    __UpperCamelCase			=    0.02
    __UpperCamelCase			=    fs_config.encoder_ffn_embed_dim
    __UpperCamelCase			=    1E-5
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    fs_config.encoder_attention_heads
    __UpperCamelCase			=    fs_config.conv_pos_groups
    __UpperCamelCase			=    fs_config.conv_pos
    __UpperCamelCase			=    len(lowercase_   )
    __UpperCamelCase			=    fs_config.encoder_layers

    return config




@torch.no_grad()
def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=None   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    distilhubert().model.model

    if config_path is not None:
        __UpperCamelCase			=    HubertConfig.from_pretrained(lowercase_   )
    else:
        __UpperCamelCase			=    convert_config(lowercase_   )
    __UpperCamelCase			=    model.eval()

    __UpperCamelCase			=    WavaVecaFeatureExtractor(
        feature_size=1       ,  sampling_rate=1_60_00       ,  padding_value=0       ,  do_normalize=lowercase_       ,  return_attention_mask=lowercase_       ,  )
    __UpperCamelCase			=    HubertModel(lowercase_   )

    recursively_load_weights(lowercase_       ,  lowercase_   )

    feature_extractor.save_pretrained(lowercase_   )
    hf_model.save_pretrained(lowercase_   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 a_ 		=  parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






