

import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
  _a									=  True
  _a									=  '''p3dn.24xlarge'''
  import smdistributed.dataparallel.tensorflow as sdp
else:
  _a									=  False







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Dict       ,   SCREAMING_SNAKE_CASE						:     str=None						)       ->       Dict:

 """simple docstring"""






 __lowerCAmelCase:  Any        = tqdm(SCREAMING_SNAKE_CASE						)
 for i, batch in enumerate(SCREAMING_SNAKE_CASE						):
  with tf.GradientTape() as tape:
   __lowerCAmelCase      ,				__lowerCAmelCase:  int        = batch
   __lowerCAmelCase:  List[Any]        = model(SCREAMING_SNAKE_CASE						)
   __lowerCAmelCase:  Tuple        = loss(SCREAMING_SNAKE_CASE       ,   outputs.logits						)

  if SDP_ENABLED:
   __lowerCAmelCase:  List[str]        = sdp.DistributedGradientTape(SCREAMING_SNAKE_CASE       ,   sparse_as_dense=SCREAMING_SNAKE_CASE						)

  __lowerCAmelCase:  List[str]        = tape.gradient(SCREAMING_SNAKE_CASE       ,   model.trainable_variables						)
  opt.apply_gradients(zip(SCREAMING_SNAKE_CASE       ,   model.trainable_variables						)						)

  pbar.set_description(f'''Loss: {loss_value:.4f}'''						)

  if SDP_ENABLED and i == 0:
   sdp.broadcast_variables(model.variables       ,   root_rank=0						)
   sdp.broadcast_variables(opt.variables()       ,   root_rank=0						)

  if max_steps and i >= max_steps:
   break

 __lowerCAmelCase:  Optional[Any]        = {'loss': loss_value.numpy()}
 return train_results







def _a					(			SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase      ,				__lowerCAmelCase:  List[Any]        = load_dataset('imdb'       ,   split=['train', 'test']						)

 # Preprocess train dataset
 __lowerCAmelCase:  List[str]        = train_dataset.map(
     lambda SCREAMING_SNAKE_CASE						: tokenizer(e['text']       ,   truncation=SCREAMING_SNAKE_CASE       ,   padding='max_length'						)       ,   batched=SCREAMING_SNAKE_CASE						)
 train_dataset.set_format(type='tensorflow'       ,   columns=['input_ids', 'attention_mask', 'label']						)

 __lowerCAmelCase:  List[str]        = {
     x: train_dataset[x].to_tensor(default_value=0       ,   shape=[None, tokenizer.model_max_length]						)
     for x in ['input_ids', 'attention_mask']
 }
 __lowerCAmelCase:  Optional[int]        = tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])						)

 # Preprocess test dataset
 __lowerCAmelCase:  Union[str, Any]        = test_dataset.map(
     lambda SCREAMING_SNAKE_CASE						: tokenizer(e['text']       ,   truncation=SCREAMING_SNAKE_CASE       ,   padding='max_length'						)       ,   batched=SCREAMING_SNAKE_CASE						)
 test_dataset.set_format(type='tensorflow'       ,   columns=['input_ids', 'attention_mask', 'label']						)

 __lowerCAmelCase:  List[Any]        = {
     x: test_dataset[x].to_tensor(default_value=0       ,   shape=[None, tokenizer.model_max_length]						)
     for x in ['input_ids', 'attention_mask']
 }
 __lowerCAmelCase:  Any        = tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])						)

 if SDP_ENABLED:
  __lowerCAmelCase:  Tuple        = tf_train_dataset.shard(sdp.size()       ,   sdp.rank()						)
  __lowerCAmelCase:  Dict        = tf_test_dataset.shard(sdp.size()       ,   sdp.rank()						)
 __lowerCAmelCase:  Tuple        = tf_train_dataset.batch(SCREAMING_SNAKE_CASE       ,   drop_remainder=SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  List[Any]        = tf_test_dataset.batch(SCREAMING_SNAKE_CASE       ,   drop_remainder=SCREAMING_SNAKE_CASE						)

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
  _a									=  argparse.ArgumentParser()

  # Hyperparameters sent by the client are passed as command-line arguments to the script.
  parser.add_argument('''--epochs''', type=int, default=3)
  parser.add_argument('''--per_device_train_batch_size''', type=int, default=1_6)
  parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
  parser.add_argument('''--model_name_or_path''', type=str)
  parser.add_argument('''--learning_rate''', type=str, default=5E-5)
  parser.add_argument('''--do_train''', type=bool, default=True)
  parser.add_argument('''--do_eval''', type=bool, default=True)
  parser.add_argument('''--output_dir''', type=str)
  parser.add_argument('''--max_steps''', type=int, default=None)

  # Data, model, and output directories
  parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
  parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
  parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

  _a			,      _a									=  parser.parse_known_args()

  # Set up logging
  _a									=  logging.getLogger(__name__)

  logging.basicConfig(
      level=logging.getLevelName('''INFO'''),
      handlers=[logging.StreamHandler(sys.stdout)],
      format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
  )

  if SDP_ENABLED:
    sdp.init()

    _a									=  tf.config.experimental.list_physical_devices('''GPU''')
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
    if gpus:
      tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
  _a									=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  _a									=  AutoTokenizer.from_pretrained(args.model_name_or_path)

  # get datasets
  _a			,      _a									=  get_datasets(
      tokenizer=tokenizer,
      train_batch_size=args.per_device_train_batch_size,
      eval_batch_size=args.per_device_eval_batch_size,
  )

  # fine optimizer and loss
  _a									=  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  _a									=  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  _a									=  [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  # Training
  if args.do_train:
    # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
    _a									=  time.time()
    _a									=  fit(
        model,
        loss,
        optimizer,
        tf_train_dataset,
        args.epochs,
        args.per_device_train_batch_size,
        max_steps=args.max_steps,
    )
    _a									=  time.time() - start_train_time
    logger.info('''*** Train ***''')
    logger.info(f"train_runtime = {end_train_time}")

    _a									=  os.path.join(args.output_dir, '''train_results.txt''')

    if not SDP_ENABLED or sdp.rank() == 0:
      with open(output_eval_file, '''w''') as writer:
        logger.info('''***** Train results *****''')
        logger.info(train_results)
        for key, value in train_results.items():
          logger.info(f"  {key} = {value}")
          writer.write(f"{key} = {value}\n")

    # Evaluation
  if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
    _a									=  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
    logger.info('''*** Evaluate ***''')

    _a									=  os.path.join(args.output_dir, '''eval_results.txt''')

    with open(output_eval_file, '''w''') as writer:
      logger.info('''***** Eval results *****''')
      logger.info(result)
      for key, value in result.items():
        logger.info(f"  {key} = {value}")
        writer.write(f"{key} = {value}\n")

    # Save result
  if SDP_ENABLED:
    if sdp.rank() == 0:
      model.save_pretrained(args.output_dir)
      tokenizer.save_pretrained(args.output_dir)
  else:
    model.save_pretrained(args.output_dir)
    tokenizer.save_pretrained(args.output_dir)








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 _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       str:

 """simple docstring"""






 for attribute in key.split('.'						):
  __lowerCAmelCase:  Optional[int]        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 if weight_type is not None:
  __lowerCAmelCase:  Dict        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						).shape
 else:
  __lowerCAmelCase:  str        = 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":
  __lowerCAmelCase:  str        = value
 elif weight_type == "weight_g":
  __lowerCAmelCase:  List[str]        = value
 elif weight_type == "weight_v":
  __lowerCAmelCase:  List[Any]        = value
 elif weight_type == "bias":
  __lowerCAmelCase:  Optional[int]        = value
 else:
  __lowerCAmelCase:  int        = value

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







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Any						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = []
 __lowerCAmelCase:  Optional[int]        = fairseq_model.state_dict()

 __lowerCAmelCase:  int        = hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCAmelCase:  List[str]        = False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   hf_model.config.feat_extract_norm == 'group'       ,   )
   __lowerCAmelCase:  List[str]        = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCAmelCase:  List[Any]        = mapped_key

    if key in name:
     __lowerCAmelCase:  List[Any]        = True
     if "*" in mapped_key:
      __lowerCAmelCase:  Any        = name.split(SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
      __lowerCAmelCase:  Dict        = mapped_key.replace('*'       ,   SCREAMING_SNAKE_CASE						)
     if "weight_g" in name:
      __lowerCAmelCase:  str        = 'weight_g'
     elif "weight_v" in name:
      __lowerCAmelCase:  Tuple        = 'weight_v'
     elif "weight" in name:
      __lowerCAmelCase:  Tuple        = 'weight'
     elif "bias" in name:
      __lowerCAmelCase:  Optional[Any]        = 'bias'
     else:
      __lowerCAmelCase:  Optional[Any]        = None
     set_recursively(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE						)

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







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     List[str]						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = full_name.split('conv_layers.'						)[-1]
 __lowerCAmelCase:  Optional[Any]        = name.split('.'						)
 __lowerCAmelCase:  List[str]        = int(items[0]						)
 __lowerCAmelCase:  str        = 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.'''
   )
   __lowerCAmelCase:  Optional[int]        = 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.'''
   )
   __lowerCAmelCase:  Tuple        = 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."
   )
   __lowerCAmelCase:  List[Any]        = 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.'''
   )
   __lowerCAmelCase:  Any        = value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  str        = HubertConfig()
 __lowerCAmelCase:  Any        = model.config

 __lowerCAmelCase:  str        = fs_config.activation_dropout
 __lowerCAmelCase:  Optional[Any]        = False
 __lowerCAmelCase:  Tuple        = fs_config.attention_dropout
 __lowerCAmelCase:  Tuple        = False
 __lowerCAmelCase:  Tuple        = eval(fs_config.extractor_conv_feature_layers						)
 __lowerCAmelCase:  Optional[int]        = [x[0] for x in conv_layers]
 __lowerCAmelCase:  Dict        = [x[1] for x in conv_layers]
 __lowerCAmelCase:  List[str]        = [x[2] for x in conv_layers]
 __lowerCAmelCase:  Dict        = 'gelu'
 __lowerCAmelCase:  List[str]        = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 __lowerCAmelCase:  int        = False
 __lowerCAmelCase:  Dict        = 0.0
 __lowerCAmelCase:  str        = 0.0
 __lowerCAmelCase:  List[Any]        = fs_config.activation_fn
 __lowerCAmelCase:  str        = fs_config.dropout
 __lowerCAmelCase:  List[str]        = fs_config.encoder_embed_dim
 __lowerCAmelCase:  str        = 0.0_2
 __lowerCAmelCase:  List[Any]        = fs_config.encoder_ffn_embed_dim
 __lowerCAmelCase:  List[str]        = 1E-5
 __lowerCAmelCase:  Optional[Any]        = 0.0
 __lowerCAmelCase:  int        = fs_config.encoder_attention_heads
 __lowerCAmelCase:  Any        = fs_config.conv_pos_groups
 __lowerCAmelCase:  str        = fs_config.conv_pos
 __lowerCAmelCase:  Optional[int]        = len(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = fs_config.encoder_layers

 return config







@torch.no_grad()
def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]=None						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = distilhubert().model.model

 if config_path is not None:
  __lowerCAmelCase:  Dict        = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
 else:
  __lowerCAmelCase:  Union[str, Any]        = convert_config(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Any        = model.eval()

 __lowerCAmelCase:  Tuple        = WavaVecaFeatureExtractor(
     feature_size=1       ,   sampling_rate=1_60_00       ,   padding_value=0       ,   do_normalize=SCREAMING_SNAKE_CASE       ,   return_attention_mask=SCREAMING_SNAKE_CASE       ,   )
 __lowerCAmelCase:  Tuple        = HubertModel(SCREAMING_SNAKE_CASE						)

 recursively_load_weights(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE						)


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)





