




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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_=None   )							->						Tuple:

    """simple docstring"""


    __UpperCamelCase			=    tqdm(lowercase_   )
    for i, batch in enumerate(lowercase_   ):
        with tf.GradientTape() as tape:
            __UpperCamelCase		,   __UpperCamelCase			=    batch
            __UpperCamelCase			=    model(lowercase_   )
            __UpperCamelCase			=    loss(lowercase_       ,  outputs.logits   )

        if SDP_ENABLED:
            __UpperCamelCase			=    sdp.DistributedGradientTape(lowercase_       ,  sparse_as_dense=lowercase_   )

        __UpperCamelCase			=    tape.gradient(lowercase_       ,  model.trainable_variables   )
        opt.apply_gradients(zip(lowercase_       ,  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

    __UpperCamelCase			=    {'''loss''': loss_value.numpy()}
    return train_results




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

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    load_dataset('''imdb'''       ,  split=['''train''', '''test''']   )

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

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

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

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

    if SDP_ENABLED:
        __UpperCamelCase			=    tf_train_dataset.shard(sdp.size()       ,  sdp.rank()   )
        __UpperCamelCase			=    tf_test_dataset.shard(sdp.size()       ,  sdp.rank()   )
    __UpperCamelCase			=    tf_train_dataset.batch(lowercase_       ,  drop_remainder=lowercase_   )
    __UpperCamelCase			=    tf_test_dataset.batch(lowercase_       ,  drop_remainder=lowercase_   )

    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=16)
 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        __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)






