






'''simple docstring'''


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():
  SCREAMING_SNAKE_CASE_				      =							True
  SCREAMING_SNAKE_CASE_				      =							'p3dn.24xlarge'
  import smdistributed.dataparallel.tensorflow as sdp
else:
  SCREAMING_SNAKE_CASE_				      =							False






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__=None		):
 __a    :	str									=						tqdm(_A		)
 for i, batch in enumerate(_A		):
  with tf.GradientTape() as tape:
   __a			,    __a    :	Union[str, Any]									=						batch
   __a    :	int									=						model(_A		)
   __a    :	Union[str, Any]									=						loss(_A       ,		outputs.logits		)

  if SDP_ENABLED:
   __a    :	List[Any]									=						sdp.DistributedGradientTape(_A       ,		sparse_as_dense=_A		)

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

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






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a			,    __a    :	Optional[int]									=						load_dataset('imdb'       ,		split=['train', 'test']		)

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

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

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

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

 if SDP_ENABLED:
  __a    :	Union[str, Any]									=						tf_train_dataset.shard(sdp.size()       ,		sdp.rank()		)
  __a    :	int									=						tf_test_dataset.shard(sdp.size()       ,		sdp.rank()		)
 __a    :	Union[str, Any]									=						tf_train_dataset.batch(_A       ,		drop_remainder=_A		)
 __a    :	Optional[Any]									=						tf_test_dataset.batch(_A       ,		drop_remainder=_A		)

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_				      =							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"])

  SCREAMING_SNAKE_CASE_				      =							parser.parse_known_args()

  # Set up logging
  SCREAMING_SNAKE_CASE_				      =							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()

    SCREAMING_SNAKE_CASE_				      =							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
  SCREAMING_SNAKE_CASE_				      =							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  SCREAMING_SNAKE_CASE_				      =							AutoTokenizer.from_pretrained(args.model_name_or_path)

  # get datasets
  SCREAMING_SNAKE_CASE_				      =							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
  SCREAMING_SNAKE_CASE_				      =							tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  SCREAMING_SNAKE_CASE_				      =							tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  SCREAMING_SNAKE_CASE_				      =							[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)
    SCREAMING_SNAKE_CASE_				      =							time.time()
    SCREAMING_SNAKE_CASE_				      =							fit(
        model,
        loss,
        optimizer,
        tf_train_dataset,
        args.epochs,
        args.per_device_train_batch_size,
        max_steps=args.max_steps,
    )
    SCREAMING_SNAKE_CASE_				      =							time.time() - start_train_time
    logger.info("*** Train ***")
    logger.info(F"train_runtime = {end_train_time}")

    SCREAMING_SNAKE_CASE_				      =							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):
    SCREAMING_SNAKE_CASE_				      =							model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
    logger.info("*** Evaluate ***")

    SCREAMING_SNAKE_CASE_				      =							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)












'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							{
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   _A						=   'squeezebert'



   def __init__( self   ,   _A=30522   ,   _A=768   ,   _A=12   ,   _A=12   ,   _A=3072   ,   _A="gelu"   ,   _A=0.1   ,   _A=0.1   ,   _A=512   ,   _A=2   ,   _A=0.02   ,   _A=1E-1_2   ,   _A=0   ,   _A=768   ,   _A=4   ,   _A=4   ,   _A=4   ,   _A=1   ,   _A=4   ,   _A=4   ,   **_A   ,   )    ->   List[Any]:
    super().__init__(pad_token_id=_A   ,   **_A			)

    __a    :	Optional[int]									=						vocab_size
    __a    :	Optional[int]									=						hidden_size
    __a    :	int									=						num_hidden_layers
    __a    :	int									=						num_attention_heads
    __a    :	Tuple									=						hidden_act
    __a    :	Optional[Any]									=						intermediate_size
    __a    :	Any									=						hidden_dropout_prob
    __a    :	List[Any]									=						attention_probs_dropout_prob
    __a    :	List[str]									=						max_position_embeddings
    __a    :	Tuple									=						type_vocab_size
    __a    :	Tuple									=						initializer_range
    __a    :	List[str]									=						layer_norm_eps
    __a    :	str									=						embedding_size
    __a    :	Any									=						q_groups
    __a    :	Any									=						k_groups
    __a    :	Union[str, Any]									=						v_groups
    __a    :	List[str]									=						post_attention_groups
    __a    :	Dict									=						intermediate_groups
    __a    :	Optional[Any]									=						output_groups




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
     __a    :	Tuple									=						{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
     __a    :	List[Any]									=						{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]			)




