

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



def   UpperCamelCase(   lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_=None				)      ->							Dict:





  '''simple docstring'''





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

    if SDP_ENABLED:
      snake_case_					= sdp.DistributedGradientTape(_A     ,   sparse_as_dense=_A				)

    snake_case_					= 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

  snake_case_					= {"""loss""": loss_value.numpy()}
  return train_results



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





  '''simple docstring'''





  snake_case_					= load_dataset("""imdb"""     ,   split=["""train""", """test"""]				)

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

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

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

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

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

  return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
    lowerCamelCase_					  =   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'''])

    lowerCamelCase_					  =   parser.parse_known_args()

    # Set up logging
    lowerCamelCase_					  =   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()

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

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

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

        lowerCamelCase_					  =   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)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__			:  Tuple					     =     {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Tuple					     =     [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__			:  Optional[int]					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




