





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   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE=None      )					->  Optional[int]:


      '''simple docstring'''


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

            if SDP_ENABLED:
                  SCREAMING_SNAKE_CASE						=		sdp.DistributedGradientTape(_SCREAMING_SNAKE_CASE					,		sparse_as_dense=_SCREAMING_SNAKE_CASE      )

            SCREAMING_SNAKE_CASE						=		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

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



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  Union[str, Any]:


      '''simple docstring'''


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

      # Preprocess train dataset
      SCREAMING_SNAKE_CASE						=		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"""]      )

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

      # Preprocess test dataset
      SCREAMING_SNAKE_CASE						=		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"""]      )

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

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

      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_  ,		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_  ,		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)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_		  =			{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		  =			["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		  =			[
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE_		  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


