






"""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():
      A_			: List[Any]				=			True
      A_			: Optional[Any]				=			"p3dn.24xlarge"
      import smdistributed.dataparallel.tensorflow as sdp
else:
      A_			: Any				=			False






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase=None				):
 lowerCamelCase__     :			List[Any]     				=						tqdm(_lowerCamelCase				)
 for i, batch in enumerate(_lowerCamelCase				):
  with tf.GradientTape() as tape:
   lowerCamelCase__				,     lowerCamelCase__     :			str     				=						batch
   lowerCamelCase__     :			int     				=						model(_lowerCamelCase				)
   lowerCamelCase__     :			List[Any]     				=						loss(_lowerCamelCase			,   outputs.logits				)

  if SDP_ENABLED:
   lowerCamelCase__     :			Union[str, Any]     				=						sdp.DistributedGradientTape(_lowerCamelCase			,   sparse_as_dense=_lowerCamelCase				)

  lowerCamelCase__     :			List[str]     				=						tape.gradient(_lowerCamelCase			,   model.trainable_variables				)
  opt.apply_gradients(zip(_lowerCamelCase			,   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__     :			str     				=						{'loss': loss_value.numpy()}
 return train_results






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 # Load dataset
 lowerCamelCase__				,     lowerCamelCase__     :			Any     				=						load_dataset('imdb'			,   split=['train', 'test']				)

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

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

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

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

 if SDP_ENABLED:
  lowerCamelCase__     :			int     				=						tf_train_dataset.shard(sdp.size()			,   sdp.rank()				)
  lowerCamelCase__     :			str     				=						tf_test_dataset.shard(sdp.size()			,   sdp.rank()				)
 lowerCamelCase__     :			Optional[Any]     				=						tf_train_dataset.batch(_lowerCamelCase			,   drop_remainder=_lowerCamelCase				)
 lowerCamelCase__     :			Tuple     				=						tf_test_dataset.batch(_lowerCamelCase			,   drop_remainder=_lowerCamelCase				)

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
      A_			: Any				=			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_			: List[str]				=			parser.parse_known_args()

      # Set up logging
      A_			: Tuple				=			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_			: Tuple				=			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_			: List[Any]				=			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      A_			: int				=			AutoTokenizer.from_pretrained(args.model_name_or_path)

      # get datasets
      A_,       A_			: List[Any]				=			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_			: Optional[int]				=			tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      A_			: List[str]				=			tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      A_			: Union[str, Any]				=			[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_			: List[Any]				=			time.time()
            A_			: str				=			fit(
                model,
                loss,
                optimizer,
                tf_train_dataset,
                args.epochs,
                args.per_device_train_batch_size,
                max_steps=args.max_steps,
            )
            A_			: Optional[int]				=			time.time() - start_train_time
            logger.info("*** Train ***")
            logger.info(f"train_runtime = {end_train_time}")

            A_			: List[Any]				=			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_			: Union[str, Any]				=			model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
            logger.info("*** Evaluate ***")

            A_			: Union[str, Any]				=			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"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_xglm import XGLMTokenizer
else:
  _UpperCamelCase     			=  None


_UpperCamelCase     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase     			=  {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

_UpperCamelCase     			=  {
    """facebook/xglm-564M""": 2048,
}







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=			PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=			['''input_ids''', '''attention_mask''']
    SCREAMING_SNAKE_CASE		=			XGLMTokenizer






    def __init__( self	,A=None	,A=None	,A="<s>"	,A="</s>"	,A="</s>"	,A="<s>"	,A="<unk>"	,A="<pad>"	,**A	,):
          # Compatibility with the original tokenizer
          UpperCAmelCase							=       7
          UpperCAmelCase							=       [F'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

          UpperCAmelCase							=       kwargs.get("""additional_special_tokens"""	,[]					)
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              A	,tokenizer_file=A	,bos_token=A	,eos_token=A	,sep_token=A	,cls_token=A	,unk_token=A	,pad_token=A	,**A	,)

          UpperCAmelCase							=       vocab_file
          UpperCAmelCase							=       False if not self.vocab_file else True






    def 				_UpperCamelCase							( self	,A	,A = None					):

          if token_ids_a is None:
                return [self.sep_token_id] + token_ids_a
          UpperCAmelCase							=       [self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a






    def 				_UpperCamelCase							( self	,A	,A = None					):
          UpperCAmelCase							=       [self.sep_token_id]

          if token_ids_a is None:
                return len(sep + token_ids_a					) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a					) * [0]






    def 				_UpperCamelCase							( self	,A	,A = None					):
          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer."""					)

          if not os.path.isdir(A					):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''					)
                return
          UpperCAmelCase							=       os.path.join(
              A	,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

          if os.path.abspath(self.vocab_file					) != os.path.abspath(A					):
                copyfile(self.vocab_file	,A					)

          return (out_vocab_file,)



