




'''simple docstring'''







import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class      _a    (      unittest.TestCase					):
       @slow
       @unittest.skip('''Model is not available.'''							)
       def        A (      self		:      Dict							):



           '''simple docstring'''

           UpperCAmelCase     			=						'''nvidia/megatron-gpt2-345m/'''
           if "MYDIR" in os.environ:
               UpperCAmelCase     			=						os.path.join(os.environ['''MYDIR''']  ,							lowercase							)
           UpperCAmelCase     			=						GPTaLMHeadModel.from_pretrained(lowercase							)
           model.to(lowercase							)
           model.half()

           UpperCAmelCase     			=						torch.tensor(
               [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]  ,							device=lowercase  ,							dtype=torch.long  ,							)

           with torch.no_grad():
               UpperCAmelCase     			=						model(lowercase							).logits

           UpperCAmelCase     			=						torch.Size((1, 9, 50_257)							)
           self.assertEqual(output.shape  ,							lowercase							)

           UpperCAmelCase     			=						torch.tensor(
               [
                   4.9414,
                   -0.2920,
                   -1.2148,
                   -4.0273,
                   -0.5161,
                   -5.2109,
                   -1.2412,
                   -1.8301,
                   -1.7734,
                   -4.7148,
                   -0.2317,
                   -1.0811,
                   -2.1777,
                   0.4141,
                   -3.7969,
                   -4.0586,
                   -2.5332,
                   -3.3809,
                   4.3867,
               ]  ,							device=lowercase  ,							dtype=torch.half  ,							)

           for i in range(19							):
               UpperCAmelCase			,	UpperCAmelCase     			=						8 * i // 17, 2_792 * i  # along the diagonal
               UpperCAmelCase			,	UpperCAmelCase     			=						output[0, r, c], expected_diag[i]
               UpperCAmelCase     			=						f"row={r} col={c} computed={computed} expected={expected}"
               self.assertAlmostEqual(lowercase  ,							lowercase  ,							delta=1E-4  ,							msg=lowercase							)










'''simple docstring'''







import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


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=1)
   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)

   A       , A					       =parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A					       =16
   A					       =16

   # 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',
   )

   # Load model and tokenizer
   A					       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A					       =AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A       , A					       =load_dataset('imdb', split=['train', 'test'])
   A					       =train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
   A					       =test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

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

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

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

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

   A					       =time.time()
   A					       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A					       =time.time() - start_train_time

   logger.info('*** Train ***')
   logger.info(f"""train_runtime = {end_train_time}""")
   for key, value in train_results.history.items():
      logger.info(f"""  {key} = {value}""")




