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

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=None				):
     lowercase_       :							int  					=       tqdm(SCREAMING_SNAKE_CASE_				)
     for i, batch in enumerate(SCREAMING_SNAKE_CASE_				):
          with tf.GradientTape() as tape:
               lowercase_					,lowercase_       :							Union[str, Any]  					=       batch
               lowercase_       :							Union[str, Any]  					=       model(SCREAMING_SNAKE_CASE_				)
               lowercase_       :							Optional[int]  					=       loss(SCREAMING_SNAKE_CASE_	,							outputs.logits				)

          if SDP_ENABLED:
               lowercase_       :							Optional[int]  					=       sdp.DistributedGradientTape(SCREAMING_SNAKE_CASE_	,							sparse_as_dense=SCREAMING_SNAKE_CASE_				)

          lowercase_       :							Dict  					=       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

     lowercase_       :							Tuple  					=       {'loss': loss_value.numpy()}
     return train_results

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     # Load dataset
     lowercase_					,lowercase_       :							int  					=       load_dataset('imdb'	,							split=['train', 'test']				)

     # Preprocess train dataset
     lowercase_       :							Optional[int]  					=       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']				)

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

     # Preprocess test dataset
     lowercase_       :							Optional[int]  					=       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']				)

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

     if SDP_ENABLED:
          lowercase_       :							Dict  					=       tf_train_dataset.shard(sdp.size()	,							sdp.rank()				)
          lowercase_       :							Any  					=       tf_test_dataset.shard(sdp.size()	,							sdp.rank()				)
     lowercase_       :							Dict  					=       tf_train_dataset.batch(SCREAMING_SNAKE_CASE_	,							drop_remainder=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Optional[Any]  					=       tf_test_dataset.batch(SCREAMING_SNAKE_CASE_	,							drop_remainder=SCREAMING_SNAKE_CASE_				)

     return tf_train_dataset, tf_test_dataset


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=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'])

 _A					,			_A										= parser.parse_known_args()

 # 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',
 )

 if SDP_ENABLED:
  sdp.init()

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

 # get datasets
 _A					,			_A										= 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										= 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)

 # Training
 if args.do_train:
  # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
  _A										= time.time()
  _A										= fit(
      model,
      loss,
      optimizer,
      tf_train_dataset,
      args.epochs,
      args.per_device_train_batch_size,
      max_steps=args.max_steps,
  )
  _A										= time.time() - start_train_time
  logger.info('*** Train ***')
  logger.info(F"""train_runtime = {end_train_time}""")

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

  _A										= 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 dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							Union[List[PIL.Image.Image], np.ndarray]
   A				:							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline




