


'''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[str]										= True
   A      :     Union[str, Any]										= '''p3dn.24xlarge'''
   import smdistributed.dataparallel.tensorflow as sdp
else:
   A      :     List[str]										= False






def       lowerCAmelCase__       (				lowerCamelCase     :			Optional[int]  ,lowerCamelCase     :			List[Any]  ,lowerCamelCase     :			str  ,lowerCamelCase     :			int  ,lowerCamelCase     :			Tuple  ,lowerCamelCase     :			Optional[Any]  ,lowerCamelCase     :			Any=None   ):
       _A     :	int      =					tqdm(lowerCamelCase   )
       for i, batch in enumerate(lowerCamelCase   ):
              with tf.GradientTape() as tape:
                     _A  , _A     :	Optional[Any]      =					batch
                     _A     :	int      =					model(lowerCamelCase   )
                     _A     :	Tuple      =					loss(lowerCamelCase  ,outputs.logits   )

              if SDP_ENABLED:
                     _A     :	List[Any]      =					sdp.DistributedGradientTape(lowerCamelCase  ,sparse_as_dense=lowerCamelCase   )

              _A     :	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

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






def       lowerCAmelCase__       (				lowerCamelCase     :			Tuple  ,lowerCamelCase     :			Dict  ,lowerCamelCase     :			str   ):
       # Load dataset
       _A  , _A     :	int      =					load_dataset('imdb'  ,split=['train', 'test']   )

       # Preprocess train dataset
       _A     :	Union[str, Any]      =					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']   )

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

       # Preprocess test dataset
       _A     :	str      =					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']   )

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

       if SDP_ENABLED:
              _A     :	Dict      =					tf_train_dataset.shard(sdp.size()  ,sdp.rank()   )
              _A     :	Any      =					tf_test_dataset.shard(sdp.size()  ,sdp.rank()   )
       _A     :	int      =					tf_train_dataset.batch(lowerCamelCase  ,drop_remainder=lowerCamelCase   )
       _A     :	Any      =					tf_test_dataset.batch(lowerCamelCase  ,drop_remainder=lowerCamelCase   )

       return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
   A      :     Tuple										= 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[Any]										= parser.parse_known_args()

   # Set up logging
   A      :     List[str]										= 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      :     Any										= 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      :     int										= TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A      :     Optional[int]										= AutoTokenizer.from_pretrained(args.model_name_or_path)

   # get datasets
   A						,					A      :     int										= 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      :     Union[str, Any]										= tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   A      :     Optional[int]										= tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   A      :     Optional[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      :     Tuple										= time.time()
      A      :     Dict										= fit(
          model,
          loss,
          optimizer,
          tf_train_dataset,
          args.epochs,
          args.per_device_train_batch_size,
          max_steps=args.max_steps,
      )
      A      :     str										= 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      :     Optional[int]										= 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 sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class  __lowerCamelCase :
    """simple docstring"""


    def __init__(  self			:							str      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							List[Any]=sys.maxsize):
           _A     :	Tuple      =					'bilinear'
           _A     :	List[Any]      =					max_size
           _A     :	Optional[int]      =					short_edge_length


    def __call__(  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]):
           _A     :	Dict      =					[]
           for img in imgs:
                  _A  , _A     :	Dict      =					img.shape[:2]
                  # later: provide list and randomly choose index for resize
                  _A     :	int      =					np.random.randint(self.short_edge_length[0]      ,							self.short_edge_length[1] + 1)
                  if size == 0:
                         return img
                  _A     :	Dict      =					size * 1.0 / min(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                  if h < w:
                         _A  , _A     :	Dict      =					size, scale * w
                  else:
                         _A  , _A     :	Optional[Any]      =					scale * h, size
                  if max(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE) > self.max_size:
                         _A     :	List[Any]      =					self.max_size * 1.0 / max(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                         _A     :	Dict      =					newh * scale
                         _A     :	Tuple      =					neww * scale
                  _A     :	List[Any]      =					int(neww + 0.5)
                  _A     :	int      =					int(newh + 0.5)

                  if img.dtype == np.uinta:
                         _A     :	List[Any]      =					Image.fromarray(SCREAMING_SNAKE_CASE)
                         _A     :	List[str]      =					pil_image.resize((neww, newh)      ,							PILImageResampling.BILINEAR)
                         _A     :	Dict      =					np.asarray(SCREAMING_SNAKE_CASE)
                  else:
                         _A     :	List[str]      =					img.permute(2      ,							0      ,							1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
                         _A     :	int      =					nn.functional.interpolate(
                             SCREAMING_SNAKE_CASE      ,							(newh, neww)      ,							mode=self.interp_method      ,							align_corners=SCREAMING_SNAKE_CASE).squeeze(0)
                  img_augs.append(SCREAMING_SNAKE_CASE)

           return img_augs




class  __lowerCamelCase :
    """simple docstring"""


    def __init__(  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							int):
           _A     :	Dict      =					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,							cfg.INPUT.MAX_SIZE_TEST)
           _A     :	Dict      =					cfg.INPUT.FORMAT
           _A     :	Union[str, Any]      =					cfg.SIZE_DIVISIBILITY
           _A     :	Optional[int]      =					cfg.PAD_VALUE
           _A     :	str      =					cfg.INPUT.MAX_SIZE_TEST
           _A     :	List[str]      =					cfg.MODEL.DEVICE
           _A     :	int      =					torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)      ,							1      ,							1)
           _A     :	int      =					torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)      ,							1      ,							1)
           _A     :	Tuple      =					lambda SCREAMING_SNAKE_CASE: (x - self.pixel_mean) / self.pixel_std


    def    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	Any      =					tuple(max(SCREAMING_SNAKE_CASE) for s in zip(*[img.shape for img in images]))
           _A     :	str      =					[im.shape[-2:] for im in images]
           _A     :	int      =					[
               nn.functional.pad(
                   SCREAMING_SNAKE_CASE      ,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,							value=self.pad_value      ,							)
               for size, im in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           ]

           return torch.stack(SCREAMING_SNAKE_CASE), torch.tensor(SCREAMING_SNAKE_CASE)


    def __call__(  self			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Any=False):
           with torch.no_grad():
                  if not isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Dict      =					[images]
                  if single_image:
                         assert len(SCREAMING_SNAKE_CASE) == 1
                  for i in range(len(SCREAMING_SNAKE_CASE)):
                         if isinstance(images[i]      ,							torch.Tensor):
                                images.insert(SCREAMING_SNAKE_CASE      ,							images.pop(SCREAMING_SNAKE_CASE).to(self.device).float())
                         elif not isinstance(images[i]      ,							torch.Tensor):
                                images.insert(
                                    SCREAMING_SNAKE_CASE      ,							torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE)      ,							input_format=self.input_format))
                                    .to(self.device)
                                    .float()      ,							)
            # resize smallest edge
                  _A     :	List[Any]      =					torch.tensor([im.shape[:2] for im in images])
                  _A     :	Optional[Any]      =					self.aug(SCREAMING_SNAKE_CASE)
                  # transpose images and convert to torch tensors
                  # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                  # now normalize before pad to avoid useless arithmetic
                  _A     :	List[str]      =					[self.normalizer(SCREAMING_SNAKE_CASE) for x in images]
                  # now pad them to do the following operations
                  _A  , _A     :	str      =					self.pad(SCREAMING_SNAKE_CASE)
                  # Normalize

                  if self.size_divisibility > 0:
                         raise NotImplementedError()
                  # pad
                  _A     :	Optional[int]      =					torch.true_divide(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                  if single_image:
                         return images[0], sizes[0], scales_yx[0]
                  else:
                         return images, sizes, scales_yx






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			Union[str, Any]   ):
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes






def       lowerCAmelCase__       (				lowerCamelCase     :			Any  ,lowerCamelCase     :			Tuple[int, int]   ):
       assert torch.isfinite(lowerCamelCase   ).all(), "Box tensor contains infinite or NaN!"
       _A  , _A     :	Union[str, Any]      =					box_size
       tensor[:, 0].clamp_(min=0  ,max=lowerCamelCase   )
       tensor[:, 1].clamp_(min=0  ,max=lowerCamelCase   )
       tensor[:, 2].clamp_(min=0  ,max=lowerCamelCase   )
       tensor[:, 3].clamp_(min=0  ,max=lowerCamelCase   )






