





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_		   =				logging.get_logger(__name__)


class   lowerCamelCase__  (					_A):



 """simple docstring"""

 a__				:   Tuple   =     ["pixel_values"]

 def __init__(						self				:				int ,	__lowerCAmelCase				:				bool = True ,	__lowerCAmelCase				:				Optional[Dict[str, int]] = None ,	__lowerCAmelCase				:				PILImageResampling = PILImageResampling.BILINEAR ,	__lowerCAmelCase				:				bool = True ,	__lowerCAmelCase				:				Union[int, float] = 1 / 2_55 ,	__lowerCAmelCase				:				bool = True ,	__lowerCAmelCase				:				Optional[Union[float, List[float]]] = None ,	__lowerCAmelCase				:				Optional[Union[float, List[float]]] = None ,	**__lowerCAmelCase				:				int ,	)      ->  None:
    super().__init__(**__lowerCAmelCase			)
    _A						     =					size if size is not None else {'''height''': 2_24, '''width''': 2_24}
    _A						     =					get_size_dict(__lowerCAmelCase			)
    _A						     =					do_resize
    _A						     =					do_rescale
    _A						     =					do_normalize
    _A						     =					size
    _A						     =					resample
    _A						     =					rescale_factor
    _A						     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    _A						     =					image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 						snake_case_			(						self				:				Optional[int] ,	__lowerCAmelCase				:				np.ndarray ,	__lowerCAmelCase				:				Dict[str, int] ,	__lowerCAmelCase				:				PILImageResampling = PILImageResampling.BILINEAR ,	__lowerCAmelCase				:				Optional[Union[str, ChannelDimension]] = None ,	**__lowerCAmelCase				:				Optional[Any] ,	)      ->  np.ndarray:
    _A						     =					get_size_dict(__lowerCAmelCase			)
    if "height" not in size or "width" not in size:
       raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''			)
    return resize(
        __lowerCAmelCase ,	size=(size['''height'''], size['''width''']) ,	resample=__lowerCAmelCase ,	data_format=__lowerCAmelCase ,	**__lowerCAmelCase			)

 def 						snake_case_			(						self				:				int ,	__lowerCAmelCase				:				np.ndarray ,	__lowerCAmelCase				:				float ,	__lowerCAmelCase				:				Optional[Union[str, ChannelDimension]] = None ,	**__lowerCAmelCase				:				Tuple			)      ->  np.ndarray:
    return rescale(__lowerCAmelCase ,	scale=__lowerCAmelCase ,	data_format=__lowerCAmelCase ,	**__lowerCAmelCase			)

 def 						snake_case_			(						self				:				Any ,	__lowerCAmelCase				:				np.ndarray ,	__lowerCAmelCase				:				Union[float, List[float]] ,	__lowerCAmelCase				:				Union[float, List[float]] ,	__lowerCAmelCase				:				Optional[Union[str, ChannelDimension]] = None ,	**__lowerCAmelCase				:				Union[str, Any] ,	)      ->  np.ndarray:
    return normalize(__lowerCAmelCase ,	mean=__lowerCAmelCase ,	std=__lowerCAmelCase ,	data_format=__lowerCAmelCase ,	**__lowerCAmelCase			)

 def 						snake_case_			(						self				:				Union[str, Any] ,	__lowerCAmelCase				:				ImageInput ,	__lowerCAmelCase				:				Optional[bool] = None ,	__lowerCAmelCase				:				Dict[str, int] = None ,	__lowerCAmelCase				:				PILImageResampling = None ,	__lowerCAmelCase				:				Optional[bool] = None ,	__lowerCAmelCase				:				Optional[float] = None ,	__lowerCAmelCase				:				Optional[bool] = None ,	__lowerCAmelCase				:				Optional[Union[float, List[float]]] = None ,	__lowerCAmelCase				:				Optional[Union[float, List[float]]] = None ,	__lowerCAmelCase				:				Optional[Union[str, TensorType]] = None ,	__lowerCAmelCase				:				Union[str, ChannelDimension] = ChannelDimension.FIRST ,	**__lowerCAmelCase				:				Dict ,	)      ->  Tuple:
    _A						     =					do_resize if do_resize is not None else self.do_resize
    _A						     =					do_rescale if do_rescale is not None else self.do_rescale
    _A						     =					do_normalize if do_normalize is not None else self.do_normalize
    _A						     =					resample if resample is not None else self.resample
    _A						     =					rescale_factor if rescale_factor is not None else self.rescale_factor
    _A						     =					image_mean if image_mean is not None else self.image_mean
    _A						     =					image_std if image_std is not None else self.image_std

    _A						     =					size if size is not None else self.size
    _A						     =					get_size_dict(__lowerCAmelCase			)

    _A						     =					make_list_of_images(__lowerCAmelCase			)

    if not valid_images(__lowerCAmelCase			):
       raise ValueError(
           '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
           '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

    if do_resize and size is None:
       raise ValueError('''Size must be specified if do_resize is True.'''			)

    if do_rescale and rescale_factor is None:
       raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

    # All transformations expect numpy arrays.
    _A						     =					[to_numpy_array(__lowerCAmelCase			) for image in images]

    if do_resize:
       _A						     =					[self.resize(image=__lowerCAmelCase ,	size=__lowerCAmelCase ,	resample=__lowerCAmelCase			) for image in images]

    if do_rescale:
       _A						     =					[self.rescale(image=__lowerCAmelCase ,	scale=__lowerCAmelCase			) for image in images]

    if do_normalize:
       _A						     =					[self.normalize(image=__lowerCAmelCase ,	mean=__lowerCAmelCase ,	std=__lowerCAmelCase			) for image in images]

    _A						     =					[to_channel_dimension_format(__lowerCAmelCase ,	__lowerCAmelCase			) for image in images]

    _A						     =					{'''pixel_values''': images}
    return BatchFeature(data=__lowerCAmelCase ,	tensor_type=__lowerCAmelCase			)








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():
   UpperCAmelCase_		   =				True
   UpperCAmelCase_		   =				"""p3dn.24xlarge"""
   import smdistributed.dataparallel.tensorflow as sdp
else:
   UpperCAmelCase_		   =				False

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

      if SDP_ENABLED:
         _A						     =					sdp.DistributedGradientTape(_snake_case  ,      sparse_as_dense=_snake_case							)

      _A						     =					tape.gradient(_snake_case  ,      model.trainable_variables							)
      opt.apply_gradients(zip(_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

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

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :Optional[Any]  ,      _snake_case  :List[str]							)     ->    Dict:
   # Load dataset
   _A     ,						_A						     =					load_dataset('''imdb'''  ,      split=['''train''', '''test''']							)

   # Preprocess train dataset
   _A						     =					train_dataset.map(
       lambda _snake_case							: tokenizer(e['''text''']  ,      truncation=_snake_case  ,      padding='''max_length'''							)  ,      batched=_snake_case							)
   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'''])							)

   # Preprocess test dataset
   _A						     =					test_dataset.map(
       lambda _snake_case							: tokenizer(e['''text''']  ,      truncation=_snake_case  ,      padding='''max_length'''							)  ,      batched=_snake_case							)
   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'''])							)

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

   return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
   UpperCAmelCase_		   =				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"""])

   UpperCAmelCase_   ,UpperCAmelCase_		   =				parser.parse_known_args()

   # Set up logging
   UpperCAmelCase_		   =				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()

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

   # get datasets
   UpperCAmelCase_   ,UpperCAmelCase_		   =				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
   UpperCAmelCase_		   =				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   UpperCAmelCase_		   =				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   UpperCAmelCase_		   =				[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)
      UpperCAmelCase_		   =				time.time()
      UpperCAmelCase_		   =				fit(
          model,
          loss,
          optimizer,
          tf_train_dataset,
          args.epochs,
          args.per_device_train_batch_size,
          max_steps=args.max_steps,
      )
      UpperCAmelCase_		   =				time.time() - start_train_time
      logger.info("""*** Train ***""")
      logger.info(f'train_runtime = {end_train_time}')

      UpperCAmelCase_		   =				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):
      UpperCAmelCase_		   =				model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
      logger.info("""*** Evaluate ***""")

      UpperCAmelCase_		   =				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)



