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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase=None							):
    snake_case__  =			tqdm(__lowerCAmelCase							)
    for i, batch in enumerate(__lowerCAmelCase							):
        with tf.GradientTape() as tape:
            snake_case__  ,   snake_case__  =			batch
            snake_case__  =			model(__lowerCAmelCase							)
            snake_case__  =			loss(__lowerCAmelCase      ,						outputs.logits							)

        if SDP_ENABLED:
            snake_case__  =			sdp.DistributedGradientTape(__lowerCAmelCase      ,						sparse_as_dense=__lowerCAmelCase							)

        snake_case__  =			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

    snake_case__  =			{"loss": loss_value.numpy()}
    return train_results





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    # Load dataset
    snake_case__  ,   snake_case__  =			load_dataset("imdb"      ,						split=["train", "test"]							)

    # Preprocess train dataset
    snake_case__  =			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"]							)

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

    # Preprocess test dataset
    snake_case__  =			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"]							)

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

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

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
     __magic_name__           =					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'''])

     __magic_name__    , __magic_name__           =					parser.parse_known_args()

     # Set up logging
     __magic_name__           =					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()

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

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

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

          __magic_name__           =					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)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__           =					logging.get_logger(__name__)

__magic_name__           =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Any      		=       'swiftformer'
      def __init__(     self						,		lowerCamelCase=3						,		lowerCamelCase=[3, 3, 6, 4]						,		lowerCamelCase=[48, 56, 1_12, 2_20]						,		lowerCamelCase=4						,		lowerCamelCase=[True, True, True, True]						,		lowerCamelCase="gelu"						,		lowerCamelCase=3						,		lowerCamelCase=2						,		lowerCamelCase=1						,		lowerCamelCase=0.0						,		lowerCamelCase=True						,		lowerCamelCase=1e-5						,		lowerCamelCase=1e-5						,		**lowerCamelCase						,		):
          super().__init__(**lowerCamelCase	)
          snake_case__  =			num_channels
          snake_case__  =			depths
          snake_case__  =			embed_dims
          snake_case__  =			mlp_ratio
          snake_case__  =			downsamples
          snake_case__  =			hidden_act
          snake_case__  =			down_patch_size
          snake_case__  =			down_stride
          snake_case__  =			down_pad
          snake_case__  =			drop_path_rate
          snake_case__  =			use_layer_scale
          snake_case__  =			layer_scale_init_value
          snake_case__  =			batch_norm_eps





class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						int      		=       version.parse('1.11'	)
      @property
      def A_     (     self	):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]	)



      @property
      def A_     (     self	):
          return 1e-4



