

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__":
 _UpperCamelCase			      =		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)

 _UpperCamelCase      ,      _UpperCamelCase			      =		parser.parse_known_args()

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

 # 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",
 )

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

 # Load dataset
 _UpperCamelCase      ,      _UpperCamelCase			      =		load_dataset("imdb", split=["train", "test"])
 _UpperCamelCase			      =		train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
 _UpperCamelCase			      =		test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

 # Preprocess train dataset
 _UpperCamelCase			      =		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"])

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

 # Preprocess test dataset
 _UpperCamelCase			      =		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"])

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

 _UpperCamelCase			      =		time.time()
 _UpperCamelCase			      =		model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
 _UpperCamelCase			      =		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}")





import heapq







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = [(0, start)]  # cost from start node,end node
      __lowerCAmelCase :						Any = set()
      while heap:
            ((__lowerCAmelCase),							(__lowerCAmelCase)) :						str = heapq.heappop(lowercase__						)
            if u in visited:
                  continue
            visited.add(lowercase__						)
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  __lowerCAmelCase :						str = cost + c
                  heapq.heappush(lowercase__       ,     (next_item, v)						)
      return -1


_UpperCamelCase			      =		{
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}
_UpperCamelCase			      =		{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
_UpperCamelCase			      =		{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

_UpperCamelCase			      =		dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_UpperCamelCase			      =		dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

_UpperCamelCase			      =		dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
 import doctest

 doctest.testmod()


