





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





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase=None   ):
						_UpperCamelCase			:					Optional[Any]     =    tqdm(_lowercase   )
						for i, batch in enumerate(_lowercase   ):
												with tf.GradientTape() as tape:
																		_UpperCamelCase   ,							_UpperCamelCase			:					Optional[Any]     =    batch
																		_UpperCamelCase			:					Dict     =    model(_lowercase   )
																		_UpperCamelCase			:					List[Any]     =    loss(_lowercase       ,						outputs.logits   )

												if SDP_ENABLED:
																		_UpperCamelCase			:					Tuple     =    sdp.DistributedGradientTape(_lowercase       ,						sparse_as_dense=_lowercase   )

												_UpperCamelCase			:					List[str]     =    tape.gradient(_lowercase       ,						model.trainable_variables   )
												opt.apply_gradients(zip(_lowercase       ,						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

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





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						# Load dataset
						_UpperCamelCase   ,							_UpperCamelCase			:					List[Any]     =    load_dataset('''imdb'''       ,						split=['''train''', '''test''']   )

						# Preprocess train dataset
						_UpperCamelCase			:					Dict     =    train_dataset.map(
						    lambda _lowercase   : tokenizer(e['''text''']       ,						truncation=_lowercase       ,						padding='''max_length'''   )       ,						batched=_lowercase   )
						train_dataset.set_format(type='''tensorflow'''       ,						columns=['''input_ids''', '''attention_mask''', '''label''']   )

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

						# Preprocess test dataset
						_UpperCamelCase			:					str     =    test_dataset.map(
						    lambda _lowercase   : tokenizer(e['''text''']       ,						truncation=_lowercase       ,						padding='''max_length'''   )       ,						batched=_lowercase   )
						test_dataset.set_format(type='''tensorflow'''       ,						columns=['''input_ids''', '''attention_mask''', '''label''']   )

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

						if SDP_ENABLED:
												_UpperCamelCase			:					Tuple     =    tf_train_dataset.shard(sdp.size()       ,						sdp.rank()   )
												_UpperCamelCase			:					Optional[int]     =    tf_test_dataset.shard(sdp.size()       ,						sdp.rank()   )
						_UpperCamelCase			:					Optional[Any]     =    tf_train_dataset.batch(_lowercase       ,						drop_remainder=_lowercase   )
						_UpperCamelCase			:					List[str]     =    tf_test_dataset.batch(_lowercase       ,						drop_remainder=_lowercase   )

						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=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"""])

						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)










"""simple docstring"""




UpperCamelCase_           ={
    """A""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """B""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """C""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """D""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """E""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """F""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """G""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """H""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """I""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """J""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """K""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """L""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """M""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """N""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """O""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """P""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """Q""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """R""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """S""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """T""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """U""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """V""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """W""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """X""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """Y""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
    """Z""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
}





def        a_				(							_lowercase   ):
						return [alphabet[char] for char in key.upper()]





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Optional[Any]     =    ''''''
						_UpperCamelCase			:					Tuple     =    0
						_UpperCamelCase			:					Optional[int]     =    generate_table(_lowercase   )
						for char in words.upper():
												cipher += get_opponent(table[count]       ,						_lowercase   )
												_UpperCamelCase			:					Optional[int]     =    (count + 1) % len(_lowercase   )
						return cipher





def        a_				(							_lowercase       ,						_lowercase   ):
						return encrypt(_lowercase       ,						_lowercase   )





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Dict     =    0 if char in table[0] else 1
						_UpperCamelCase			:					str     =    table[row].index(_lowercase   )
						return row, col





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    get_position(_lowercase       ,						char.upper()   )
						if row == 1:
												return table[0][col]
						else:
												return table[1][col] if row == 0 else char


if __name__ == "__main__":
						import doctest

						doctest.testmod()  # Fist ensure that all our tests are passing...
						UpperCamelCase_           =input("""Enter key: """).strip()
						UpperCamelCase_           =input("""Enter text to encrypt: """).strip()
						UpperCamelCase_           =encrypt(key, text)

						print(F"Encrypted: {cipher_text}")
						print(F"Decrypted with key: {decrypt(key, cipher_text)}")



