
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_	:    List[Any]  					=       True
			UpperCamelCase_	:    int  					=       """p3dn.24xlarge"""
			import smdistributed.dataparallel.tensorflow as sdp
else:
			UpperCamelCase_	:    List[str]  					=       False



def 		UpperCamelCase    (					_UpperCAmelCase     :      int   , _UpperCAmelCase     :      Dict   , _UpperCAmelCase     :      str   , _UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      List[Any]   , _UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      Dict=None     )      -> Optional[Any]:






						'''simple docstring'''

						_lowercase					:  str    			=      tqdm(lowercase__     )
						for i, batch in enumerate(lowercase__     ):
												with tf.GradientTape() as tape:
																		_lowercase			,						_lowercase					:  Optional[Any]    			=      batch
																		_lowercase					:  List[Any]    			=      model(lowercase__     )
																		_lowercase					:  Optional[int]    			=      loss(lowercase__   , outputs.logits     )

												if SDP_ENABLED:
																		_lowercase					:  int    			=      sdp.DistributedGradientTape(lowercase__   , sparse_as_dense=lowercase__     )

												_lowercase					:  Any    			=      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

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



def 		UpperCamelCase    (					_UpperCAmelCase     :      Union[str, Any]   , _UpperCAmelCase     :      Any   , _UpperCAmelCase     :      int     )      -> Optional[Any]:






						'''simple docstring'''

						_lowercase			,						_lowercase					:  Optional[int]    			=      load_dataset("imdb"   , split=["train", "test"]     )

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

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

						# Preprocess test dataset
						_lowercase					:  Optional[int]    			=      test_dataset.map(
						    lambda _UpperCAmelCase     : tokenizer(e["text"]   , truncation=lowercase__   , padding="max_length"     )   , batched=lowercase__     )
						test_dataset.set_format(type="tensorflow"   , columns=["input_ids", "attention_mask", "label"]     )

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

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

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
			UpperCamelCase_	:    int  					=       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_	:    Union[str, Any]  					=       parser.parse_known_args()

			# Set up logging
			UpperCamelCase_	:    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()

						UpperCamelCase_	:    str  					=       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_	:    Tuple  					=       TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
			UpperCamelCase_	:    Tuple  					=       AutoTokenizer.from_pretrained(args.model_name_or_path)

			# get datasets
			UpperCamelCase_					,     UpperCamelCase_	:    Tuple  					=       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_	:    Dict  					=       tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
			UpperCamelCase_	:    Dict  					=       tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
			UpperCamelCase_	:    Optional[int]  					=       [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_	:    Any  					=       time.time()
						UpperCamelCase_	:    List[Any]  					=       fit(
						    model,
						    loss,
						    optimizer,
						    tf_train_dataset,
						    args.epochs,
						    args.per_device_train_batch_size,
						    max_steps=args.max_steps,
						)
						UpperCamelCase_	:    List[Any]  					=       time.time() - start_train_time
						logger.info("""*** Train ***""")
						logger.info(f'''train_runtime = {end_train_time}''')

						UpperCamelCase_	:    Optional[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):
						UpperCamelCase_	:    Any  					=       model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
						logger.info("""*** Evaluate ***""")

						UpperCamelCase_	:    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)






from __future__ import annotations



def 		UpperCamelCase    (					_UpperCAmelCase     :      list[int]   , _UpperCAmelCase     :      int     )      -> int:






						'''simple docstring'''

						_lowercase			,						_lowercase					:  Any    			=      0, len(_UpperCAmelCase     ) - 1
						while start_ind <= end_ind:
												if array[start_ind] == search_item:
																		return start_ind
												elif array[end_ind] == search_item:
																		return end_ind
												else:
																		start_ind += 1
																		end_ind -= 1
    # returns -1 if search_item is not found in array
						return -1


if __name__ == "__main__":
							print(double_linear_search(list(range(100)), 40))




