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





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

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

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





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						# Load dataset
						lowercase__	,  lowercase__								=    load_dataset("""imdb"""						,						split=["""train""", """test"""]		)

						# Preprocess train dataset
						lowercase__								=    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"""]		)

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

						# Preprocess test dataset
						lowercase__								=    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"""]		)

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

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

						return tf_train_dataset, tf_test_dataset


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

				__A      ,    __A       =      parser.parse_known_args()

				# Set up logging
				__A       =      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()

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

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

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

								__A       =      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'''



import warnings

from .generation import FlaxGenerationMixin





class      A							(    __UpperCAmelCase			):
			# warning at import time
			warnings.warn(
			    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
			    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""						,  __UpperCAmelCase						,  )






