"""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():
				_SCREAMING_SNAKE_CASE       :  int        = True
				_SCREAMING_SNAKE_CASE       :  Dict        = 'p3dn.24xlarge'
				import smdistributed.dataparallel.tensorflow as sdp
else:
				_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = False



def        _lowerCAmelCase      (						UpperCAmelCase     :							Dict      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Union[str, Any]      ,							UpperCAmelCase     :							str      ,							UpperCAmelCase     :							Union[str, Any]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							str=None				):


					'''simple docstring'''






					UpperCamelCase__		:    List[str]         =tqdm(_A				)
					for i, batch in enumerate(_A				):
										with tf.GradientTape() as tape:
															UpperCamelCase__  ,			UpperCamelCase__		:    int         =batch
															UpperCamelCase__		:    Dict         =model(_A				)
															UpperCamelCase__		:    List[str]         =loss(_A      ,							outputs.logits				)

										if SDP_ENABLED:
															UpperCamelCase__		:    str         =sdp.DistributedGradientTape(_A      ,							sparse_as_dense=_A				)

										UpperCamelCase__		:    str         =tape.gradient(_A      ,							model.trainable_variables				)
										opt.apply_gradients(zip(_A      ,							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__		:    Any         ={'''loss''': loss_value.numpy()}
					return train_results



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Tuple				):


					'''simple docstring'''






					UpperCamelCase__  ,			UpperCamelCase__		:    int         =load_dataset('''imdb'''      ,							split=['''train''', '''test''']				)

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

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

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

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

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

					return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
				_SCREAMING_SNAKE_CASE       :  Any        = 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=1_6)
				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"""])

				_SCREAMING_SNAKE_CASE       :  Tuple        = parser.parse_known_args()

				# Set up logging
				_SCREAMING_SNAKE_CASE       :  Tuple        = 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()

								_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = 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
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
				_SCREAMING_SNAKE_CASE       :  List[Any]        = AutoTokenizer.from_pretrained(args.model_name_or_path)

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

								_SCREAMING_SNAKE_CASE       :  List[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):
								_SCREAMING_SNAKE_CASE       :  Dict        = model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
								logger.info("""*** Evaluate ***""")

								_SCREAMING_SNAKE_CASE       :  List[str]        = 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"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = ["""XLMProphetNetTokenizer"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = [
				    """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLMProphetNetDecoder""",
				    """XLMProphetNetEncoder""",
				    """XLMProphetNetForCausalLM""",
				    """XLMProphetNetForConditionalGeneration""",
				    """XLMProphetNetModel""",
				    """XLMProphetNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm_prophetnet import (
								    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMProphetNetDecoder,
								    XLMProphetNetEncoder,
								    XLMProphetNetForCausalLM,
								    XLMProphetNetForConditionalGeneration,
								    XLMProphetNetModel,
								    XLMProphetNetPreTrainedModel,
								)

else:
				import sys

				_SCREAMING_SNAKE_CASE       :  Dict        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


