






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__":
						__lowerCamelCase						= 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)

						__lowerCamelCase						= parser.parse_known_args()

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

						# Set up logging
						__lowerCamelCase						= 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
						__lowerCamelCase						= TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						__lowerCamelCase						= AutoTokenizer.from_pretrained(args.model_name_or_path)

						# Load dataset
						__lowerCamelCase						= load_dataset('imdb', split=['train', 'test'])
						__lowerCamelCase						= train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
						__lowerCamelCase						= test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

						# Preprocess train dataset
						__lowerCamelCase						= 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'])

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

						# Preprocess test dataset
						__lowerCamelCase						= 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'])

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

						# fine optimizer and loss
						__lowerCamelCase						= tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
						__lowerCamelCase						= tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
						__lowerCamelCase						= [tf.keras.metrics.SparseCategoricalAccuracy()]
						model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

						__lowerCamelCase						= time.time()
						__lowerCamelCase						= model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						__lowerCamelCase						= 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}''')







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_   :	Union[str, Any]							      ={'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	int							      =[
					    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''GPTJForCausalLM''',
					    '''GPTJForQuestionAnswering''',
					    '''GPTJForSequenceClassification''',
					    '''GPTJModel''',
					    '''GPTJPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	str							      =[
					    '''TFGPTJForCausalLM''',
					    '''TFGPTJForQuestionAnswering''',
					    '''TFGPTJForSequenceClassification''',
					    '''TFGPTJModel''',
					    '''TFGPTJPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	List[Any]							      =[
					    '''FlaxGPTJForCausalLM''',
					    '''FlaxGPTJModel''',
					    '''FlaxGPTJPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gptj import (
										    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTJForCausalLM,
										    GPTJForQuestionAnswering,
										    GPTJForSequenceClassification,
										    GPTJModel,
										    GPTJPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gptj import (
										    TFGPTJForCausalLM,
										    TFGPTJForQuestionAnswering,
										    TFGPTJForSequenceClassification,
										    TFGPTJModel,
										    TFGPTJPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
					import sys

					snake_case_   :	List[str]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

