




'''simple docstring'''




from typing import TYPE_CHECKING

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


snake_case__          =						{
    """configuration_codegen""": ["""CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CodeGenConfig""", """CodeGenOnnxConfig"""],
    """tokenization_codegen""": ["""CodeGenTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__          =						["""CodeGenTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__          =						[
							    """CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CodeGenForCausalLM""",
							    """CodeGenModel""",
							    """CodeGenPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
							from .tokenization_codegen import CodeGenTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_codegen_fast import CodeGenTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_codegen import (
														    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CodeGenForCausalLM,
														    CodeGenModel,
														    CodeGenPreTrainedModel,
														)

else:
							import sys

							snake_case__          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''simple docstring'''




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__":
							snake_case__          =						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)

							snake_case__       ,						snake_case__          =						parser.parse_known_args()

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

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

							# Load dataset
							snake_case__       ,						snake_case__          =						load_dataset("""imdb""", split=["""train""", """test"""])
							snake_case__          =						train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
							snake_case__          =						test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							snake_case__          =						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"""])

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

							# Preprocess test dataset
							snake_case__          =						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"""])

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

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

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



