

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__":
			UpperCAmelCase_       :				Optional[Any]		   =      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)

			UpperCAmelCase_       :				Union[str, Any]		   =      parser.parse_known_args()

			# overwrite batch size until we have tf_glue.py
			UpperCAmelCase_       :				str		   =      16
			UpperCAmelCase_       :				Union[str, Any]		   =      16

			# Set up logging
			UpperCAmelCase_       :				Optional[Any]		   =      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
			UpperCAmelCase_       :				int		   =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
			UpperCAmelCase_       :				str		   =      AutoTokenizer.from_pretrained(args.model_name_or_path)

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

			# Preprocess train dataset
			UpperCAmelCase_       :				List[Any]		   =      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'''])

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

			# Preprocess test dataset
			UpperCAmelCase_       :				str		   =      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'''])

			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_       :				str		   =      tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])).batch(
			    args.per_device_eval_batch_size
			)

			# fine optimizer and loss
			UpperCAmelCase_       :				Optional[int]		   =      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
			UpperCAmelCase_       :				Dict		   =      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
			UpperCAmelCase_       :				Tuple		   =      [tf.keras.metrics.SparseCategoricalAccuracy()]
			model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

			UpperCAmelCase_       :				int		   =      time.time()
			UpperCAmelCase_       :				str		   =      model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
			UpperCAmelCase_       :				List[Any]		   =      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}""")








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
			from PIL import Image






class     lowerCamelCase_			(  unittest.TestCase	,			_lowercase  ):




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : str								= load_tool("""image-captioning"""       )
		self.tool.setup()
		__A   : Dict								= load_tool("""image-captioning"""				,			remote=__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
		__A   : List[Any]								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : Optional[Any]								= self.tool(__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : Any								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : List[str]								= self.remote_tool(__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
		__A   : Optional[Any]								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : str								= self.tool(image=__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )




	def 					lowerCAmelCase_					(    self	:    str       ):
		__A   : Dict								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : List[Any]								= self.remote_tool(image=__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )





