




'''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__":
		a__         =     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)

		a__         =     parser.parse_known_args()

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

		# 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''',
		)

		# Load model and tokenizer
		a__         =     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
		a__         =     AutoTokenizer.from_pretrained(args.model_name_or_path)

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

		# Preprocess train dataset
		a__         =     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'''])

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

		# Preprocess test dataset
		a__         =     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'''])

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

		a__         =     time.time()
		a__         =     model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
		a__         =     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}")
'''simple docstring'''


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  str  = min(re.search('^ +'      ,	lowerCamelCase_  ).end() if t.startswith(' '  ) else 0 for t in string.splitlines()  )
     return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCamelCase_  )]  )


snake_case__	:     Dict      =    '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

snake_case__	:     Union[str, Any]      =    '''\
---
---
# Begin of markdown

Some cool dataset card
'''


snake_case__	:     int      =    '''\
# Begin of markdown

Some cool dataset card
'''






class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase_				:  Tuple  = Path(snake_case_		) / 'README.md'
            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Optional[Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Union[str, Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  List[str]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertEqual(snake_case_ ,						{}		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  int  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '		)
       with self.assertRaises(snake_case_		):
            DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  str  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)






