
import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf
					from tensorflow.python.eager import context
					from tensorflow.python.framework import ops

					from transformers import GradientAccumulator, create_optimizer







@require_tf
class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      int:


								'''simple docstring'''




								self.assertEqual(len(_snake_case				)		,     len(_snake_case				)				)
								for a, b in zip(_snake_case		,     _snake_case				):
												self.assertAlmostEqual(_snake_case		,     _snake_case		,     delta=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    GradientAccumulator()
								accumulator([tf.constant([1.0, 2.0]				)]				)
								accumulator([tf.constant([-2.0, 1.0]				)]				)
								accumulator([tf.constant([-1.0, 2.0]				)]				)
								with self.assertRaises(_snake_case				):
												accumulator([tf.constant([1.0, 1.0]				), tf.constant([2.0, 2.0]				)]				)
								self.assertEqual(accumulator.step		,     3				)
								self.assertEqual(len(accumulator.gradients				)		,     1				)
								self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,     [-2.0, 5.0]		,     tol=1E-2				)
								accumulator.reset()
								self.assertEqual(accumulator.step		,     0				)
								self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,     [0.0, 0.0]		,     tol=1E-2				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    None
								ops.enable_eager_execution_internal()
								__a										=    tf.config.list_physical_devices('''CPU'''				)
								if len(_snake_case				) == 1:
												tf.config.set_logical_device_configuration(
												    physical_devices[0]		,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]				)
								__a										=    tf.config.list_logical_devices(device_type='''CPU'''				)
								__a										=    tf.distribute.MirroredStrategy(devices=devices[:2]				)

								with strategy.scope():
												__a										=    GradientAccumulator()
												__a										=    tf.Variable([4.0, 3.0]				)
												__a							,     __a										=    create_optimizer(5E-5		,     10		,     5				)
												__a										=    tf.Variable([0.0, 0.0]		,     trainable=_snake_case				)

								def accumulate_on_replica(_snake_case				):
												accumulator([gradient]				)

								def apply_on_replica():
												optimizer.apply_gradients(list(zip(accumulator.gradients		,     [variable]				)				)				)

								@tf.function
								def accumulate(_snake_case		,     _snake_case				):
												with strategy.scope():
																__a										=    strategy.experimental_local_results(_snake_case				)
																local_variables[0].assign(_snake_case				)
																local_variables[1].assign(_snake_case				)
																strategy.run(_snake_case		,     args=(gradient_placeholder,)				)

								@tf.function
								def apply_grad():
												with strategy.scope():
																strategy.run(_snake_case				)

								def _check_local_values(_snake_case		,     _snake_case				):
												__a										=    strategy.experimental_local_results(accumulator._gradients[0]				)
												self.assertListAlmostEqual(values[0].value()		,     _snake_case		,     tol=1E-2				)
												self.assertListAlmostEqual(values[1].value()		,     _snake_case		,     tol=1E-2				)

								accumulate([1.0, 2.0]		,     [-1.0, 1.0]				)
								accumulate([3.0, -1.0]		,     [-1.0, -1.0]				)
								accumulate([-2.0, 2.0]		,     [3.0, -2.0]				)
								self.assertEqual(accumulator.step		,     3				)
								_check_local_values([2.0, 3.0]		,     [1.0, -2.0]				)
								apply_grad()
								self.assertListAlmostEqual(variable.value()		,     [4.0, 3.0]		,     tol=1E-2				)
								accumulator.reset()
								self.assertEqual(accumulator.step		,     0				)
								_check_local_values([0.0, 0.0]		,     [0.0, 0.0]				)

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


if __name__ == "__main__":
					fire.Fire(save_len_file)