



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     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Dict:
          self.assertEqual(len(SCREAMING_SNAKE_CASE_	),      len(SCREAMING_SNAKE_CASE_	)	)
          for a, b in zip(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             self.assertAlmostEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      delta=SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Any						     =     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(SCREAMING_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  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  Optional[int]						     =     None
          ops.enable_eager_execution_internal()
          UpperCamelCase   :  str						     =     tf.config.list_physical_devices('CPU'	)
          if len(SCREAMING_SNAKE_CASE_	) == 1:
             tf.config.set_logical_device_configuration(
                 physical_devices[0],      [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]	)
          UpperCamelCase   :  Optional[int]						     =     tf.config.list_logical_devices(device_type='CPU'	)
          UpperCamelCase   :  Dict						     =     tf.distribute.MirroredStrategy(devices=devices[:2]	)

          with strategy.scope():
             UpperCamelCase   :  Any						     =     GradientAccumulator()
             UpperCamelCase   :  Any						     =     tf.Variable([4.0, 3.0]	)
             UpperCamelCase     ,					UpperCamelCase   :  Optional[Any]						     =     create_optimizer(5e-5,      10,      5	)
             UpperCamelCase   :  Optional[int]						     =     tf.Variable([0.0, 0.0],      trainable=SCREAMING_SNAKE_CASE_	)

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

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

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

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

          def _check_local_values(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  str						     =     strategy.experimental_local_results(accumulator._gradients[0]	)
             self.assertListAlmostEqual(values[0].value(),      SCREAMING_SNAKE_CASE_,      tol=1e-2	)
             self.assertListAlmostEqual(values[1].value(),      SCREAMING_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]	)








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = "arrow",      **SCREAMING_SNAKE_CASE_,      )					->		int:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      keep_in_memory=SCREAMING_SNAKE_CASE_,      streaming=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  Any						     =     load_from_cache_file
          UpperCamelCase   :  Any						     =     file_format
          UpperCamelCase   :  str						     =     Spark(
              df=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      working_dir=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )






       def  snake_case_	(      self	)					->		Tuple:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split	)
          UpperCamelCase   :  Dict						     =     None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,      file_format=self._file_format,      )
          return self.builder.as_dataset(split=self.split	)



