



'''simple docstring'''

import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


__lowerCAmelCase						      =       {
    """debug""": logging.DEBUG,
    """info""": logging.INFO,
    """warning""": logging.WARNING,
    """error""": logging.ERROR,
    """critical""": logging.CRITICAL,
}

__lowerCAmelCase						      =       logging.WARNING



def 					__lowerCamelCase					(				)      ->				Dict:
		_a					:				Union[str, Any]				=							os.getenv('DATASETS_VERBOSITY'						,      lowerCAmelCase_  )
		if env_level_str:
				if env_level_str in log_levels:
						return log_levels[env_level_str]
				else:
						logging.getLogger().warning(
						    f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
						    f"""has to be one of: { ', '.join(log_levels.keys()  ) }"""  )
		return _default_log_level



def 					__lowerCamelCase					(				)      ->				str:
		return __name__.split('.'  )[0]



def 					__lowerCamelCase					(				)      ->				logging.Logger:
		return logging.getLogger(_get_library_name()  )



def 					__lowerCamelCase					(				)      ->				None:
		# Apply our default configuration to the library root logger.
		_a					:				Tuple				=							_get_library_root_logger()
		library_root_logger.setLevel(_get_default_logging_level()  )



def 					__lowerCamelCase					(				)      ->				None:
		_a					:				Optional[Any]				=							_get_library_root_logger()
		library_root_logger.setLevel(logging.NOTSET  )



def 					__lowerCamelCase					(				lowerCAmelCase_ = None  )      ->				logging.Logger:
		if name is None:
				_a					:				str				=							_get_library_name()
		return logging.getLogger(lowerCAmelCase_  )



def 					__lowerCamelCase					(				)      ->				int:
		return _get_library_root_logger().getEffectiveLevel()



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				None:
		_get_library_root_logger().setLevel(lowerCAmelCase_  )



def 					__lowerCamelCase					(				)      ->				Any:
		return set_verbosity(lowerCAmelCase_  )



def 					__lowerCamelCase					(				)      ->				Tuple:
		return set_verbosity(lowerCAmelCase_  )



def 					__lowerCamelCase					(				)      ->				int:
		return set_verbosity(lowerCAmelCase_  )



def 					__lowerCamelCase					(				)      ->				List[Any]:
		return set_verbosity(lowerCAmelCase_  )



def 					__lowerCamelCase					(				)      ->				None:
		_a					:				Any				=							False



def 					__lowerCamelCase					(				)      ->				None:
		_a					:				Tuple				=							True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()

class      __magic_name__						:







							def __init__(							self :	Tuple     ,*_UpperCAmelCase :	Optional[Any]     ,**_UpperCAmelCase :	Optional[Any]		):  # pylint: disable=unused-argument
									_a					:				str				=							args[0] if args else None







							def __iter__(							self :	Any		):
									return iter(self._iterator		)







							def __getattr__(							self :	Dict     ,_UpperCAmelCase :	List[str]		):

									def empty_fn(*_UpperCAmelCase :	Optional[int]     ,**_UpperCAmelCase :	List[str]		):  # pylint: disable=unused-argument
											return

									return empty_fn







							def __enter__(							self :	Dict		):
									return self







							def __exit__(							self :	List[str]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Union[str, Any]		):
									return
__lowerCAmelCase						      =       True

class      __magic_name__						:







							def __call__(							self :	Optional[Any]     ,*_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Tuple=False     ,**_UpperCAmelCase :	Optional[int]		):

									if _tqdm_active and not disable:
											return tqdm_lib.tqdm(*__SCREAMING_SNAKE_CASE     ,**__SCREAMING_SNAKE_CASE		)
									else:
											return EmptyTqdm(*__SCREAMING_SNAKE_CASE     ,**__SCREAMING_SNAKE_CASE		)







							def 							__lowercase      (							self :	Optional[int]     ,*_UpperCAmelCase :	int     ,**_UpperCAmelCase :	Any		):
									_a					:				int				=							None
									if _tqdm_active:
											return tqdm_lib.tqdm.set_lock(*__SCREAMING_SNAKE_CASE     ,**__SCREAMING_SNAKE_CASE		)







							def 							__lowercase      (							self :	Optional[Any]		):

									if _tqdm_active:
											return tqdm_lib.tqdm.get_lock()
__lowerCAmelCase						      =       _tqdm_cls()



def 					__lowerCamelCase					(				)      ->				bool:
		global _tqdm_active
		return bool(_tqdm_active  )



def 					__lowerCamelCase					(				)      ->				Optional[int]:
		global _tqdm_active
		_a					:				Dict				=							True



def 					__lowerCamelCase					(				)      ->				str:
		global _tqdm_active
		_a					:				Optional[Any]				=							False





'''simple docstring'''

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      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[Any]		):
									self.assertEqual(len(_UpperCAmelCase		)     ,len(_UpperCAmelCase		)		)
									for a, b in zip(_UpperCAmelCase     ,_UpperCAmelCase		):
											self.assertAlmostEqual(_UpperCAmelCase     ,_UpperCAmelCase     ,delta=_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				int				=							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(_UpperCAmelCase		):
											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 							__lowercase      (							self :	Any		):
									_a					:				int				=							None
									ops.enable_eager_execution_internal()
									_a					:				Optional[int]				=							tf.config.list_physical_devices('CPU'		)
									if len(_UpperCAmelCase		) == 1:
											tf.config.set_logical_device_configuration(
											    physical_devices[0]     ,[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]		)
									_a					:				Tuple				=							tf.config.list_logical_devices(device_type='CPU'		)
									_a					:				List[str]				=							tf.distribute.MirroredStrategy(devices=devices[:2]		)

									with strategy.scope():
											_a					:				Tuple				=							GradientAccumulator()
											_a					:				List[Any]				=							tf.Variable([4.0, 3.0]		)
											_a	, _a					:				Dict				=							create_optimizer(5E-5     ,10     ,5		)
											_a					:				Tuple				=							tf.Variable([0.0, 0.0]     ,trainable=_UpperCAmelCase		)

									def accumulate_on_replica(_UpperCAmelCase :	str		):
											accumulator([gradient]		)

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

									@tf.function
									def accumulate(_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[int]		):
											with strategy.scope():
													_a					:				Union[str, Any]				=							strategy.experimental_local_results(_UpperCAmelCase		)
													local_variables[0].assign(_UpperCAmelCase		)
													local_variables[1].assign(_UpperCAmelCase		)
													strategy.run(_UpperCAmelCase     ,args=(gradient_placeholder,)		)

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

									def _check_local_values(_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	List[str]		):
											_a					:				List[Any]				=							strategy.experimental_local_results(accumulator._gradients[0]		)
											self.assertListAlmostEqual(values[0].value()     ,_UpperCAmelCase     ,tol=1E-2		)
											self.assertListAlmostEqual(values[1].value()     ,_UpperCAmelCase     ,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]		)




