






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 			_UpperCamelCase(       unittest.TestCase		):

		def    __lowerCAmelCase  (					self		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



					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    __lowerCAmelCase  (					self		:    Any    ):



					'''simple docstring'''



					__a						:  List[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    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					__a						:  int									=     None
					ops.enable_eager_execution_internal()
					__a						:  Optional[Any]									=     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()]    )
					__a						:  int									=     tf.config.list_logical_devices(device_type='CPU'    )
					__a						:  str									=     tf.distribute.MirroredStrategy(devices=devices[:2]    )

					with strategy.scope():
								__a						:  List[str]									=     GradientAccumulator()
								__a						:  Tuple									=     tf.Variable([4.0, 3.0]    )
								__a						,					__a						:  int									=     create_optimizer(5e-5    ,       1_0    ,       5    )
								__a						:  List[Any]									=     tf.Variable([0.0, 0.0]    ,       trainable=SCREAMING_SNAKE_CASE__    )

					def accumulate_on_replica(SCREAMING_SNAKE_CASE__		:    Optional[Any]    ):
								accumulator([gradient]    )

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

					@tf.function
					def accumulate(SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ):
								with strategy.scope():
											__a						:  Optional[Any]									=     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__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    int    ):
								__a						:  Union[str, Any]									=     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]    )









'''simple docstring'''






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase				=pytest.mark.integration





@pytest.mark.parametrize('path'			,       ['paws', 'csv']				)
def lowerCamelCase__     (		__lowerCamelCase			:				List[Any]			,       __lowerCamelCase			:				Optional[Any]				):







   '''simple docstring'''




   inspect_dataset(__lowerCamelCase			,       __lowerCamelCase				)
   _UpperCAmelCase  :  List[str]				       =path + '.py'
   assert script_name in os.listdir(__lowerCamelCase				)
   assert "__pycache__" not in os.listdir(__lowerCamelCase				)





@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'				)
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'				)
@pytest.mark.parametrize('path'			,       ['accuracy']				)
def lowerCamelCase__     (		__lowerCamelCase			:				Dict			,       __lowerCamelCase			:				str				):







   '''simple docstring'''




   inspect_metric(__lowerCamelCase			,       __lowerCamelCase				)
   _UpperCAmelCase  :  List[Any]				       =path + '.py'
   assert script_name in os.listdir(__lowerCamelCase				)
   assert "__pycache__" not in os.listdir(__lowerCamelCase				)





@pytest.mark.parametrize(
    'path, config_name, expected_splits'			,       [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Any			,       __lowerCamelCase			:				Optional[int]			,       __lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       =get_dataset_config_info(__lowerCamelCase			,       config_name=__lowerCamelCase				)
   assert info.config_name == config_name
   assert list(info.splits.keys()				) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'			,       [
        ('paws', None, ValueError),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				List[Any]			,       __lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				List[str]				):







   '''simple docstring'''




   with pytest.raises(__lowerCamelCase				):
      get_dataset_config_info(__lowerCamelCase			,       config_name=__lowerCamelCase				)





@pytest.mark.parametrize(
    'path, expected'			,       [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				List[str]				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =get_dataset_config_names(__lowerCamelCase				)
   assert expected in config_names





@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'			,       [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				List[Any]			,       __lowerCamelCase			:				List[Any]				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[int]				       =get_dataset_infos(__lowerCamelCase				)
   assert list(infos.keys()				) == expected_configs
   _UpperCAmelCase  :  Optional[int]				       =expected_configs[0]
   assert expected_config in infos
   _UpperCAmelCase  :  Optional[int]				       =infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()				) == expected_splits_in_first_config





@pytest.mark.parametrize(
    'path, expected_config, expected_splits'			,       [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       =get_dataset_infos(__lowerCamelCase				)
   assert expected_config in infos
   _UpperCAmelCase  :  Dict				       =infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()				) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'			,       [
        ('paws', None, ValueError),
    ]			,       )
def lowerCamelCase__     (		__lowerCamelCase			:				Union[str, Any]			,       __lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				Any				):







   '''simple docstring'''




   with pytest.raises(__lowerCamelCase				):
      get_dataset_split_names(__lowerCamelCase			,       config_name=__lowerCamelCase				)

