

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        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Any					,      __A						:     Union[str, Any]					,      __A						:     Union[str, Any]							):
		self.assertEqual(len(__A							)					,      len(__A							)							)
		for a, b in zip(__A					,      __A							):
			self.assertAlmostEqual(__A					,      __A					,      delta=__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__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(__A							):
			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						:     List[Any]							):
		__A :			List[Any]		    =			None
		ops.enable_eager_execution_internal()
		__A :			List[Any]		    =			tf.config.list_physical_devices("""CPU"""							)
		if len(__A							) == 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 :			Union[str, Any]		    =			GradientAccumulator()
			__A :			List[str]		    =			tf.Variable([4.0, 3.0]							)
			__A    ,				__A :			Any		    =			create_optimizer(5e-5					,      10					,      5							)
			__A :			int		    =			tf.Variable([0.0, 0.0]					,      trainable=__A							)

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

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

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

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

		def _check_local_values(__A						:     List[Any]					,      __A						:     Tuple							):
			__A :			int		    =			strategy.experimental_local_results(accumulator._gradients[0]							)
			self.assertListAlmostEqual(values[0].value()					,      __A					,      tol=1e-2							)
			self.assertListAlmostEqual(values[1].value()					,      __A					,      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 os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase_							:					Optional[Any]		 =   [
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str=True       )					->				List[Any]:
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]






@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowercase	)	)
class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Optional[int]     					=				None
	_lowercase		:   str     					=				None




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Optional[int]					,      __A						:     Optional[Any]							):
		with TemporaryDirectory() as tmp_dir:
			__A :			List[Any]		    =			dataset_module_factory(__A					,      cache_dir=__A							)

			__A :			Tuple		    =			import_main_class(dataset_module.module_path					,      dataset=__A							)

			__A :			DatasetBuilder		    =			builder_cls(
			    cache_dir=__A					,      config_name=__A					,      hash=dataset_module.hash					,      )

			__A :			List[Any]		    =			"""/""".join(
			    [
			        HF_GCP_BASE_URL,
			        builder_instance._relative_data_dir(with_hash=__A							).replace(os.sep					,      """/"""							),
			        config.DATASET_INFO_FILENAME,
			    ]							)
			__A :			Union[str, Any]		    =			cached_path(__A					,      cache_dir=__A							)
			self.assertTrue(os.path.exists(__A							)							)






@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict       )					->				Optional[Any]:
	__A :			Optional[Any]		    =			tmp_path_factory.mktemp("""test_hf_gcp"""       ) / """test_wikipedia_simple"""
	__A :			Union[str, Any]		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			List[Any]		    =			import_main_class(dataset_module.module_path       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	__A :			Any		    =			None
	builder_instance.download_and_prepare()
	__A :			Union[str, Any]		    =			builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				List[str]:
	__A :			Tuple		    =			dataset_module_factory("""wikipedia"""			,cache_dir=a__       )
	__A :			str		    =			import_main_class(dataset_module.module_path			,dataset=a__       )
	__A :			DatasetBuilder		    =			builder_cls(
	    cache_dir=a__			,config_name="""20220301.frr"""			,hash=dataset_module.hash			,)
	__A :			Optional[int]		    =			builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(a__			,a__       )
	assert "train" in ds
	assert isinstance(ds["""train"""]			,a__       )
	assert next(iter(ds["""train"""]       )       )





