



import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam
class 				_snake_case			(					datasets.BeamBasedBuilder):




				def 							A__      (						self					:						Dict						):
							return datasets.DatasetInfo(
							    features=datasets.Features({"content": datasets.Value("string"						)}						),					supervised_keys=__lowercase,					)




				def 							A__      (						self					:						int,					__lowercase					:						Union[str, Any],					__lowercase					:						List[Any]						):
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN,					gen_kwargs={"examples": get_test_dummy_examples()}						)]




				def 							A__      (						self					:						Any,					__lowercase					:						Any,					__lowercase					:						Tuple						):
							import apache_beam as beam

							return pipeline | "Load Examples" >> beam.Create(__lowercase						)
class 				_snake_case			(					datasets.BeamBasedBuilder):




				def 							A__      (						self					:						Tuple						):
							return datasets.DatasetInfo(
							    features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"						)}						)}						),					supervised_keys=__lowercase,					)




				def 							A__      (						self					:						List[Any],					__lowercase					:						List[str],					__lowercase					:						Any						):
							return [
							    datasets.SplitGenerator(name=datasets.Split.TRAIN,					gen_kwargs={"examples": get_test_nested_examples()}						)
							]




				def 							A__      (						self					:						List[Any],					__lowercase					:						Dict,					__lowercase					:						Tuple						):
							import apache_beam as beam

							return pipeline | "Load Examples" >> beam.Create(__lowercase						)



def __lowerCAmelCase (     ):
			return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"]  )]



def __lowerCAmelCase (     ):
			return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"]  )]




class 				_snake_case			(					lowercase__):




				@require_beam
				def 							A__      (						self					:						Any						):
							lowercase__					  =				len(get_test_dummy_examples()						)
							with tempfile.TemporaryDirectory() as tmp_cache_dir:
										lowercase__					  =				DummyBeamDataset(cache_dir=__lowercase,					beam_runner="DirectRunner"						)
										builder.download_and_prepare()
										self.assertTrue(
										    os.path.exists(
										        os.path.join(__lowercase,					builder.name,					"default",					"0.0.0",					F'''{builder.name}-train.arrow'''						)						)						)
										self.assertDictEqual(builder.info.features,					datasets.Features({"content": datasets.Value("string"						)}						)						)
										lowercase__					  =				builder.as_dataset()
										self.assertEqual(dset["train"].num_rows,					__lowercase						)
										self.assertEqual(dset["train"].info.splits["train"].num_examples,					__lowercase						)
										self.assertDictEqual(dset["train"][0],					get_test_dummy_examples()[0][1]						)
										self.assertDictEqual(
										    dset["train"][expected_num_examples - 1],					get_test_dummy_examples()[expected_num_examples - 1][1]						)
										self.assertTrue(
										    os.path.exists(os.path.join(__lowercase,					builder.name,					"default",					"0.0.0",					"dataset_info.json"						)						)						)
										del dset




				@require_beam
				def 							A__      (						self					:						List[str]						):
							import apache_beam as beam

							lowercase__					  =				beam.io.parquetio.WriteToParquet

							lowercase__					  =				len(get_test_dummy_examples()						)
							with tempfile.TemporaryDirectory() as tmp_cache_dir:
										lowercase__					  =				DummyBeamDataset(cache_dir=__lowercase,					beam_runner="DirectRunner"						)
										with patch("apache_beam.io.parquetio.WriteToParquet"						) as write_parquet_mock:
													lowercase__					  =				partial(__lowercase,					num_shards=2						)
													builder.download_and_prepare()
										self.assertTrue(
										    os.path.exists(
										        os.path.join(
										            __lowercase,					builder.name,					"default",					"0.0.0",					F'''{builder.name}-train-00000-of-00002.arrow'''						)						)						)
										self.assertTrue(
										    os.path.exists(
										        os.path.join(
										            __lowercase,					builder.name,					"default",					"0.0.0",					F'''{builder.name}-train-00000-of-00002.arrow'''						)						)						)
										self.assertDictEqual(builder.info.features,					datasets.Features({"content": datasets.Value("string"						)}						)						)
										lowercase__					  =				builder.as_dataset()
										self.assertEqual(dset["train"].num_rows,					__lowercase						)
										self.assertEqual(dset["train"].info.splits["train"].num_examples,					__lowercase						)
										# Order is not preserved when sharding, so we just check that all the elements are there
										self.assertListEqual(sorted(dset["train"]["content"]						),					sorted(["foo", "bar", "foobar"]						)						)
										self.assertTrue(
										    os.path.exists(os.path.join(__lowercase,					builder.name,					"default",					"0.0.0",					"dataset_info.json"						)						)						)
										del dset




				@require_beam
				def 							A__      (						self					:						Union[str, Any]						):
							with tempfile.TemporaryDirectory() as tmp_cache_dir:
										lowercase__					  =				DummyBeamDataset(cache_dir=__lowercase						)
										self.assertRaises(datasets.builder.MissingBeamOptions,					builder.download_and_prepare						)




				@require_beam
				def 							A__      (						self					:						Any						):
							lowercase__					  =				len(get_test_nested_examples()						)
							with tempfile.TemporaryDirectory() as tmp_cache_dir:
										lowercase__					  =				NestedBeamDataset(cache_dir=__lowercase,					beam_runner="DirectRunner"						)
										builder.download_and_prepare()
										self.assertTrue(
										    os.path.exists(
										        os.path.join(__lowercase,					builder.name,					"default",					"0.0.0",					F'''{builder.name}-train.arrow'''						)						)						)
										self.assertDictEqual(
										    builder.info.features,					datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string"						)}						)}						)						)
										lowercase__					  =				builder.as_dataset()
										self.assertEqual(dset["train"].num_rows,					__lowercase						)
										self.assertEqual(dset["train"].info.splits["train"].num_examples,					__lowercase						)
										self.assertDictEqual(dset["train"][0],					get_test_nested_examples()[0][1]						)
										self.assertDictEqual(
										    dset["train"][expected_num_examples - 1],					get_test_nested_examples()[expected_num_examples - 1][1]						)
										self.assertTrue(
										    os.path.exists(os.path.join(__lowercase,					builder.name,					"default",					"0.0.0",					"dataset_info.json"						)						)						)
										del dset









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XmodForCausalLM""",
							    """XmodForMaskedLM""",
							    """XmodForMultipleChoice""",
							    """XmodForQuestionAnswering""",
							    """XmodForSequenceClassification""",
							    """XmodForTokenClassification""",
							    """XmodModel""",
							    """XmodPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xmod import (
														    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XmodForCausalLM,
														    XmodForMaskedLM,
														    XmodForMultipleChoice,
														    XmodForQuestionAnswering,
														    XmodForSequenceClassification,
														    XmodForTokenClassification,
														    XmodModel,
														    XmodPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




