
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


_A   =     [
    {"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    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]=True				):




			"""simple docstring"""


			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=__a       )       )
class 	_lowerCAmelCase					(	__a       ):
				_lowercase					=None
				_lowercase					=None




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      int:
							with TemporaryDirectory() as tmp_dir:
										lowerCAmelCase_						=	dataset_module_factory(_UpperCamelCase				,				cache_dir=_UpperCamelCase     )

										lowerCAmelCase_						=	import_main_class(dataset_module.module_path				,				dataset=_UpperCamelCase     )

										lowerCAmelCase_						=	builder_cls(
										    cache_dir=_UpperCamelCase				,				config_name=_UpperCamelCase				,				hash=dataset_module.hash				,				)

										lowerCAmelCase_						=	"/".join(
										    [
										        HF_GCP_BASE_URL,
										        builder_instance._relative_data_dir(with_hash=_UpperCamelCase     ).replace(os.sep				,				"/"     ),
										        config.DATASET_INFO_FILENAME,
										    ]     )
										lowerCAmelCase_						=	cached_path(_UpperCamelCase				,				cache_dir=_UpperCamelCase     )
										self.assertTrue(os.path.exists(_UpperCamelCase     )     )






@pytest.mark.integration
def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]				):




			"""simple docstring"""


			lowerCAmelCase_						=	tmp_path_factory.mktemp("test_hf_gcp"				) / "test_wikipedia_simple"
			lowerCAmelCase_						=	dataset_module_factory("wikipedia"    ,							cache_dir=__lowerCAmelCase				)
			lowerCAmelCase_						=	import_main_class(dataset_module.module_path				)
			lowerCAmelCase_						=	builder_cls(
			    cache_dir=__lowerCAmelCase    ,							config_name="20220301.frr"    ,							hash=dataset_module.hash    ,							)
			# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
			lowerCAmelCase_						=	None
			builder_instance.download_and_prepare()
			lowerCAmelCase_						=	builder_instance.as_dataset()
			assert ds






@pytest.mark.integration
def    lowerCamelCase__				(				__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	dataset_module_factory("wikipedia"    ,							cache_dir=__lowerCAmelCase				)
			lowerCAmelCase_						=	import_main_class(dataset_module.module_path    ,							dataset=__lowerCAmelCase				)
			lowerCAmelCase_						=	builder_cls(
			    cache_dir=__lowerCAmelCase    ,							config_name="20220301.frr"    ,							hash=dataset_module.hash    ,							)
			lowerCAmelCase_						=	builder_instance.as_streaming_dataset()
			assert ds
			assert isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				)
			assert "train" in ds
			assert isinstance(ds["train"]    ,							__lowerCAmelCase				)
			assert next(iter(ds["train"]				)				)





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =     logging.get_logger(__name__)

_A   =     {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''sew'''




				def __init__(					self				,				_UpperCamelCase=32				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=12				,				_UpperCamelCase=3_072				,				_UpperCamelCase=2				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-5				,				_UpperCamelCase="group"				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,				_UpperCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,				_UpperCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,				_UpperCamelCase=False				,				_UpperCamelCase=128				,				_UpperCamelCase=16				,				_UpperCamelCase=True				,				_UpperCamelCase=0.05				,				_UpperCamelCase=10				,				_UpperCamelCase=2				,				_UpperCamelCase=0.0				,				_UpperCamelCase=10				,				_UpperCamelCase=0				,				_UpperCamelCase="mean"				,				_UpperCamelCase=False				,				_UpperCamelCase=False				,				_UpperCamelCase=256				,				_UpperCamelCase=0				,				_UpperCamelCase=1				,				_UpperCamelCase=2				,				**_UpperCamelCase				,				)				->      Union[str, Any]:
							super().__init__(**_UpperCamelCase				,				pad_token_id=_UpperCamelCase				,				bos_token_id=_UpperCamelCase				,				eos_token_id=_UpperCamelCase     )
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	feat_extract_norm
							lowerCAmelCase_						=	feat_extract_activation
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	conv_bias
							lowerCAmelCase_						=	num_conv_pos_embeddings
							lowerCAmelCase_						=	num_conv_pos_embedding_groups
							lowerCAmelCase_						=	len(self.conv_dim     )
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	squeeze_factor
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_dropout
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	activation_dropout
							lowerCAmelCase_						=	feat_proj_dropout
							lowerCAmelCase_						=	final_dropout
							lowerCAmelCase_						=	layerdrop
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	vocab_size

							if (
							    (len(self.conv_stride     ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim     )}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride     )}`, `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCAmelCase_						=	apply_spec_augment
							lowerCAmelCase_						=	mask_time_prob
							lowerCAmelCase_						=	mask_time_length
							lowerCAmelCase_						=	mask_time_min_masks
							lowerCAmelCase_						=	mask_feature_prob
							lowerCAmelCase_						=	mask_feature_length
							lowerCAmelCase_						=	mask_feature_min_masks

							# ctc loss
							lowerCAmelCase_						=	ctc_loss_reduction
							lowerCAmelCase_						=	ctc_zero_infinity

							# sequence classification
							lowerCAmelCase_						=	use_weighted_layer_sum
							lowerCAmelCase_						=	classifier_proj_size




				@property
				def      __a						(					self     )				->      Optional[Any]:
							return functools.reduce(operator.mul				,				self.conv_stride				,				1     )



