






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      __A					(     _lowercase=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=snake_case					)					)
class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					None
							A_						  =					None

							def       __A (  self:   Union[str, Any]		,     __A:   Dict		,     __A:   Any   )    ->				Any:
												with TemporaryDirectory() as tmp_dir:
																	_A								= dataset_module_factory(__A		,     cache_dir=__A   )

																	_A								= import_main_class(dataset_module.module_path		,     dataset=__A   )

																	_A								= builder_cls(
																	    cache_dir=__A		,     config_name=__A		,     hash=dataset_module.hash		,     )

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




@pytest.mark.integration
def      __A					(     _lowercase ):

					'''simple docstring'''







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




@pytest.mark.integration
def      __A					(     _lowercase ):

					'''simple docstring'''







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














import flax.linen as nn
import jax
import jax.numpy as jnp





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   Tuple   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   Dict   )    ->				Tuple:
												_A							,_A							,_A							,_A								= hidden_states.shape
												_A								= jax.image.resize(
												    __A		,     shape=(batch, height * 2, width * 2, channels)		,     method='''nearest'''		,     )
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   List[str]   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(2, 2)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Union[str, Any]		,     __A:   List[Any]   )    ->				Union[str, Any]:
												# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
												# hidden_states = jnp.pad(hidden_states, pad_width=pad)
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					None
							A_						  =					0.0
							A_						  =					None
							A_						  =					jnp.floataa

							def       __A (  self:   Dict   )    ->				Dict:
												_A								= self.in_channels if self.out_channels is None else self.out_channels

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= nn.Dense(__A		,     dtype=self.dtype   )

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Dropout(self.dropout_prob   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

												_A								= None
												if use_nin_shortcut:
																	_A								= nn.Conv(
																	    __A		,     kernel_size=(1, 1)		,     strides=(1, 1)		,     padding='''VALID'''		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   List[Any]		,     __A:   List[Any]		,     __A:   Any=True   )    ->				List[Any]:
												_A								= hidden_states
												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.conva(__A   )

												_A								= self.time_emb_proj(nn.swish(__A   )   )
												_A								= jnp.expand_dims(jnp.expand_dims(__A		,     1   )		,     1   )
												_A								= hidden_states + temb

												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.dropout(__A		,     __A   )
												_A								= self.conva(__A   )

												if self.conv_shortcut is not None:
																	_A								= self.conv_shortcut(__A   )

												return hidden_states + residual



