





import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    'files'	,			[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Tuple  						=   tmp_path_factory.mktemp('dset_infos_dir' )
				if "full:README.md" in files:
								with open(dataset_infos_dir / 'README.md'	,			'w' ) as f:
												f.write('---\ndataset_info:\n  dataset_size: 42\n---' )
				if "empty:README.md" in files:
								with open(dataset_infos_dir / 'README.md'	,			'w' ) as f:
												f.write('' )
    # we want to support dataset_infos.json for backward compatibility
				if "dataset_infos.json" in files:
								with open(dataset_infos_dir / 'dataset_infos.json'	,			'w' ) as f:
												f.write('{"default": {"dataset_size": 42}}' )
				_snake_case :			int  						=   DatasetInfosDict.from_directory(__lowerCAmelCase )
				assert dataset_infos
				assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    'dataset_info'	,			[
        DatasetInfo(),
        DatasetInfo(
            description='foo'	,			features=Features({'a': Value('int32' )} )	,			builder_name='builder'	,			config_name='config'	,			version='1.0.0'	,			splits=[{'name': 'train'}]	,			download_size=42	,			),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Dict  						=   str(__lowerCAmelCase )
				dataset_info.write_to_directory(__lowerCAmelCase )
				_snake_case :			Union[str, Any]  						=   DatasetInfo.from_directory(__lowerCAmelCase )
				assert dataset_info == reloaded
				assert os.path.exists(os.path.join(__lowerCAmelCase	,			'dataset_info.json' ) )

def       A__(		):
				_snake_case :			Optional[int]  						=   DatasetInfo(
				    description='foo'	,			citation='bar'	,			homepage='https://foo.bar'	,			license='CC0'	,			features=Features({'a': Value('int32' )} )	,			post_processed={}	,			supervised_keys=()	,			task_templates=[]	,			builder_name='builder'	,			config_name='config'	,			version='1.0.0'	,			splits=[{'name': 'train', 'num_examples': 42}]	,			download_checksums={}	,			download_size=13_37	,			post_processing_size=4_42	,			dataset_size=12_34	,			size_in_bytes=13_37 + 4_42 + 12_34	,			)
				_snake_case :			int  						=   dataset_info._to_yaml_dict()
				assert sorted(__lowerCAmelCase ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								assert key in dataset_info_yaml_dict
								assert isinstance(dataset_info_yaml_dict[key]	,			(list, dict, int, str) )
				_snake_case :			str  						=   yaml.safe_dump(__lowerCAmelCase )
				_snake_case :			Tuple  						=   yaml.safe_load(__lowerCAmelCase )
				assert dataset_info_yaml_dict == reloaded

def       A__(		):
				_snake_case :			int  						=   DatasetInfo()
				_snake_case :			List[str]  						=   dataset_info._to_yaml_dict()
				assert dataset_info_yaml_dict == {}

@pytest.mark.parametrize(
    'dataset_infos_dict'	,			[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()} ),
        DatasetInfosDict({'my_config_name': DatasetInfo()} ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'	,			features=Features({'a': Value('int32' )} )	,			builder_name='builder'	,			config_name='config'	,			version='1.0.0'	,			splits=[{'name': 'train'}]	,			download_size=42	,			)
            } ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42 ),
                'v2': DatasetInfo(dataset_size=13_37 ),
            } ),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Dict  						=   str(__lowerCAmelCase )
				dataset_infos_dict.write_to_directory(__lowerCAmelCase )
				_snake_case :			List[str]  						=   DatasetInfosDict.from_directory(__lowerCAmelCase )

				# the config_name of the dataset_infos_dict take over the attribute
				for config_name, dataset_info in dataset_infos_dict.items():
								_snake_case :			Tuple  						=   config_name
								# the yaml representation doesn't include fields like description or citation
								# so we just test that we can recover what we can from the yaml
								_snake_case :			List[str]  						=   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
				assert dataset_infos_dict == reloaded

				if dataset_infos_dict:
								assert os.path.exists(os.path.join(__lowerCAmelCase	,			'README.md' ) )













import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 			lowercase				(							a_		,			a_		,			a_     ):



						"""simple docstring"""

						@register_to_config
						def __init__(    self			:		Any ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										super().__init__()

										_snake_case :			Optional[int]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			List[str]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   False

										_snake_case :			Tuple  						=   nn.Dropout(p=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   TaConfig(
										    vocab_size=lowerCamelCase_ ,				d_model=lowerCamelCase_ ,				num_heads=lowerCamelCase_ ,				d_kv=lowerCamelCase_ ,				d_ff=lowerCamelCase_ ,				dropout_rate=lowerCamelCase_ ,				feed_forward_proj=lowerCamelCase_ ,				is_decoder=lowerCamelCase_ ,				is_encoder_decoder=lowerCamelCase_ ,				)

										_snake_case :			Union[str, Any]  						=   nn.ModuleList()
										for lyr_num in range(lowerCamelCase_					):
														_snake_case :			Any  						=   TaBlock(lowerCamelCase_					)
														self.encoders.append(lowerCamelCase_					)

										_snake_case :			Tuple  						=   TaLayerNorm(lowerCamelCase_					)
										_snake_case :			List[str]  						=   nn.Dropout(p=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.token_embedder(lowerCamelCase_					)

										_snake_case :			List[Any]  						=   encoder_input_tokens.shape[1]
										_snake_case :			Any  						=   torch.arange(lowerCamelCase_ ,				device=encoder_input_tokens.device					)
										x += self.position_encoding(lowerCamelCase_					)

										_snake_case :			Tuple  						=   self.dropout_pre(lowerCamelCase_					)

										# inverted the attention mask
										_snake_case :			Dict  						=   encoder_input_tokens.size()
										_snake_case :			Optional[int]  						=   self.get_extended_attention_mask(lowerCamelCase_ ,				lowerCamelCase_					)

										for lyr in self.encoders:
														_snake_case :			str  						=   lyr(lowerCamelCase_ ,				lowerCamelCase_					)[0]
										_snake_case :			Any  						=   self.layer_norm(lowerCamelCase_					)

										return self.dropout_post(lowerCamelCase_					), encoder_inputs_mask






