





import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase_		:				Optional[Any]				=						pytest.mark.integration

@pytest.mark.parametrize('path'	,			['paws', 'csv'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_dataset(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			str  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.parametrize('path'	,			['accuracy'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_metric(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Tuple  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, config_name, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Union[str, Any]  						=   get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )
				assert info.config_name == config_name
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, expected'	,			[
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   get_dataset_config_names(__lowerCAmelCase )
				assert expected in config_names

@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'	,			[
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   get_dataset_infos(__lowerCAmelCase )
				assert list(infos.keys() ) == expected_configs
				_snake_case :			Any  						=   expected_configs[0]
				assert expected_config in infos
				_snake_case :			str  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits_in_first_config

@pytest.mark.parametrize(
    'path, expected_config, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   get_dataset_infos(__lowerCAmelCase )
				assert expected_config in infos
				_snake_case :			Any  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_split_names(__lowerCAmelCase	,			config_name=__lowerCAmelCase )













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






