
"""simple docstring"""

import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   List[Any]	)	-> List[Any]:






    """simple docstring"""




    assert isinstance(lowerCAmelCase	,			lowerCAmelCase	)
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'	,			[False, True]	)
def       lowercase__  (   lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   int	)	-> int:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path / 'cache'
    UpperCAmelCase       =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        UpperCAmelCase       =   CsvDatasetReader(lowerCAmelCase	,			cache_dir=lowerCAmelCase	,			keep_in_memory=lowerCAmelCase	).read()
    _check_csv_dataset(lowerCAmelCase	,			lowerCAmelCase	)




@pytest.mark.parametrize(
    'features'	,			[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]	,			)
def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Optional[Any]	)	-> List[Any]:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path / 'cache'
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    UpperCAmelCase       =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
    UpperCAmelCase       =   features.copy() if features else default_expected_features
    UpperCAmelCase       =   (
        Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
    )
    UpperCAmelCase       =   CsvDatasetReader(lowerCAmelCase	,			features=lowerCAmelCase	,			cache_dir=lowerCAmelCase	).read()
    _check_csv_dataset(lowerCAmelCase	,			lowerCAmelCase	)




@pytest.mark.parametrize('split'	,			[None, NamedSplit('train'	), 'train', 'test']	)
def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   Optional[Any]	)	-> List[str]:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path / 'cache'
    UpperCAmelCase       =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
    UpperCAmelCase       =   CsvDatasetReader(lowerCAmelCase	,			cache_dir=lowerCAmelCase	,			split=lowerCAmelCase	).read()
    _check_csv_dataset(lowerCAmelCase	,			lowerCAmelCase	)
    assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'	,			[str, list]	)
def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   Union[str, Any]	)	-> int:






    """simple docstring"""




    if issubclass(lowerCAmelCase	,			lowerCAmelCase	):
        UpperCAmelCase       =   csv_path
    elif issubclass(lowerCAmelCase	,			lowerCAmelCase	):
        UpperCAmelCase       =   [csv_path]
    UpperCAmelCase       =   tmp_path / 'cache'
    UpperCAmelCase       =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
    UpperCAmelCase       =   CsvDatasetReader(lowerCAmelCase	,			cache_dir=lowerCAmelCase	).read()
    _check_csv_dataset(lowerCAmelCase	,			lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   str	,			lowerCAmelCase			:   Union[str, Any]=("train",)	)	-> Optional[Any]:






    """simple docstring"""




    assert isinstance(lowerCAmelCase	,			lowerCAmelCase	)
    for split in splits:
        UpperCAmelCase       =   dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 3
        assert dataset.column_names == ["col_1", "col_2", "col_3"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'	,			[False, True]	)
def       lowercase__  (   lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   int	)	-> Any:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path / 'cache'
    UpperCAmelCase       =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        UpperCAmelCase       =   CsvDatasetReader({'train': csv_path}	,			cache_dir=lowerCAmelCase	,			keep_in_memory=lowerCAmelCase	).read()
    _check_csv_datasetdict(lowerCAmelCase	,			lowerCAmelCase	)




@pytest.mark.parametrize(
    'features'	,			[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]	,			)
def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   Optional[int]	)	-> List[Any]:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path / 'cache'
    # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
    UpperCAmelCase       =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
    UpperCAmelCase       =   features.copy() if features else default_expected_features
    UpperCAmelCase       =   (
        Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
    )
    UpperCAmelCase       =   CsvDatasetReader({'train': csv_path}	,			features=lowerCAmelCase	,			cache_dir=lowerCAmelCase	).read()
    _check_csv_datasetdict(lowerCAmelCase	,			lowerCAmelCase	)




@pytest.mark.parametrize('split'	,			[None, NamedSplit('train'	), 'train', 'test']	)
def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   int	)	-> Optional[int]:






    """simple docstring"""




    if split:
        UpperCAmelCase       =   {split: csv_path}
    else:
        UpperCAmelCase       =   {'train': csv_path, 'test': csv_path}
    UpperCAmelCase       =   tmp_path / 'cache'
    UpperCAmelCase       =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
    UpperCAmelCase       =   CsvDatasetReader(lowerCAmelCase	,			cache_dir=lowerCAmelCase	).read()
    _check_csv_datasetdict(lowerCAmelCase	,			lowerCAmelCase	,			splits=list(path.keys()	)	)
    assert all(dataset[split].split == split for split in path.keys()	)




def       lowercase__  (   lowerCAmelCase			:   Dict	)	-> int:






    """simple docstring"""




    with open(lowerCAmelCase	,			encoding='utf-8'	) as csvfile:
        yield from csv.reader(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[int]:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path / 'cache'
    UpperCAmelCase       =   os.path.join(lowerCAmelCase	,			'tmp.csv'	)
    UpperCAmelCase       =   CsvDatasetReader({'train': csv_path}	,			cache_dir=lowerCAmelCase	).read()
    CsvDatasetWriter(dataset['train']	,			lowerCAmelCase	,			num_proc=1	).write()

    UpperCAmelCase       =   iter_csv_file(lowerCAmelCase	)
    UpperCAmelCase       =   iter_csv_file(lowerCAmelCase	)

    for rowa, rowa in zip(lowerCAmelCase	,			lowerCAmelCase	):
        assert rowa == rowa




def       lowercase__  (   lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[int]	)	-> str:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path / 'cache'
    UpperCAmelCase       =   os.path.join(lowerCAmelCase	,			'tmp.csv'	)
    UpperCAmelCase       =   CsvDatasetReader({'train': csv_path}	,			cache_dir=lowerCAmelCase	).read()
    CsvDatasetWriter(dataset['train']	,			lowerCAmelCase	,			num_proc=2	).write()

    UpperCAmelCase       =   iter_csv_file(lowerCAmelCase	)
    UpperCAmelCase       =   iter_csv_file(lowerCAmelCase	)

    for rowa, rowa in zip(lowerCAmelCase	,			lowerCAmelCase	):
        assert rowa == rowa







def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   Optional[int]	)	-> Dict:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path / 'cache'
    UpperCAmelCase       =   os.path.join(lowerCAmelCase	,			'tmp.csv'	)
    UpperCAmelCase       =   CsvDatasetReader({'train': csv_path}	,			cache_dir=lowerCAmelCase	).read()
    with pytest.raises(lowerCAmelCase	):
        CsvDatasetWriter(dataset['train']	,			lowerCAmelCase	,			num_proc=0	)





"""simple docstring"""

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        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):




      @register_to_config
      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = False				,    )  ->   List[Any]:
          super().__init__()

          UpperCAmelCase       =   nn.Linear(lowercase_				,    lowercase_				,    bias=lowercase_							)

          UpperCAmelCase       =   nn.Embedding(lowercase_				,    lowercase_							)
          UpperCAmelCase       =   False

          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)

          UpperCAmelCase       =   TaConfig(
              d_model=lowercase_				,    num_heads=lowercase_				,    d_kv=lowercase_				,    d_ff=lowercase_				,    feed_forward_proj=lowercase_				,    dropout_rate=lowercase_				,    is_decoder=lowercase_				,    is_encoder_decoder=lowercase_				,    )
          UpperCAmelCase       =   nn.ModuleList()
          for lyr_num in range(lowercase_							):
              UpperCAmelCase       =   TaBlock(lowercase_							)
              self.encoders.append(lowercase_							)

          UpperCAmelCase       =   TaLayerNorm(lowercase_							)
          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   self.input_proj(lowercase_							)

          # terminal relative positional encodings
          UpperCAmelCase       =   encoder_inputs.shape[1]
          UpperCAmelCase       =   torch.arange(lowercase_				,    device=encoder_inputs.device							)

          UpperCAmelCase       =   encoder_inputs_mask.sum(-1							)
          UpperCAmelCase       =   torch.roll(input_positions.unsqueeze(0							)				,    tuple(seq_lens.tolist()							)				,    dims=0							)
          x += self.position_encoding(lowercase_							)

          UpperCAmelCase       =   self.dropout_pre(lowercase_							)

          # inverted the attention mask
          UpperCAmelCase       =   encoder_inputs.size()
          UpperCAmelCase       =   self.get_extended_attention_mask(lowercase_				,    lowercase_							)

          for lyr in self.encoders:
              UpperCAmelCase       =   lyr(lowercase_				,    lowercase_							)[0]
          UpperCAmelCase       =   self.layer_norm(lowercase_							)

          return self.dropout_post(lowercase_							), encoder_inputs_mask



