'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_UpperCAmelCase						:    Tuple     					=					logging.get_logger(__name__)

_UpperCAmelCase						:    Optional[int]     					=					{
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      "plbart"
   __lowerCAmelCase    						=      ["past_key_values"]
   __lowerCAmelCase    						=      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
   def __init__(			self			: List[str],   UpperCamelCase__			: str=5_00_05,   UpperCamelCase__			: Dict=10_24,   UpperCamelCase__			: str=6,   UpperCamelCase__			: Union[str, Any]=30_72,   UpperCamelCase__			: Tuple=12,   UpperCamelCase__			: int=6,   UpperCamelCase__			: Union[str, Any]=30_72,   UpperCamelCase__			: Any=12,   UpperCamelCase__			: int=0.0,   UpperCamelCase__			: Tuple=0.0,   UpperCamelCase__			: str=True,   UpperCamelCase__			: Optional[int]=True,   UpperCamelCase__			: int="gelu",   UpperCamelCase__			: List[Any]=7_68,   UpperCamelCase__			: Tuple=0.1,   UpperCamelCase__			: Dict=0.1,   UpperCamelCase__			: Dict=0.0,   UpperCamelCase__			: Optional[int]=0.02,   UpperCamelCase__			: int=0.0,   UpperCamelCase__			: Tuple=True,   UpperCamelCase__			: Dict=1,   UpperCamelCase__			: Dict=0,   UpperCamelCase__			: str=2,   UpperCamelCase__			: List[Any]=2,   **UpperCamelCase__			: Any,   )     -> str:
        _A								=     vocab_size
        _A								=     max_position_embeddings
        _A								=     d_model
        _A								=     encoder_ffn_dim
        _A								=     encoder_layers
        _A								=     encoder_attention_heads
        _A								=     decoder_ffn_dim
        _A								=     decoder_layers
        _A								=     decoder_attention_heads
        _A								=     dropout
        _A								=     attention_dropout
        _A								=     activation_dropout
        _A								=     activation_function
        _A								=     init_std
        _A								=     encoder_layerdrop
        _A								=     decoder_layerdrop
        _A								=     classifier_dropout
        _A								=     use_cache
        _A								=     encoder_layers
        _A								=     scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=UpperCamelCase__,   bos_token_id=UpperCamelCase__,   eos_token_id=UpperCamelCase__,   is_encoder_decoder=UpperCamelCase__,   forced_eos_token_id=UpperCamelCase__,   **UpperCamelCase__,   )







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   @property
   def   __UpperCAmelCase      (			self			: Dict    )     -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'sequence'}),
                ('attention_mask', {0: 'batch', 1: 'sequence'}),
            ]    )

   @property
   def   __UpperCAmelCase      (			self			: int    )     -> Mapping[str, Mapping[int, str]]:
        if self.use_past:
             return OrderedDict(
                 [
                     ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                     ('past_keys', {0: 'batch', 2: 'sequence'}),
                     ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
                 ]    )
        else:
             return OrderedDict(
                 [
                     ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                     ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
                 ]    )




'''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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : List[str]							):
     assert isinstance(__snake_case      ,		__snake_case							)
     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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[Any]      ,		__snake_case       : List[str]							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





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





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : str      ,		__snake_case       : int							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case      ,		split=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)
     assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'      ,		[str, list]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : int      ,		__snake_case       : Optional[int]							):
     if issubclass(__snake_case      ,		__snake_case							):
          _A								=     csv_path
     elif issubclass(__snake_case      ,		__snake_case							):
          _A								=     [csv_path]
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : int      ,		__snake_case       : int=("train",)							):
     assert isinstance(__snake_case      ,		__snake_case							)
     for split in splits:
          _A								=     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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





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





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : int							):
     if split:
          _A								=     {split: csv_path}
     else:
          _A								=     {'train': csv_path, 'test': csv_path}
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case      ,		splits=list(path.keys()							)							)
     assert all(dataset[split].split == split for split in path.keys()							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]							):
     with open(__snake_case      ,		encoding='utf-8'							) as csvfile:
          yield from csv.reader(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=1							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

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





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=2							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

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





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Tuple							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     with pytest.raises(__snake_case							):
          CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=0							)



