





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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       assert isinstance(UpperCamelCase_       ,		UpperCamelCase_	)
       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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		int:
       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(UpperCamelCase_       ,		cache_dir=UpperCamelCase_       ,		keep_in_memory=UpperCamelCase_	).read()
       _check_csv_dataset(UpperCamelCase_       ,		UpperCamelCase_	)




@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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		str:
       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(UpperCamelCase_	) for feature, dtype in features.items()}	) if features is not None else None
       )
       UpperCamelCase_ 							=	CsvDatasetReader(UpperCamelCase_       ,		features=UpperCamelCase_       ,		cache_dir=UpperCamelCase_	).read()
       _check_csv_dataset(UpperCamelCase_       ,		UpperCamelCase_	)




@pytest.mark.parametrize("split"       ,		[None, NamedSplit("train"	), "train", "test"]	)
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	tmp_path / "cache"
       UpperCamelCase_ 							=	{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
       UpperCamelCase_ 							=	CsvDatasetReader(UpperCamelCase_       ,		cache_dir=UpperCamelCase_       ,		split=UpperCamelCase_	).read()
       _check_csv_dataset(UpperCamelCase_       ,		UpperCamelCase_	)
       assert dataset.split == split if split else "train"




@pytest.mark.parametrize("path_type"       ,		[str, list]	)
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       if issubclass(UpperCamelCase_       ,		UpperCamelCase_	):
              UpperCamelCase_ 							=	csv_path
       elif issubclass(UpperCamelCase_       ,		UpperCamelCase_	):
              UpperCamelCase_ 							=	[csv_path]
       UpperCamelCase_ 							=	tmp_path / "cache"
       UpperCamelCase_ 							=	{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
       UpperCamelCase_ 							=	CsvDatasetReader(UpperCamelCase_       ,		cache_dir=UpperCamelCase_	).read()
       _check_csv_dataset(UpperCamelCase_       ,		UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=("train",)	)  ->		int:
       assert isinstance(UpperCamelCase_       ,		UpperCamelCase_	)
       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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		int:
       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=UpperCamelCase_       ,		keep_in_memory=UpperCamelCase_	).read()
       _check_csv_datasetdict(UpperCamelCase_       ,		UpperCamelCase_	)




@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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       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(UpperCamelCase_	) for feature, dtype in features.items()}	) if features is not None else None
       )
       UpperCamelCase_ 							=	CsvDatasetReader({"train": csv_path}       ,		features=UpperCamelCase_       ,		cache_dir=UpperCamelCase_	).read()
       _check_csv_datasetdict(UpperCamelCase_       ,		UpperCamelCase_	)




@pytest.mark.parametrize("split"       ,		[None, NamedSplit("train"	), "train", "test"]	)
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[int]:
       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(UpperCamelCase_       ,		cache_dir=UpperCamelCase_	).read()
       _check_csv_datasetdict(UpperCamelCase_       ,		UpperCamelCase_       ,		splits=list(path.keys()	)	)
       assert all(dataset[split].split == split for split in path.keys()	)




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Any:
       with open(UpperCamelCase_       ,		encoding="utf-8"	) as csvfile:
              yield from csv.reader(UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	tmp_path / "cache"
       UpperCamelCase_ 							=	os.path.join(UpperCamelCase_       ,		"tmp.csv"	)
       UpperCamelCase_ 							=	CsvDatasetReader({"train": csv_path}       ,		cache_dir=UpperCamelCase_	).read()
       CsvDatasetWriter(dataset["train"]       ,		UpperCamelCase_       ,		num_proc=1	).write()

       UpperCamelCase_ 							=	iter_csv_file(UpperCamelCase_	)
       UpperCamelCase_ 							=	iter_csv_file(UpperCamelCase_	)

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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       UpperCamelCase_ 							=	tmp_path / "cache"
       UpperCamelCase_ 							=	os.path.join(UpperCamelCase_       ,		"tmp.csv"	)
       UpperCamelCase_ 							=	CsvDatasetReader({"train": csv_path}       ,		cache_dir=UpperCamelCase_	).read()
       CsvDatasetWriter(dataset["train"]       ,		UpperCamelCase_       ,		num_proc=2	).write()

       UpperCamelCase_ 							=	iter_csv_file(UpperCamelCase_	)
       UpperCamelCase_ 							=	iter_csv_file(UpperCamelCase_	)

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







def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Any:
       UpperCamelCase_ 							=	tmp_path / "cache"
       UpperCamelCase_ 							=	os.path.join(UpperCamelCase_       ,		"tmp.csv"	)
       UpperCamelCase_ 							=	CsvDatasetReader({"train": csv_path}       ,		cache_dir=UpperCamelCase_	).read()
       with pytest.raises(UpperCamelCase_	):
              CsvDatasetWriter(dataset["train"]       ,		UpperCamelCase_       ,		num_proc=0	)








from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'mnaylor/mega-base-wikitext': 'https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json',
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Union[str, Any]       = '''mega'''

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=30522       , _SCREAMING_SNAKE_CASE:			Optional[Any]=128       , _SCREAMING_SNAKE_CASE:			Any=4       , _SCREAMING_SNAKE_CASE:			Tuple=256       , _SCREAMING_SNAKE_CASE:			List[str]=16       , _SCREAMING_SNAKE_CASE:			Dict=True       , _SCREAMING_SNAKE_CASE:			Optional[int]=64       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=False       , _SCREAMING_SNAKE_CASE:			List[Any]=-1       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			Optional[Any]=True       , _SCREAMING_SNAKE_CASE:			Optional[int]="scalenorm"       , _SCREAMING_SNAKE_CASE:			int=True       , _SCREAMING_SNAKE_CASE:			List[str]="silu"       , _SCREAMING_SNAKE_CASE:			int="softmax"       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[int]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Tuple=False       , _SCREAMING_SNAKE_CASE:			List[Any]=True       , _SCREAMING_SNAKE_CASE:			Tuple=256       , _SCREAMING_SNAKE_CASE:			Any=True       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2048       , _SCREAMING_SNAKE_CASE:			Any=False       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2       , _SCREAMING_SNAKE_CASE:			List[str]=0.02       , _SCREAMING_SNAKE_CASE:			Tuple=0.2       , _SCREAMING_SNAKE_CASE:			Optional[int]=0.02       , _SCREAMING_SNAKE_CASE:			Tuple=1.0       , _SCREAMING_SNAKE_CASE:			List[str]=1       , _SCREAMING_SNAKE_CASE:			List[Any]=0       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2       , _SCREAMING_SNAKE_CASE:			Optional[int]="rotary"       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			Any=True       , _SCREAMING_SNAKE_CASE:			Optional[Any]=True       , **_SCREAMING_SNAKE_CASE:			Union[str, Any]       , )							->			Optional[Any]:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	hidden_size
              UpperCamelCase_ 							=	num_hidden_layers
              UpperCamelCase_ 							=	activation
              UpperCamelCase_ 							=	attention_activation
              UpperCamelCase_ 							=	intermediate_size
              UpperCamelCase_ 							=	ema_projection_size
              UpperCamelCase_ 							=	bidirectional
              UpperCamelCase_ 							=	shared_representation_size
              UpperCamelCase_ 							=	use_chunking
              UpperCamelCase_ 							=	chunk_size
              UpperCamelCase_ 							=	truncation
              UpperCamelCase_ 							=	normalize_before_mega
              UpperCamelCase_ 							=	normalization_type
              UpperCamelCase_ 							=	norm_affine
              UpperCamelCase_ 							=	dropout_prob
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	attention_probs_dropout_prob
              UpperCamelCase_ 							=	use_feature_dropout
              UpperCamelCase_ 							=	use_normalized_ffn
              UpperCamelCase_ 							=	nffn_hidden_size
              UpperCamelCase_ 							=	normalize_before_ffn
              UpperCamelCase_ 							=	nffn_activation_dropout_prob
              UpperCamelCase_ 							=	max_positions
              UpperCamelCase_ 							=	add_token_type_embeddings
              UpperCamelCase_ 							=	type_vocab_size
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	ema_delta_alpha_range
              UpperCamelCase_ 							=	ema_beta_range
              UpperCamelCase_ 							=	ema_gamma_omega_range
              UpperCamelCase_ 							=	relative_positional_bias
              UpperCamelCase_ 							=	use_cache
              UpperCamelCase_ 							=	classifier_dropout
              UpperCamelCase_ 							=	add_lm_hidden_dense_layer
              UpperCamelCase_ 							=	1  # not used but required by Hugging Face



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       @property
       def lowercase			(		self:			Optional[int]		)							->			Mapping[str, Mapping[int, str]]:




              """simple docstring"""


              if self.task == "multiple-choice":
                     UpperCamelCase_ 							=	{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase_ 							=	{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                  ]		)

