





"""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    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							assert isinstance(_A   ,    _A			)
							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    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							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(_A   ,    cache_dir=_A   ,    keep_in_memory=_A			).read()
							_check_csv_dataset(_A   ,    _A			)






@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    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							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(_A			) for feature, dtype in features.items()}			) if features is not None else None
							)
							A						=    CsvDatasetReader(_A   ,    features=_A   ,    cache_dir=_A			).read()
							_check_csv_dataset(_A   ,    _A			)






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






@pytest.mark.parametrize("path_type"   ,    [str, list]			)
def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):







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






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase=("train",)			):
							assert isinstance(_A   ,    _A			)
							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    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							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=_A   ,    keep_in_memory=_A			).read()
							_check_csv_datasetdict(_A   ,    _A			)






@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    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							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(_A			) for feature, dtype in features.items()}			) if features is not None else None
							)
							A						=    CsvDatasetReader({"train": csv_path}   ,    features=_A   ,    cache_dir=_A			).read()
							_check_csv_datasetdict(_A   ,    _A			)






@pytest.mark.parametrize("split"   ,    [None, NamedSplit("train"			), "train", "test"]			)
def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):







							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(_A   ,    cache_dir=_A			).read()
							_check_csv_datasetdict(_A   ,    _A   ,    splits=list(path.keys()			)			)
							assert all(dataset[split].split == split for split in path.keys()			)






def    A__							(     UpperCamelCase			):







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






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    tmp_path / "cache"
							A						=    os.path.join(_A   ,    "tmp.csv"			)
							A						=    CsvDatasetReader({"train": csv_path}   ,    cache_dir=_A			).read()
							CsvDatasetWriter(dataset["train"]   ,    _A   ,    num_proc=1			).write()

							A						=    iter_csv_file(_A			)
							A						=    iter_csv_file(_A			)

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






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    tmp_path / "cache"
							A						=    os.path.join(_A   ,    "tmp.csv"			)
							A						=    CsvDatasetReader({"train": csv_path}   ,    cache_dir=_A			).read()
							CsvDatasetWriter(dataset["train"]   ,    _A   ,    num_proc=2			).write()

							A						=    iter_csv_file(_A			)
							A						=    iter_csv_file(_A			)

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






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    tmp_path / "cache"
							A						=    os.path.join(_A   ,    "tmp.csv"			)
							A						=    CsvDatasetReader({"train": csv_path}   ,    cache_dir=_A			).read()
							with pytest.raises(_A			):
														CsvDatasetWriter(dataset["train"]   ,    _A   ,    num_proc=0			)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  'yoso'



   def __init__(							self,       lowercase_=50265,       lowercase_=768,       lowercase_=12,       lowercase_=12,       lowercase_=3072,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=4096,       lowercase_=1,       lowercase_=0.02,       lowercase_=1E-12,       lowercase_="absolute",       lowercase_=True,       lowercase_=9,       lowercase_=64,       lowercase_=None,       lowercase_=True,       lowercase_=True,       lowercase_=1,       lowercase_=0,       lowercase_=2,       **lowercase_,       )				->       List[str]:






     """simple docstring"""






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

     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =initializer_range
     a__    =type_vocab_size
     a__    =layer_norm_eps
     a__    =position_embedding_type
     a__    =use_expectation
     a__    =hash_code_len
     a__    =num_hash
     a__    =conv_window
     a__    =use_fast_hash
     a__    =lsh_backward
