import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      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


@require_sqlalchemy
@pytest.mark.parametrize("keep_in_memory"     ,      [False, True]				)
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE_          =  SqlDatasetReader(
                "dataset"     ,      "sqlite:///" + sqlite_path     ,      cache_dir=__UpperCamelCase     ,      keep_in_memory=__UpperCamelCase				).read()
      _check_sql_dataset(__UpperCamelCase     ,      __UpperCamelCase				)


@require_sqlalchemy
@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     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      SCREAMING_SNAKE_CASE_          =  features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_          =  (
          Features({feature: Value(__UpperCamelCase				) for feature, dtype in features.items()}				) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_          =  SqlDatasetReader("dataset"     ,      "sqlite:///" + sqlite_path     ,      features=__UpperCamelCase     ,      cache_dir=__UpperCamelCase				).read()
      _check_sql_dataset(__UpperCamelCase     ,      __UpperCamelCase				)


def      a__ (		__UpperCamelCase				):
      with contextlib.closing(sqlitea.connect(__UpperCamelCase				)				) as con:
            SCREAMING_SNAKE_CASE_          =  con.cursor()
            cur.execute("SELECT * FROM dataset"				)
            for row in cur:
                  yield row


@require_sqlalchemy
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  os.path.join(__UpperCamelCase     ,      "tmp.sql"				)
      SCREAMING_SNAKE_CASE_          =  SqlDatasetReader("dataset"     ,      "sqlite:///" + sqlite_path     ,      cache_dir=__UpperCamelCase				).read()
      SqlDatasetWriter(__UpperCamelCase     ,      "dataset"     ,      "sqlite:///" + output_sqlite_path     ,      num_proc=1				).write()

      SCREAMING_SNAKE_CASE_          =  iter_sql_file(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  iter_sql_file(__UpperCamelCase				)

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


@require_sqlalchemy
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  os.path.join(__UpperCamelCase     ,      "tmp.sql"				)
      SCREAMING_SNAKE_CASE_          =  SqlDatasetReader("dataset"     ,      "sqlite:///" + sqlite_path     ,      cache_dir=__UpperCamelCase				).read()
      SqlDatasetWriter(__UpperCamelCase     ,      "dataset"     ,      "sqlite:///" + output_sqlite_path     ,      num_proc=2				).write()

      SCREAMING_SNAKE_CASE_          =  iter_sql_file(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  iter_sql_file(__UpperCamelCase				)

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





@require_sqlalchemy
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  os.path.join(__UpperCamelCase     ,      "tmp.sql"				)
      SCREAMING_SNAKE_CASE_          =  SqlDatasetReader("dataset"     ,      "sqlite:///" + sqlite_path     ,      cache_dir=__UpperCamelCase				).read()
      with pytest.raises(__UpperCamelCase				):
            SqlDatasetWriter(__UpperCamelCase     ,      "dataset"     ,      "sqlite:///" + output_sqlite_path     ,      num_proc=0				).write()



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :  str					     =       logging.getLogger(__name__)


@dataclass
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						field(
      default=0.0  ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''whether to use adafactor'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default='''linear'''  ,      metadata={'''help''': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}"}  ,      )


