



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					)						->      str:
			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					)						->      str:
			A__       =    tmp_path / 'cache'
			A__       =    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						A__       =    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					)						->      Union[str, Any]:
			A__       =    tmp_path / 'cache'
			A__       =    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			A__       =    features.copy() if features else default_expected_features
			A__       =    (
			    Features({feature: Value(__UpperCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
			)
			A__       =    SqlDatasetReader('dataset' ,	'sqlite:///' + sqlite_path ,	features=__UpperCamelCase ,	cache_dir=__UpperCamelCase					).read()
			_check_sql_dataset(__UpperCamelCase ,	__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      Union[str, Any]:
			with contextlib.closing(sqlitea.connect(__UpperCamelCase					)					) as con:
						A__       =    con.cursor()
						cur.execute('SELECT * FROM dataset'					)
						for row in cur:
									yield row


@require_sqlalchemy
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Any:
			A__       =    tmp_path / 'cache'
			A__       =    os.path.join(__UpperCamelCase ,	'tmp.sql'					)
			A__       =    SqlDatasetReader('dataset' ,	'sqlite:///' + sqlite_path ,	cache_dir=__UpperCamelCase					).read()
			SqlDatasetWriter(__UpperCamelCase ,	'dataset' ,	'sqlite:///' + output_sqlite_path ,	num_proc=1					).write()

			A__       =    iter_sql_file(__UpperCamelCase					)
			A__       =    iter_sql_file(__UpperCamelCase					)

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


@require_sqlalchemy
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Union[str, Any]:
			A__       =    tmp_path / 'cache'
			A__       =    os.path.join(__UpperCamelCase ,	'tmp.sql'					)
			A__       =    SqlDatasetReader('dataset' ,	'sqlite:///' + sqlite_path ,	cache_dir=__UpperCamelCase					).read()
			SqlDatasetWriter(__UpperCamelCase ,	'dataset' ,	'sqlite:///' + output_sqlite_path ,	num_proc=2					).write()

			A__       =    iter_sql_file(__UpperCamelCase					)
			A__       =    iter_sql_file(__UpperCamelCase					)

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






@require_sqlalchemy
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      str:
			A__       =    tmp_path / 'cache'
			A__       =    os.path.join(__UpperCamelCase ,	'tmp.sql'					)
			A__       =    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 unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




