


'''simple docstring'''



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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Any				)	->						Any:
     with contextlib.closing(sqlitea.connect(UpperCAmelCase_				)				) as con:
          __lowerCamelCase  :      List[str]														=						con.cursor()
          cur.execute('SELECT * FROM dataset'				)
          for row in cur:
               yield row



@require_sqlalchemy
def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							Tuple ,       UpperCAmelCase_       :							Optional[Any]				)	->						Union[str, Any]:
     __lowerCamelCase  :      Optional[int]														=						tmp_path / 'cache'
     __lowerCamelCase  :      Any														=						os.path.join(UpperCAmelCase_ ,       'tmp.sql'				)
     __lowerCamelCase  :      List[str]														=						SqlDatasetReader('dataset' ,       'sqlite:///' + sqlite_path ,       cache_dir=UpperCAmelCase_				).read()
     SqlDatasetWriter(UpperCAmelCase_ ,       'dataset' ,       'sqlite:///' + output_sqlite_path ,       num_proc=1				).write()

     __lowerCamelCase  :      Optional[Any]														=						iter_sql_file(UpperCAmelCase_				)
     __lowerCamelCase  :      int														=						iter_sql_file(UpperCAmelCase_				)

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



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

     __lowerCamelCase  :      str														=						iter_sql_file(UpperCAmelCase_				)
     __lowerCamelCase  :      List[Any]														=						iter_sql_file(UpperCAmelCase_				)

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



@require_sqlalchemy
def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							List[str]				)	->						int:
     __lowerCamelCase  :      str														=						tmp_path / 'cache'
     __lowerCamelCase  :      Dict														=						os.path.join(UpperCAmelCase_ ,       'tmp.sql'				)
     __lowerCamelCase  :      int														=						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 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						@slow
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"   )
											__a					:						Dict      =					AutoTokenizer.from_pretrained("google/mt5-small"   )

											__a					:						Any      =					tokenizer("Hello there" ,					return_tensors="np"   ).input_ids
											__a					:						Union[str, Any]      =					tokenizer("Hi I am" ,					return_tensors="np"   ).input_ids

											__a					:						Union[str, Any]      =					shift_tokens_right(__UpperCAmelCase ,					model.config.pad_token_id ,					model.config.decoder_start_token_id   )

											__a					:						Tuple      =					model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase   ).logits
											__a					:						str      =					optax.softmax_cross_entropy(__UpperCAmelCase ,					onehot(__UpperCAmelCase ,					logits.shape[-1]   )   ).mean()

											__a					:						List[str]      =					-(labels.shape[-1] * loss.item())

											__a					:						Optional[int]      =					-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )





