
'''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 						_snake_case    (   A_							:     str  ,		A_							:     Any ):
  """simple docstring"""







  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






@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory"""  ,		[False, True] )
def 						_snake_case    (   A_							:     Dict  ,		A_							:     List[Any]  ,		A_							:     str  ,		A_							:     str ):
  """simple docstring"""







  a_   :						List[Any]   =				tmp_path / """cache"""
  a_   :						Optional[int]   =				{"""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_   :						Dict   =				SqlDatasetReader(
        """dataset"""  ,		"""sqlite:///""" + sqlite_path  ,		cache_dir=_A  ,		keep_in_memory=_A ).read()
  _check_sql_dataset(_A  ,		_A )






@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 						_snake_case    (   A_							:     List[str]  ,		A_							:     Optional[int]  ,		A_							:     Dict  ,		A_							:     Any ):
  """simple docstring"""







  a_   :						str   =				tmp_path / """cache"""
  a_   :						Tuple   =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  a_   :						Tuple   =				features.copy() if features else default_expected_features
  a_   :						List[Any]   =				(
      Features({feature: Value(_A ) for feature, dtype in features.items()} ) if features is not None else None
  )
  a_   :						List[Any]   =				SqlDatasetReader("""dataset"""  ,		"""sqlite:///""" + sqlite_path  ,		features=_A  ,		cache_dir=_A ).read()
  _check_sql_dataset(_A  ,		_A )






def 						_snake_case    (   A_							:     int ):
  """simple docstring"""







  with contextlib.closing(sqlitea.connect(_A ) ) as con:
    a_   :						Optional[int]   =				con.cursor()
    cur.execute("""SELECT * FROM dataset""" )
    for row in cur:
      yield row






@require_sqlalchemy
def 						_snake_case    (   A_							:     Optional[int]  ,		A_							:     List[Any]  ,		A_							:     Dict ):
  """simple docstring"""







  a_   :						List[Any]   =				tmp_path / """cache"""
  a_   :						List[Any]   =				os.path.join(_A  ,		"""tmp.sql""" )
  a_   :						Union[str, Any]   =				SqlDatasetReader("""dataset"""  ,		"""sqlite:///""" + sqlite_path  ,		cache_dir=_A ).read()
  SqlDatasetWriter(_A  ,		"""dataset"""  ,		"""sqlite:///""" + output_sqlite_path  ,		num_proc=1 ).write()

  a_   :						Dict   =				iter_sql_file(_A )
  a_   :						Optional[Any]   =				iter_sql_file(_A )

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






@require_sqlalchemy
def 						_snake_case    (   A_							:     Tuple  ,		A_							:     Optional[int]  ,		A_							:     Optional[Any] ):
  """simple docstring"""







  a_   :						int   =				tmp_path / """cache"""
  a_   :						int   =				os.path.join(_A  ,		"""tmp.sql""" )
  a_   :						Dict   =				SqlDatasetReader("""dataset"""  ,		"""sqlite:///""" + sqlite_path  ,		cache_dir=_A ).read()
  SqlDatasetWriter(_A  ,		"""dataset"""  ,		"""sqlite:///""" + output_sqlite_path  ,		num_proc=2 ).write()

  a_   :						Union[str, Any]   =				iter_sql_file(_A )
  a_   :						Dict   =				iter_sql_file(_A )

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






@require_sqlalchemy
def 						_snake_case    (   A_							:     str  ,		A_							:     Dict  ,		A_							:     Tuple ):
  """simple docstring"""







  a_   :						Optional[Any]   =				tmp_path / """cache"""
  a_   :						Optional[int]   =				os.path.join(_A  ,		"""tmp.sql""" )
  a_   :						Optional[Any]   =				SqlDatasetReader("""dataset"""  ,		"""sqlite:///""" + sqlite_path  ,		cache_dir=_A ).read()
  with pytest.raises(_A ):
    SqlDatasetWriter(_A  ,		"""dataset"""  ,		"""sqlite:///""" + output_sqlite_path  ,		num_proc=0 ).write()



import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.getLogger()

SCREAMING_SNAKE_CASE			:						List[str]				  =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int							):
   os.makedirs(__SCREAMING_SNAKE_CASE  ,				exist_ok=__SCREAMING_SNAKE_CASE							)
   __a							=	{"source": "What is love ?", "target": "life"}
   __a							=	{"train": 12, "val": 2, "test": 2}
   for split in ["train", "test", "val"]:
    for field in ["source", "target"]:
     __a							=	"\n".join([contents[field]] * n_lines[split]							)
     with open(os.path.join(__SCREAMING_SNAKE_CASE  ,				f"""{split}.{field}"""							)  ,				"w"							) as f:
      f.write(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str = "pytorch"							):
   __a							=	self.get_auto_remove_tmp_dir()
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"output"							)
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"data"							)
   self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE							)

   __a							=	f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

   if gpus > 0:
    testargs.append(f"""--gpus={gpus}"""							)
    if is_apex_available():
     testargs.append("--fp16"							)
   else:
    testargs.append("--gpus=0"							)
    testargs.append("--distributed_backend=ddp_cpu"							)
    testargs.append("--num_processes=2"							)

   __a							=	[sys.executable, str(Path(finetune_rag.__file__							).resolve()							)] + testargs
   execute_subprocess_async(__SCREAMING_SNAKE_CASE  ,				env=self.get_env()							)

   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"metrics.json"							)
   with open(__SCREAMING_SNAKE_CASE							) as f:
    __a							=	json.load(__SCREAMING_SNAKE_CASE							)
   return result

  @require_torch_gpu
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self._run_finetune(gpus=1							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self._run_finetune(gpus=2							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)


