"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__   			=      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      ['CodeGenTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
     'CodeGenForCausalLM',
     'CodeGenModel',
     'CodeGenPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
 from .tokenization_codegen import CodeGenTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_codegen_fast import CodeGenTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_codegen import (
      CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
      CodeGenForCausalLM,
      CodeGenModel,
      CodeGenPreTrainedModel,
  )

else:
 import sys

 lowercase__   			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""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 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Any      ):
     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





@pytest.mark.parametrize("""keep_in_memory"""       , [False, True]      )
def 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Optional[Any]      ):
     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(UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





@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  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Dict      ):
     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(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





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





@pytest.mark.parametrize("""path_type"""       , [str, list]      )
def 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Dict      ):
     if issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	csv_path
     elif issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	[csv_path]
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       int=("train",)      ):
     assert isinstance(UpperCAmelCase_       , UpperCAmelCase_      )
     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 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       List[str]      ):
     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=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_      )





@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  (     UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Optional[Any]      ):
     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(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_      )





@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""      ), """train""", """test"""]      )
def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Optional[int]      ):
     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(UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_       , splits=list(path.keys()      )      )
     assert all(dataset[split].split == split for split in path.keys()      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]      ):
     with open(UpperCAmelCase_       , encoding="""utf-8"""      ) as csvfile:
          yield from csv.reader(UpperCAmelCase_      )





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

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

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





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

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

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





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






