'''simple docstring'''






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
   from .testing_utils import (
       floats_tensor,
       load_hf_numpy,
       load_image,
       load_numpy,
       load_pt,
       nightly,
       parse_flag_from_env,
       print_tensor_test,
       require_torch_a,
       require_torch_gpu,
       skip_mps,
       slow,
       torch_all_close,
       torch_device,
   )
   from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_UpperCAmelCase						:    Optional[Any]     					=					get_logger(__name__)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any							):
     if version.parse(__snake_case							) < version.parse(__snake_case							):
          if "dev" in min_version:
               _A								=     (
                   'This example requires a source install from HuggingFace diffusers (see '
                   '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
               )
          else:
               _A								=     F'This example requires a minimum version of {min_version},'
          error_message += F' but the version found is {__version__}.\n'
          raise ImportError(__snake_case							)




'''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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : List[str]							):
     assert isinstance(__snake_case      ,		__snake_case							)
     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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[Any]      ,		__snake_case       : 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(__snake_case      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





@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    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Any      ,		__snake_case       : 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(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader(__snake_case      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : str      ,		__snake_case       : int							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case      ,		split=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)
     assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'      ,		[str, list]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : int      ,		__snake_case       : Optional[int]							):
     if issubclass(__snake_case      ,		__snake_case							):
          _A								=     csv_path
     elif issubclass(__snake_case      ,		__snake_case							):
          _A								=     [csv_path]
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : int      ,		__snake_case       : int=("train",)							):
     assert isinstance(__snake_case      ,		__snake_case							)
     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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Any      ,		__snake_case       : 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({'train': csv_path}      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]      ,		__snake_case       : str							):
     _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(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader({'train': csv_path}      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : 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(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case      ,		splits=list(path.keys()							)							)
     assert all(dataset[split].split == split for split in path.keys()							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]							):
     with open(__snake_case      ,		encoding='utf-8'							) as csvfile:
          yield from csv.reader(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=1							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

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





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=2							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

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





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Tuple							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     with pytest.raises(__snake_case							):
          CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=0							)



