
"""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 					a__ ( a :					Optional[Any]							,  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
@pytest.mark.parametrize("keep_in_memory"							,  [False, True]		)
def 					a__ ( a :					int							,  a :					Union[str, Any]							,  a :					Dict		):

  """simple docstring"""







  _snake_case  :    List[Any]            = tmp_path / "cache"
  _snake_case  :    Tuple            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case  :    Optional[int]            = CsvDatasetReader(a							,  cache_dir=a							,  keep_in_memory=a		).read()
  _check_csv_dataset(a							,  a		)
@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__ ( a :					List[Any]							,  a :					str							,  a :					Any		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  _snake_case  :    str            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    str            = features.copy() if features else default_expected_features
  _snake_case  :    List[Any]            = (
      Features({feature: Value(a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  _snake_case  :    List[str]            = CsvDatasetReader(a							,  features=a							,  cache_dir=a		).read()
  _check_csv_dataset(a							,  a		)
@pytest.mark.parametrize("split"							,  [None, NamedSplit("train"		), "train", "test"]		)
def 					a__ ( a :					int							,  a :					List[str]							,  a :					Any		):

  """simple docstring"""







  _snake_case  :    Dict            = tmp_path / "cache"
  _snake_case  :    Optional[int]            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    Any            = CsvDatasetReader(a							,  cache_dir=a							,  split=a		).read()
  _check_csv_dataset(a							,  a		)
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"							,  [str, list]		)
def 					a__ ( a :					int							,  a :					Union[str, Any]							,  a :					Optional[Any]		):

  """simple docstring"""







  if issubclass(a							,  a		):
    _snake_case  :    Any            = csv_path
  elif issubclass(a							,  a		):
    _snake_case  :    Optional[int]            = [csv_path]
  _snake_case  :    Optional[Any]            = tmp_path / "cache"
  _snake_case  :    Tuple            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    int            = CsvDatasetReader(a							,  cache_dir=a		).read()
  _check_csv_dataset(a							,  a		)
def 					a__ ( a :					List[str]							,  a :					int							,  a :					int=("train",)		):

  """simple docstring"""







  assert isinstance(a							,  a		)
  for split in splits:
    _snake_case  :    Tuple            = 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 					a__ ( a :					Optional[int]							,  a :					str							,  a :					str		):

  """simple docstring"""







  _snake_case  :    int            = tmp_path / "cache"
  _snake_case  :    Optional[Any]            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case  :    Any            = CsvDatasetReader({"train": csv_path}							,  cache_dir=a							,  keep_in_memory=a		).read()
  _check_csv_datasetdict(a							,  a		)
@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__ ( a :					Any							,  a :					int							,  a :					Optional[int]		):

  """simple docstring"""







  _snake_case  :    Dict            = tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  _snake_case  :    Any            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    str            = features.copy() if features else default_expected_features
  _snake_case  :    str            = (
      Features({feature: Value(a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  _snake_case  :    Dict            = CsvDatasetReader({"train": csv_path}							,  features=a							,  cache_dir=a		).read()
  _check_csv_datasetdict(a							,  a		)
@pytest.mark.parametrize("split"							,  [None, NamedSplit("train"		), "train", "test"]		)
def 					a__ ( a :					Optional[int]							,  a :					Dict							,  a :					List[str]		):

  """simple docstring"""







  if split:
    _snake_case  :    str            = {split: csv_path}
  else:
    _snake_case  :    List[Any]            = {"train": csv_path, "test": csv_path}
  _snake_case  :    Dict            = tmp_path / "cache"
  _snake_case  :    Tuple            = {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  _snake_case  :    str            = CsvDatasetReader(a							,  cache_dir=a		).read()
  _check_csv_datasetdict(a							,  a							,  splits=list(path.keys()		)		)
  assert all(dataset[split].split == split for split in path.keys()		)
def 					a__ ( a :					int		):

  """simple docstring"""







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

  """simple docstring"""







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

  _snake_case  :    List[Any]            = iter_csv_file(a		)
  _snake_case  :    Any            = iter_csv_file(a		)

  for rowa, rowa in zip(a							,  a		):
    assert rowa == rowa
def 					a__ ( a :					Optional[int]							,  a :					Optional[int]		):

  """simple docstring"""







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

  _snake_case  :    int            = iter_csv_file(a		)
  _snake_case  :    Optional[Any]            = iter_csv_file(a		)

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



def 					a__ ( a :					Tuple							,  a :					Tuple		):

  """simple docstring"""







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



"""simple docstring"""






from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
				import torch

__UpperCAmelCase 					=    logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
				from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






				def 		_snake_case			(      cls						:    Tuple						)    ->							List[Any]:


							'''simple docstring'''





							return cls






def 		_snake_case			(      lowercase__						:    Union[Tuple, List] ,	lowercase__						:    Optional[Union[List["torch.Generator"], "torch.Generator"]] = None ,	lowercase__						:    Optional["torch.device"] = None ,	lowercase__						:    Optional["torch.dtype"] = None ,	lowercase__						:    Optional["torch.layout"] = None ,	)    ->							Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    device
			lowerCAmelCase_       :Any     	=    shape[0]

			lowerCAmelCase_       :Dict     	=    layout or torch.strided
			lowerCAmelCase_       :List[str]     	=    device or torch.device("""cpu"""						)

			if generator is not None:
						lowerCAmelCase_       :Optional[Any]     	=    generator.device.type if not isinstance(lowercase__ ,	lowercase__						) else generator[0].device.type
						if gen_device_type != device.type and gen_device_type == "cpu":
									lowerCAmelCase_       :Optional[int]     	=    """cpu"""
									if device != "mps":
												logger.info(
												    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
												    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
												    f""" slighly speed up this function by passing a generator that was created on the {device} device."""						)
						elif gen_device_type != device.type and gen_device_type == "cuda":
									raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""						)

			if isinstance(lowercase__ ,	lowercase__						):
						lowerCAmelCase_       :List[str]     	=    (1,) + shape[1:]
						lowerCAmelCase_       :Optional[Any]     	=    [
						    torch.randn(lowercase__ ,	generator=generator[i] ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						)
						    for i in range(lowercase__						)
						]
						lowerCAmelCase_       :str     	=    torch.cat(lowercase__ ,	dim=0						).to(lowercase__						)
			else:
						lowerCAmelCase_       :Dict     	=    torch.randn(lowercase__ ,	generator=lowercase__ ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						).to(lowercase__						)

			return latents






def 		_snake_case			(      lowercase__						:    Union[str, Any]						)    ->							int:


			'''simple docstring'''





			if is_torch_version("""<""" ,	"""2.0.0"""						) or not hasattr(lowercase__ ,	"""_dynamo"""						):
						return False
			return isinstance(lowercase__ ,	torch._dynamo.eval_frame.OptimizedModule						)






