




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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:


			'''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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	tmp_path / 'cache'
			SCREAMING_SNAKE_CASE_			=	{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						SCREAMING_SNAKE_CASE_			=	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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	tmp_path / 'cache'
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			SCREAMING_SNAKE_CASE_			=	{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
			SCREAMING_SNAKE_CASE_			=	features.copy() if features else default_expected_features
			SCREAMING_SNAKE_CASE_			=	(
			    Features({feature: Value(_A   ) for feature, dtype in features.items()}   ) if features is not None else None
			)
			SCREAMING_SNAKE_CASE_			=	CsvDatasetReader(_A					,						features=_A					,						cache_dir=_A   ).read()
			_check_csv_dataset(_A					,						_A   )

@pytest.mark.parametrize('split'					,						[None, NamedSplit('train'   ), 'train', 'test']   )
def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	tmp_path / 'cache'
			SCREAMING_SNAKE_CASE_			=	{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
			SCREAMING_SNAKE_CASE_			=	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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[Any]:


			'''simple docstring'''







			if issubclass(_A					,						_A   ):
						SCREAMING_SNAKE_CASE_			=	csv_path
			elif issubclass(_A					,						_A   ):
						SCREAMING_SNAKE_CASE_			=	[csv_path]
			SCREAMING_SNAKE_CASE_			=	tmp_path / 'cache'
			SCREAMING_SNAKE_CASE_			=	{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
			SCREAMING_SNAKE_CASE_			=	CsvDatasetReader(_A					,						cache_dir=_A   ).read()
			_check_csv_dataset(_A					,						_A   )

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=("train",)   ) ->       Optional[int]:


			'''simple docstring'''







			assert isinstance(_A					,						_A   )
			for split in splits:
						SCREAMING_SNAKE_CASE_			=	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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	tmp_path / 'cache'
			SCREAMING_SNAKE_CASE_			=	{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						SCREAMING_SNAKE_CASE_			=	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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	tmp_path / 'cache'
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			SCREAMING_SNAKE_CASE_			=	{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
			SCREAMING_SNAKE_CASE_			=	features.copy() if features else default_expected_features
			SCREAMING_SNAKE_CASE_			=	(
			    Features({feature: Value(_A   ) for feature, dtype in features.items()}   ) if features is not None else None
			)
			SCREAMING_SNAKE_CASE_			=	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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:


			'''simple docstring'''







			if split:
						SCREAMING_SNAKE_CASE_			=	{split: csv_path}
			else:
						SCREAMING_SNAKE_CASE_			=	{'train': csv_path, 'test': csv_path}
			SCREAMING_SNAKE_CASE_			=	tmp_path / 'cache'
			SCREAMING_SNAKE_CASE_			=	{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
			SCREAMING_SNAKE_CASE_			=	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 lowercase						(     SCREAMING_SNAKE_CASE   ) ->       str:


			'''simple docstring'''







			with open(_A					,						encoding='utf-8'   ) as csvfile:
						yield from csv.reader(_A   )

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:


			'''simple docstring'''







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

			SCREAMING_SNAKE_CASE_			=	iter_csv_file(_A   )
			SCREAMING_SNAKE_CASE_			=	iter_csv_file(_A   )

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

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Union[str, Any]:


			'''simple docstring'''







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

			SCREAMING_SNAKE_CASE_			=	iter_csv_file(_A   )
			SCREAMING_SNAKE_CASE_			=	iter_csv_file(_A   )

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

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:


			'''simple docstring'''







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






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available



@dataclass
class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
				from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
				from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
