






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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> str:

     '''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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:

     '''simple docstring'''
     UpperCAmelCase__			:     Any										= tmp_path / """cache"""
     UpperCAmelCase__			:     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():
          UpperCAmelCase__			:     List[Any]										= 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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> List[str]:

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





@pytest.mark.parametrize("""split"""   ,       [None, NamedSplit("""train"""					), """train""", """test"""]					)
def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__			:     Optional[int]										= tmp_path / """cache"""
     UpperCAmelCase__			:     List[Any]										= {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     UpperCAmelCase__			:     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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> Union[str, Any]:

     '''simple docstring'''


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





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=("train",)					)   -> Tuple:

     '''simple docstring'''
     assert isinstance(_A   ,       _A					)
     for split in splits:
          UpperCAmelCase__			:     Union[str, Any]										= 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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> str:

     '''simple docstring'''
     UpperCAmelCase__			:     Tuple										= tmp_path / """cache"""
     UpperCAmelCase__			:     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():
          UpperCAmelCase__			:     str										= 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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> str:

     '''simple docstring'''
     UpperCAmelCase__			:     Any										= tmp_path / """cache"""
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     UpperCAmelCase__			:     Optional[int]										= {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     UpperCAmelCase__			:     Optional[Any]										= features.copy() if features else default_expected_features
     UpperCAmelCase__			:     Optional[Any]										= (
         Features({feature: Value(_A					) for feature, dtype in features.items()}					) if features is not None else None
     )
     UpperCAmelCase__			:     List[str]										= 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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> int:

     '''simple docstring'''


     if split:
          UpperCAmelCase__			:     List[str]										= {split: csv_path}
     else:
          UpperCAmelCase__			:     str										= {"""train""": csv_path, """test""": csv_path}
     UpperCAmelCase__			:     List[Any]										= tmp_path / """cache"""
     UpperCAmelCase__			:     Optional[Any]										= {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     UpperCAmelCase__			:     Optional[int]										= 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    _lowerCamelCase	(	__lowerCamelCase					)   -> Union[str, Any]:

     '''simple docstring'''


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





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> str:

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

     UpperCAmelCase__			:     int										= iter_csv_file(_A					)
     UpperCAmelCase__			:     Union[str, Any]										= iter_csv_file(_A					)

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





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> List[str]:

     '''simple docstring'''
     UpperCAmelCase__			:     str										= tmp_path / """cache"""
     UpperCAmelCase__			:     Union[str, Any]										= os.path.join(_A   ,       """tmp.csv"""					)
     UpperCAmelCase__			:     Dict										= CsvDatasetReader({"""train""": csv_path}   ,       cache_dir=_A					).read()
     CsvDatasetWriter(dataset["""train"""]   ,       _A   ,       num_proc=2					).write()

     UpperCAmelCase__			:     Optional[Any]										= iter_csv_file(_A					)
     UpperCAmelCase__			:     List[Any]										= iter_csv_file(_A					)

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







def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Dict:

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














import math
import os

import cva
import numpy as np

SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							255.0





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> float:

     '''simple docstring'''
     UpperCAmelCase__			:     List[Any]										= np.mean((original - contrast) ** 2					)
     if mse == 0:
          return 100

     return 20 * math.logaa(PIXEL_MAX / math.sqrt(__lowerCamelCase					)					)





def    _lowerCamelCase	(	)   -> None:

     '''simple docstring'''
     UpperCAmelCase__			:     Union[str, Any]										= os.path.dirname(os.path.realpath(__lowerCamelCase					)					)
     # Loading images (original image and compressed image)
     UpperCAmelCase__			:     List[Any]										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/original_image.png"""					)					)
     UpperCAmelCase__			:     Dict										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/compressed_image.png"""					)   ,       1					)

     UpperCAmelCase__			:     Tuple										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-base.png"""					)					)
     UpperCAmelCase__			:     int										= cva.imread(
         os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-comp-10.jpg"""					)   ,       1					)

     # Value expected: 29.73dB
     print("""-- First Test --"""					)
     print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)

     # # Value expected: 31.53dB (Wikipedia Example)
     print("""\n-- Second Test --"""					)
     print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)


if __name__ == "__main__":
      main()






