



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


UpperCamelCase_	       =   logging.get_logger(__name__)







class 							a_  (		_snake_case				):
						UpperCamelCase__      :						Optional[int]				       =["pixel_values"]

						def __init__(    self	:Tuple   ,						_lowercase	:bool = True   ,						_lowercase	:Dict[str, int] = None   ,						_lowercase	:PILImageResampling = PIL.Image.NEAREST   ,						_lowercase	:bool = False   ,						_lowercase	:Dict[str, int] = None   ,						_lowercase	:Union[int, float] = 1 / 255   ,						_lowercase	:bool = False   ,						_lowercase	:bool = True   ,						_lowercase	:bool = True   ,						_lowercase	:Optional[Union[float, List[float]]] = None   ,						_lowercase	:Optional[Union[float, List[float]]] = None   ,						_lowercase	:bool = True   ,						**_lowercase	:str   ,						)		->					None:
												super().__init__(**_lowercase)
												UpperCAmelCase_     							=     size if size is not None else {'''height''': 346, '''width''': 346}
												UpperCAmelCase_     							=     get_size_dict(_lowercase)
												UpperCAmelCase_     							=     crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
												UpperCAmelCase_     							=     get_size_dict(_lowercase   ,						param_name='''crop_size''')

												UpperCAmelCase_     							=     do_resize
												UpperCAmelCase_     							=     size
												UpperCAmelCase_     							=     resample
												UpperCAmelCase_     							=     do_center_crop
												UpperCAmelCase_     							=     crop_size
												UpperCAmelCase_     							=     do_rescale
												UpperCAmelCase_     							=     rescale_factor
												UpperCAmelCase_     							=     rescale_offset
												UpperCAmelCase_     							=     do_normalize
												UpperCAmelCase_     							=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												UpperCAmelCase_     							=     image_std if image_std is not None else IMAGENET_STANDARD_STD
												UpperCAmelCase_     							=     include_top

						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:np.ndarray   ,						_lowercase	:Dict[str, int]   ,						_lowercase	:PILImageResampling = PIL.Image.NEAREST   ,						_lowercase	:Optional[Union[str, ChannelDimension]] = None   ,						**_lowercase	:Optional[int]   ,						)		->					np.ndarray:
												UpperCAmelCase_     							=     get_size_dict(_lowercase)
												if "height" not in size or "width" not in size:
																		raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
												return resize(
												    _lowercase   ,						size=(size['''height'''], size['''width'''])   ,						resample=_lowercase   ,						data_format=_lowercase   ,						**_lowercase)

						def 	__a       (    self	:List[Any]   ,						_lowercase	:np.ndarray   ,						_lowercase	:Dict[str, int]   ,						_lowercase	:Optional[Union[str, ChannelDimension]] = None   ,						**_lowercase	:Dict   ,						)		->					np.ndarray:
												UpperCAmelCase_     							=     get_size_dict(_lowercase)
												if "height" not in size or "width" not in size:
																		raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
												return center_crop(_lowercase   ,						size=(size['''height'''], size['''width'''])   ,						data_format=_lowercase   ,						**_lowercase)

						def 	__a       (    self	:Union[str, Any]   ,						_lowercase	:np.ndarray   ,						_lowercase	:Union[int, float]   ,						_lowercase	:bool = True   ,						_lowercase	:Optional[Union[str, ChannelDimension]] = None   ,						**_lowercase	:Tuple   ,						)		->					int:
												if offset:
																		UpperCAmelCase_     							=     (image - 127.5) * scale
																		if data_format is not None:
																								UpperCAmelCase_     							=     to_channel_dimension_format(_lowercase   ,						_lowercase)
																		UpperCAmelCase_     							=     rescaled_image.astype(np.floataa)
												else:
																		UpperCAmelCase_     							=     rescale(_lowercase   ,						scale=_lowercase   ,						data_format=_lowercase   ,						**_lowercase)
												return rescaled_image

						def 	__a       (    self	:str   ,						_lowercase	:np.ndarray   ,						_lowercase	:Union[float, List[float]]   ,						_lowercase	:Union[float, List[float]]   ,						_lowercase	:Optional[Union[str, ChannelDimension]] = None   ,						**_lowercase	:Union[str, Any]   ,						)		->					np.ndarray:
												return normalize(_lowercase   ,						mean=_lowercase   ,						std=_lowercase   ,						data_format=_lowercase   ,						**_lowercase)




						def 	__a       (    self	:str   ,						_lowercase	:ImageInput   ,						_lowercase	:bool = None   ,						_lowercase	:Dict[str, int] = None   ,						_lowercase	:List[Any]=None   ,						_lowercase	:bool = None   ,						_lowercase	:Dict[str, int] = None   ,						_lowercase	:bool = None   ,						_lowercase	:float = None   ,						_lowercase	:bool = None   ,						_lowercase	:bool = None   ,						_lowercase	:Optional[Union[float, List[float]]] = None   ,						_lowercase	:Optional[Union[float, List[float]]] = None   ,						_lowercase	:bool = None   ,						_lowercase	:Optional[Union[str, TensorType]] = None   ,						_lowercase	:ChannelDimension = ChannelDimension.FIRST   ,						**_lowercase	:str   ,						)		->					PIL.Image.Image:
												UpperCAmelCase_     							=     do_resize if do_resize is not None else self.do_resize
												UpperCAmelCase_     							=     resample if resample is not None else self.resample
												UpperCAmelCase_     							=     do_center_crop if do_center_crop is not None else self.do_center_crop
												UpperCAmelCase_     							=     do_rescale if do_rescale is not None else self.do_rescale
												UpperCAmelCase_     							=     rescale_factor if rescale_factor is not None else self.rescale_factor
												UpperCAmelCase_     							=     rescale_offset if rescale_offset is not None else self.rescale_offset
												UpperCAmelCase_     							=     do_normalize if do_normalize is not None else self.do_normalize
												UpperCAmelCase_     							=     image_mean if image_mean is not None else self.image_mean
												UpperCAmelCase_     							=     image_std if image_std is not None else self.image_std
												UpperCAmelCase_     							=     include_top if include_top is not None else self.include_top

												UpperCAmelCase_     							=     size if size is not None else self.size
												UpperCAmelCase_     							=     get_size_dict(_lowercase)
												UpperCAmelCase_     							=     crop_size if crop_size is not None else self.crop_size
												UpperCAmelCase_     							=     get_size_dict(_lowercase   ,						param_name='''crop_size''')

												UpperCAmelCase_     							=     make_list_of_images(_lowercase)

												if not valid_images(_lowercase):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.''')

												if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.''')

												if do_center_crop and crop_size is None:
																		raise ValueError('''Crop size must be specified if do_center_crop is True.''')

												if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

												if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

												# All transformations expect numpy arrays.
												UpperCAmelCase_     							=     [to_numpy_array(_lowercase) for image in images]

												if do_resize:
																		UpperCAmelCase_     							=     [self.resize(image=_lowercase   ,						size=_lowercase   ,						resample=_lowercase) for image in images]

												if do_center_crop:
																		UpperCAmelCase_     							=     [self.center_crop(image=_lowercase   ,						size=_lowercase) for image in images]

												if do_rescale:
																		UpperCAmelCase_     							=     [self.rescale(image=_lowercase   ,						scale=_lowercase   ,						offset=_lowercase) for image in images]

												if do_normalize:
																		UpperCAmelCase_     							=     [self.normalize(image=_lowercase   ,						mean=_lowercase   ,						std=_lowercase) for image in images]

												if include_top:
																		UpperCAmelCase_     							=     [self.normalize(image=_lowercase   ,						mean=[0, 0, 0]   ,						std=_lowercase) for image in images]

												UpperCAmelCase_     							=     [to_channel_dimension_format(_lowercase   ,						_lowercase) for image in images]

												UpperCAmelCase_     							=     {'''pixel_values''': images}
												return BatchFeature(data=_lowercase   ,						tensor_type=_lowercase)








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_      (__A     :						Any					,   __A     :						Union[str, Any]     )			->	int:
   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 	snake_case_      (__A     :						int					,   __A     :						int					,   __A     :						List[str]     )			->	Tuple:
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase  :			List[str]					    =						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 	snake_case_      (__A     :						Dict					,   __A     :						List[Any]					,   __A     :						List[str]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			int					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			List[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader(__A					,   features=__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 	snake_case_      (__A     :						str					,   __A     :						Optional[int]					,   __A     :						int     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[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 	snake_case_      (__A     :						Union[str, Any]					,   __A     :						Any					,   __A     :						List[str]     )			->	Optional[int]:
   if issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Dict					    =						csv_path
   elif issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Tuple					    =						[csv_path]
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Dict					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader(__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





def 	snake_case_      (__A     :						int					,   __A     :						Optional[int]					,   __A     :						Any=("train",)     )			->	Optional[int]:
   assert isinstance(__A					,   __A     )
   for split in splits:
      __lowerCAmelCase  :			int					    =						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_      (__A     :						Optional[Any]					,   __A     :						List[str]					,   __A     :						Any     )			->	List[str]:
   __lowerCAmelCase  :			int					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			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():
      __lowerCAmelCase  :			Tuple					    =						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 	snake_case_      (__A     :						List[str]					,   __A     :						List[Any]					,   __A     :						Optional[int]     )			->	Optional[int]:
   __lowerCAmelCase  :			Optional[int]					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Union[str, Any]					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[int]					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			Optional[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Union[str, Any]					    =						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 	snake_case_      (__A     :						Optional[Any]					,   __A     :						Optional[Any]					,   __A     :						Union[str, Any]     )			->	Optional[int]:
   if split:
      __lowerCAmelCase  :			Any					    =						{split: csv_path}
   else:
      __lowerCAmelCase  :			Dict					    =						{"""train""": csv_path, """test""": csv_path}
   __lowerCAmelCase  :			List[str]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			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 	snake_case_      (__A     :						List[str]     )			->	Optional[Any]:
   with open(__A					,   encoding="""utf-8"""     ) as csvfile:
      yield from csv.reader(__A     )





def 	snake_case_      (__A     :						Optional[int]					,   __A     :						Optional[int]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			Optional[Any]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=1     ).write()

   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )

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





def 	snake_case_      (__A     :						int					,   __A     :						List[str]     )			->	str:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[str]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Dict					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=2     ).write()

   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )

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





def 	snake_case_      (__A     :						int					,   __A     :						Optional[Any]     )			->	List[str]:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[Any]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   with pytest.raises(__A     ):
      CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=0     )


