

'''simple docstring'''

import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil





@pytest.fixture
def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					tmp_path / "file.csv"
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					textwrap.dedent(
      "\\n        header1,header2\n        1,2\n        10,20\n        " )
  with open(snake_case      ,       "w" ) as f:
    f.write(snake_case )
  return str(snake_case )





@pytest.fixture
def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:int						      =					tmp_path / "malformed_file.csv"
  SCREAMING_SNAKE_CASE:List[str]						      =					textwrap.dedent(
      "\\n        header1,header2\n        1,2\n        10,20,\n        " )
  with open(snake_case      ,       "w" ) as f:
    f.write(snake_case )
  return str(snake_case )





@pytest.fixture
def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tmp_path / "csv_with_image.csv"
  SCREAMING_SNAKE_CASE:Any						      =					textwrap.dedent(
      F'''\
        image
        {image_file}
        ''' )
  with open(snake_case      ,       "w" ) as f:
    f.write(snake_case )
  return str(snake_case )





@pytest.fixture
def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					tmp_path / "csv_with_label.csv"
  SCREAMING_SNAKE_CASE:List[Any]						      =					textwrap.dedent(
      "\\n        label\n        good\n        bad\n        good\n        " )
  with open(snake_case      ,       "w" ) as f:
    f.write(snake_case )
  return str(snake_case )





@pytest.fixture
def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					tmp_path / "csv_with_int_list.csv"
  SCREAMING_SNAKE_CASE:Any						      =					textwrap.dedent(
      "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        " )
  with open(snake_case      ,       "w" ) as f:
    f.write(snake_case )
  return str(snake_case )





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					Csv()
  SCREAMING_SNAKE_CASE:Dict						      =					csv._generate_tables([[csv_file, malformed_csv_file]] )
  with pytest.raises(snake_case      ,       match="Error tokenizing data" ):
    for _ in generator:
      pass
  assert any(
      record.levelname == "ERROR"
      and "Failed to read file" in record.message
      and os.path.basename(snake_case ) in record.message
      for record in caplog.records )





@require_pil
def    A_  ( snake_case ):
  with open(snake_case      ,       encoding="utf-8" ) as f:
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					f.read().splitlines()[1]
  SCREAMING_SNAKE_CASE:Tuple						      =					Csv(encoding="utf-8"      ,       features=Features({"image": Image()} ) )
  SCREAMING_SNAKE_CASE:Dict						      =					csv._generate_tables([[csv_file_with_image]] )
  SCREAMING_SNAKE_CASE:List[Any]						      =					pa.concat_tables([table for _, table in generator] )
  assert pa_table.schema.field("image" ).type == Image()()
  SCREAMING_SNAKE_CASE:Dict						      =					pa_table.to_pydict()["image"]
  assert generated_content == [{"path": image_file, "bytes": None}]





def    A_  ( snake_case ):
  with open(snake_case      ,       encoding="utf-8" ) as f:
    SCREAMING_SNAKE_CASE:Tuple						      =					f.read().splitlines()[1:]
  SCREAMING_SNAKE_CASE:List[str]						      =					Csv(encoding="utf-8"      ,       features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
  SCREAMING_SNAKE_CASE:str						      =					csv._generate_tables([[csv_file_with_label]] )
  SCREAMING_SNAKE_CASE:str						      =					pa.concat_tables([table for _, table in generator] )
  assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
  SCREAMING_SNAKE_CASE:int						      =					pa_table.to_pydict()["label"]
  assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(snake_case ) for label in labels]





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					Csv(encoding="utf-8"      ,       sep=","      ,       converters={"int_list": lambda snake_case : [int(snake_case ) for i in x.split()]} )
  SCREAMING_SNAKE_CASE:Any						      =					csv._generate_tables([[csv_file_with_int_list]] )
  SCREAMING_SNAKE_CASE:List[str]						      =					pa.concat_tables([table for _, table in generator] )
  assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					pa_table.to_pydict()["int_list"]
  assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]









'''simple docstring'''

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,
    get_resize_output_image_size,
    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


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Union[str, Any]       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BILINEAR     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Union[int, float] = 1 / 255     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,**SCREAMING_SNAKE_CASE__			:      List[str]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					size if size is not None else {"shortest_edge": 384}
     SCREAMING_SNAKE_CASE:List[str]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					do_resize
     SCREAMING_SNAKE_CASE:List[Any]						      =					size
     # Default value set here for backwards compatibility where the value in config is None
     SCREAMING_SNAKE_CASE:Dict						      =					crop_pct if crop_pct is not None else 224 / 256
     SCREAMING_SNAKE_CASE:Any						      =					resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[int]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					do_normalize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE:Tuple						      =					image_std if image_std is not None else IMAGENET_STANDARD_STD
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      float     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Any						      =					size["shortest_edge"]

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       SCREAMING_SNAKE_CASE:Any						      =					int(shortest_edge / crop_pct							)
       SCREAMING_SNAKE_CASE:Any						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      ImageInput     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,SCREAMING_SNAKE_CASE__			:      ChannelDimension = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:List[str]						      =					crop_pct if crop_pct is not None else self.crop_pct
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:List[str]						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:str						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:str						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Any						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Tuple						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					make_list_of_images(SCREAMING_SNAKE_CASE__							)

     if not valid_images(SCREAMING_SNAKE_CASE__							):
       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_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError("crop_pct must be specified if size < 384."							)

     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.
     SCREAMING_SNAKE_CASE:int						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					[self.resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,crop_pct=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.rescale(image=SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					[self.normalize(image=SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Any						      =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:int						      =					{"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)






