





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__A									=				logging.get_logger(__name__)


if is_vision_available():
     import PIL



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			["pixel_values"]

 def __init__(self				:   Optional[Any]       , UpperCAmelCase_				:   bool = True       , UpperCAmelCase_				:   Dict[str, int] = None       , UpperCAmelCase_				:   PILImageResampling = PILImageResampling.BICUBIC       , UpperCAmelCase_				:   bool = True       , UpperCAmelCase_				:   Dict[str, int] = None       , UpperCAmelCase_				:   bool = True       , UpperCAmelCase_				:   Union[int, float] = 1 / 255       , UpperCAmelCase_				:   bool = True       , UpperCAmelCase_				:   Optional[Union[float, List[float]]] = None       , UpperCAmelCase_				:   Optional[Union[float, List[float]]] = None       , UpperCAmelCase_				:   bool = True       , **UpperCAmelCase_				:   Tuple       , )       ->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =size if size is not None else {"shortest_edge": 224}
   lowerCamelCase__:      Optional[Any]			  =get_size_dict(UpperCAmelCase_       , default_to_square=UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase__:      Tuple			  =get_size_dict(UpperCAmelCase_       , default_to_square=UpperCAmelCase_       , param_name="crop_size")

   lowerCamelCase__:      Union[str, Any]			  =do_resize
   lowerCamelCase__:      Union[str, Any]			  =size
   lowerCamelCase__:      Any			  =resample
   lowerCamelCase__:      List[str]			  =do_center_crop
   lowerCamelCase__:      List[Any]			  =crop_size
   lowerCamelCase__:      List[str]			  =do_rescale
   lowerCamelCase__:      List[str]			  =rescale_factor
   lowerCamelCase__:      str			  =do_normalize
   lowerCamelCase__:      Tuple			  =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   lowerCamelCase__:      List[Any]			  =image_std if image_std is not None else OPENAI_CLIP_STD
   lowerCamelCase__:      int			  =do_convert_rgb

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   np.ndarray       , UpperCAmelCase_				:   Dict[str, int]       , UpperCAmelCase_				:   PILImageResampling = PILImageResampling.BICUBIC       , UpperCAmelCase_				:   Optional[Union[str, ChannelDimension]] = None       , **UpperCAmelCase_				:   List[Any]       , )       ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =get_size_dict(UpperCAmelCase_       , default_to_square=UpperCAmelCase_)
   if "shortest_edge" not in size:
     raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
   lowerCamelCase__:      Optional[int]			  =get_resize_output_image_size(UpperCAmelCase_       , size=size["shortest_edge"]       , default_to_square=UpperCAmelCase_)
   return resize(UpperCAmelCase_       , size=UpperCAmelCase_       , resample=UpperCAmelCase_       , data_format=UpperCAmelCase_       , **UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   np.ndarray       , UpperCAmelCase_				:   Dict[str, int]       , UpperCAmelCase_				:   Optional[Union[str, ChannelDimension]] = None       , **UpperCAmelCase_				:   Optional[int]       , )       ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:      int			  =get_size_dict(UpperCAmelCase_)
   if "height" not in size or "width" not in size:
     raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""")
   return center_crop(UpperCAmelCase_       , size=(size["height"], size["width"])       , data_format=UpperCAmelCase_       , **UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   np.ndarray       , UpperCAmelCase_				:   Union[int, float]       , UpperCAmelCase_				:   Optional[Union[str, ChannelDimension]] = None       , **UpperCAmelCase_				:   List[str]       , )       ->int:



   '''simple docstring'''



   return rescale(UpperCAmelCase_       , scale=UpperCAmelCase_       , data_format=UpperCAmelCase_       , **UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   np.ndarray       , UpperCAmelCase_				:   Union[float, List[float]]       , UpperCAmelCase_				:   Union[float, List[float]]       , UpperCAmelCase_				:   Optional[Union[str, ChannelDimension]] = None       , **UpperCAmelCase_				:   Any       , )       ->np.ndarray:



   '''simple docstring'''



   return normalize(UpperCAmelCase_       , mean=UpperCAmelCase_       , std=UpperCAmelCase_       , data_format=UpperCAmelCase_       , **UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   ImageInput       , UpperCAmelCase_				:   bool = None       , UpperCAmelCase_				:   Dict[str, int] = None       , UpperCAmelCase_				:   PILImageResampling = None       , UpperCAmelCase_				:   bool = None       , UpperCAmelCase_				:   int = None       , UpperCAmelCase_				:   bool = None       , UpperCAmelCase_				:   float = None       , UpperCAmelCase_				:   bool = None       , UpperCAmelCase_				:   Optional[Union[float, List[float]]] = None       , UpperCAmelCase_				:   Optional[Union[float, List[float]]] = None       , UpperCAmelCase_				:   bool = None       , UpperCAmelCase_				:   Optional[Union[str, TensorType]] = None       , UpperCAmelCase_				:   Optional[ChannelDimension] = ChannelDimension.FIRST       , **UpperCAmelCase_				:   List[str]       , )       ->PIL.Image.Image:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =do_resize if do_resize is not None else self.do_resize
   lowerCamelCase__:      List[Any]			  =size if size is not None else self.size
   lowerCamelCase__:      List[Any]			  =get_size_dict(UpperCAmelCase_       , param_name="size"       , default_to_square=UpperCAmelCase_)
   lowerCamelCase__:      Any			  =resample if resample is not None else self.resample
   lowerCamelCase__:      Any			  =do_center_crop if do_center_crop is not None else self.do_center_crop
   lowerCamelCase__:      Any			  =crop_size if crop_size is not None else self.crop_size
   lowerCamelCase__:      Union[str, Any]			  =get_size_dict(UpperCAmelCase_       , param_name="crop_size"       , default_to_square=UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase__:      List[str]			  =rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase__:      int			  =do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase__:      Optional[Any]			  =image_mean if image_mean is not None else self.image_mean
   lowerCamelCase__:      Optional[int]			  =image_std if image_std is not None else self.image_std
   lowerCamelCase__:      Dict			  =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   lowerCamelCase__:      Dict			  =make_list_of_images(UpperCAmelCase_)

   if not valid_images(UpperCAmelCase_):
     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:
     raise ValueError("Size 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.")

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
     lowerCamelCase__:      List[Any]			  =[convert_to_rgb(UpperCAmelCase_) for image in images]

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

   if do_resize:
     lowerCamelCase__:      Union[str, Any]			  =[self.resize(image=UpperCAmelCase_       , size=UpperCAmelCase_       , resample=UpperCAmelCase_) for image in images]

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

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

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

   lowerCamelCase__:      List[str]			  =[to_channel_dimension_format(UpperCAmelCase_       , UpperCAmelCase_) for image in images]

   lowerCamelCase__:      str			  ={"pixel_values": images}
   return BatchFeature(data=UpperCAmelCase_       , tensor_type=UpperCAmelCase_)








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			field(default="image-classification"    ,							metadata={"include_in_asdict_even_if_is_default": True}    )
 lowercase_		=			Features({"image": Image()}    )
 lowercase_		=			Features({"labels": ClassLabel}    )
 lowercase_		=			"image"
 lowercase_		=			"labels"

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   if self.label_column not in features:
     raise ValueError(F"""Column {self.label_column} is not present in features.""")
   if not isinstance(features[self.label_column]       , UpperCAmelCase_):
     raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""")
   lowerCamelCase__:      List[Any]			  =copy.deepcopy(self)
   lowerCamelCase__:      Optional[int]			  =self.label_schema.copy()
   lowerCamelCase__:      int			  =features[self.label_column]
   lowerCamelCase__:      int			  =label_schema
   return task_template







 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict[str, str]:



   '''simple docstring'''



   return {
       self.image_column: "image",
       self.label_column: "labels",
   }

