





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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)


if is_vision_available():
      import PIL

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Any			=			["pixel_values"]






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




           '''simple docstring'''


           super().__init__(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						size if size is not None else {"""shortest_edge""": 224}
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__						,param_name="""crop_size"""       )

           SCREAMING_SNAKE_CASE		=						do_resize
           SCREAMING_SNAKE_CASE		=						size
           SCREAMING_SNAKE_CASE		=						resample
           SCREAMING_SNAKE_CASE		=						do_center_crop
           SCREAMING_SNAKE_CASE		=						crop_size
           SCREAMING_SNAKE_CASE		=						do_rescale
           SCREAMING_SNAKE_CASE		=						rescale_factor
           SCREAMING_SNAKE_CASE		=						do_normalize
           SCREAMING_SNAKE_CASE		=						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           SCREAMING_SNAKE_CASE		=						image_std if image_std is not None else OPENAI_CLIP_STD
           SCREAMING_SNAKE_CASE		=						do_convert_rgb






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    np.ndarray						,lowerCamelCase__						:    Dict[str, int]						,lowerCamelCase__						:    PILImageResampling = PILImageResampling.BICUBIC						,lowerCamelCase__						:    Optional[Union[str, ChannelDimension]] = None						,**lowerCamelCase__						:    Tuple						,)     ->		np.ndarray:




           '''simple docstring'''


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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__       )
           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(lowerCamelCase__						,size=(size["""height"""], size["""width"""])						,data_format=lowerCamelCase__						,**lowerCamelCase__       )






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




           '''simple docstring'''


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






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




           '''simple docstring'''


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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						do_resize if do_resize is not None else self.do_resize
           SCREAMING_SNAKE_CASE		=						size if size is not None else self.size
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,param_name="""size"""						,default_to_square=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						resample if resample is not None else self.resample
           SCREAMING_SNAKE_CASE		=						do_center_crop if do_center_crop is not None else self.do_center_crop
           SCREAMING_SNAKE_CASE		=						crop_size if crop_size is not None else self.crop_size
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,param_name="""crop_size"""						,default_to_square=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						do_rescale if do_rescale is not None else self.do_rescale
           SCREAMING_SNAKE_CASE		=						rescale_factor if rescale_factor is not None else self.rescale_factor
           SCREAMING_SNAKE_CASE		=						do_normalize if do_normalize is not None else self.do_normalize
           SCREAMING_SNAKE_CASE		=						image_mean if image_mean is not None else self.image_mean
           SCREAMING_SNAKE_CASE		=						image_std if image_std is not None else self.image_std
           SCREAMING_SNAKE_CASE		=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           SCREAMING_SNAKE_CASE		=						make_list_of_images(lowerCamelCase__       )

           if not valid_images(lowerCamelCase__       ):
                 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:
                 SCREAMING_SNAKE_CASE		=						[convert_to_rgb(lowerCamelCase__       ) for image in images]

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

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

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

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

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

           SCREAMING_SNAKE_CASE		=						[to_channel_dimension_format(lowerCamelCase__						,lowerCamelCase__       ) for image in images]

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









from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     def __init__(							self						:    Union[str, Any]						,lowerCamelCase__						:    Callable						,lowerCamelCase__						:    Optional[Features] = None						,lowerCamelCase__						:    str = None						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    Optional[dict] = None						,lowerCamelCase__						:    Optional[int] = None						,**lowerCamelCase__						:    Optional[Any]						,)     ->		List[str]:




           '''simple docstring'''


           super().__init__(
               features=lowerCamelCase__						,cache_dir=lowerCamelCase__						,keep_in_memory=lowerCamelCase__						,streaming=lowerCamelCase__						,num_proc=lowerCamelCase__						,**lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						Generator(
               cache_dir=lowerCamelCase__						,features=lowerCamelCase__						,generator=lowerCamelCase__						,gen_kwargs=lowerCamelCase__						,**lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           if self.streaming:
                 SCREAMING_SNAKE_CASE		=						self.builder.as_streaming_dataset(split="""train"""       )
           # Build regular (map-style) dataset
           else:
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None

                 self.builder.download_and_prepare(
                     download_config=lowerCamelCase__						,download_mode=lowerCamelCase__						,verification_mode=lowerCamelCase__						,base_path=lowerCamelCase__						,num_proc=self.num_proc						,)
                 SCREAMING_SNAKE_CASE		=						self.builder.as_dataset(
                     split="""train"""						,verification_mode=lowerCamelCase__						,in_memory=self.keep_in_memory       )
           return dataset


