





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


lowercase     :			str     						=	logging.get_logger(__name__)


if is_vision_available():
       import PIL

class 			__A(    lowerCAmelCase_			):
     __A							=							["pixel_values"]

     def __init__(   self,							A = True,							A = None,							A = PILImageResampling.BICUBIC,							A = True,							A = None,							A = True,							A = 1 / 255,							A = True,							A = None,							A = None,							A = True,							**A,							):





            """simple docstring"""

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

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

     def 						_UpperCamelCase  (   self,							A,							A,							A = PILImageResampling.BICUBIC,							A = None,							**A,							):





            """simple docstring"""

            _UpperCamelCase														=    get_size_dict(__UpperCamelCase,							default_to_square=__UpperCamelCase   )
            _UpperCamelCase														=    get_resize_output_image_size(
                __UpperCamelCase,							size=(size['''height'''], size['''width''']),							default_to_square=__UpperCamelCase   )
            return resize(__UpperCamelCase,							size=__UpperCamelCase,							resample=__UpperCamelCase,							data_format=__UpperCamelCase,							**__UpperCamelCase   )

     def 						_UpperCamelCase  (   self,							A,							A,							A = None,							**A,							):





            """simple docstring"""

            _UpperCamelCase														=    get_size_dict(__UpperCamelCase   )
            return center_crop(__UpperCamelCase,							size=(size['''height'''], size['''width''']),							data_format=__UpperCamelCase,							**__UpperCamelCase   )

     def 						_UpperCamelCase  (   self,							A,							A,							A = None,							**A,							):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A = None,							**A,							):





            """simple docstring"""

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



     def 						_UpperCamelCase  (   self,							A,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = ChannelDimension.FIRST,							**A,							):





            """simple docstring"""

            _UpperCamelCase														=    do_resize if do_resize is not None else self.do_resize
            _UpperCamelCase														=    size if size is not None else self.size
            _UpperCamelCase														=    get_size_dict(__UpperCamelCase,							default_to_square=__UpperCamelCase   )
            _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														=    crop_size if crop_size is not None else self.crop_size
            _UpperCamelCase														=    get_size_dict(__UpperCamelCase   )
            _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														=    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														=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            _UpperCamelCase														=    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:
                   _UpperCamelCase														=    [convert_to_rgb(__UpperCamelCase   ) for image in images]

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

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

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

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

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

            _UpperCamelCase														=    [to_channel_dimension_format(__UpperCamelCase,							__UpperCamelCase   ) for image in images]

            _UpperCamelCase														=    {"pixel_values": images}
            return BatchFeature(data=__UpperCamelCase,							tensor_type=__UpperCamelCase   )








"""simple docstring"""




import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


A__   :						Optional[Any] 							=     set()
A__   :						List[str] 							=     get_logger(__name__)



def 				a__			(					lowerCAmelCase						:							Optional[str] = None		):






     '''simple docstring'''

     def decorator(lowerCAmelCase						:							Callable		):
          global _emitted_deprecation_warnings

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			Any							=    deprecated_class_or_function.__init__
               UpperCAmelCase__				:			Optional[int]							=    deprecated_class_or_function.__name__
          else:
               UpperCAmelCase__				:			str							=    deprecated_class_or_function
               UpperCAmelCase__				:			List[Any]							=    deprecated_function.__name__
               # Support deprecating __init__ class method: class name instead
               UpperCAmelCase__				:			List[str]							=    name if name != "__init__" else deprecated_function.__qualname__.split("."		)[-2]

          UpperCAmelCase__				:			Optional[Any]							=    (
              F"{name} is deprecated and will be removed in the next major version of datasets." + F" {help_message}"
              if help_message
              else ""
          )

          @wraps(lowerCAmelCase		)
          def wrapper(*lowerCAmelCase						:							int							,     **lowerCAmelCase						:							str		):
               UpperCAmelCase__				:			List[str]							=    hash(lowerCAmelCase		)
               if func_hash not in _emitted_deprecation_warnings:
                    warnings.warn(lowerCAmelCase							,     category=lowerCAmelCase							,     stacklevel=2		)
                    _emitted_deprecation_warnings.add(lowerCAmelCase		)
               return deprecated_function(*lowerCAmelCase							,     **lowerCAmelCase		)

          UpperCAmelCase__				:			List[str]							=    "deprecated"

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			List[str]							=    wrapper
               return deprecated_class_or_function
          else:
               return wrapper

     return decorator







class 	_lowercase    (							enum.EnumMeta					):


     '''simple docstring'''





     def __getattribute__(   cls			,      __UpperCamelCase	)->       Optional[Any]:
          UpperCAmelCase__				:			Optional[int]							=    super().__getattribute__(__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj



     def __getitem__(   cls			,      __UpperCamelCase	)->       Optional[int]:
          UpperCAmelCase__				:			Union[str, Any]							=    super().__getitem__(__UpperCamelCase	)
          if member._on_access:
               member._on_access()
          return member



     def __call__(   cls			,      __UpperCamelCase			,      __UpperCamelCase=None			,      *, __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=1	)->       Dict:
          UpperCAmelCase__				:			Optional[int]							=    super().__call__(__UpperCamelCase			,      __UpperCamelCase			,      module=__UpperCamelCase			,      qualname=__UpperCamelCase			,      type=__UpperCamelCase			,      start=__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj







class 	_lowercase    (							enum.Enum						, metaclass=lowerCAmelCase_					):


     '''simple docstring'''





     def __new__(   cls			,      __UpperCamelCase	)->       Union[str, Any]:
          UpperCAmelCase__				:			int							=    object.__new__(cls	)
          UpperCAmelCase__				:			int							=    value
          UpperCAmelCase__				:			Optional[int]							=    member.deprecate
          return member



     @property
     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          return ""



     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			Tuple							=    F" {self.help_message}" if self.help_message else ""
          warnings.warn(
              F"'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."
              + help_message			,      __UpperCamelCase			,      stacklevel=3			,      )



