






"""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,
    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		:       Optional[Any]  				=		logging.get_logger(__name__)


if is_vision_available():
						import PIL





class 					_UpperCAmelCase       ( __A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   ["pixel_values"]




					def __init__(				self     :						int    ,						A     :						bool = True    ,						A     :						Dict[str, int] = None    ,						A     :						PILImageResampling = PILImageResampling.BICUBIC    ,						A     :						bool = True    ,						A     :						Dict[str, int] = None    ,						A     :						bool = True    ,						A     :						Union[int, float] = 1 / 2_55    ,						A     :						bool = True    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						bool = True    ,						**A     :						Union[str, Any]    ,						)  ->  None:
							super().__init__(**lowercase_   )
							lowercase_ : Tuple							       =   size if size is not None else {"shortest_edge": 2_24}
							lowercase_ : Tuple							       =   get_size_dict(lowercase_    ,						default_to_square=lowercase_   )
							lowercase_ : List[str]							       =   crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
							lowercase_ : Tuple							       =   get_size_dict(lowercase_    ,						default_to_square=lowercase_    ,						param_name='''crop_size'''   )

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




					def   A  (				self     :						Optional[Any]    ,						A     :						np.ndarray    ,						A     :						Dict[str, int]    ,						A     :						PILImageResampling = PILImageResampling.BICUBIC    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Union[str, Any]    ,						)  ->  np.ndarray:
							lowercase_ : str							       =   get_size_dict(lowercase_    ,						default_to_square=lowercase_   )
							if "shortest_edge" not in size:
									raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''   )
							lowercase_ : Dict							       =   get_resize_output_image_size(lowercase_    ,						size=size['''shortest_edge''']    ,						default_to_square=lowercase_   )
							return resize(lowercase_    ,						size=lowercase_    ,						resample=lowercase_    ,						data_format=lowercase_    ,						**lowercase_   )




					def   A  (				self     :						int    ,						A     :						np.ndarray    ,						A     :						Dict[str, int]    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						int    ,						)  ->  np.ndarray:
							lowercase_ : Optional[Any]							       =   get_size_dict(lowercase_   )
							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(lowercase_    ,						size=(size['''height'''], size['''width'''])    ,						data_format=lowercase_    ,						**lowercase_   )




					def   A  (				self     :						Optional[Any]    ,						A     :						np.ndarray    ,						A     :						Union[int, float]    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Optional[Any]    ,						)  ->  Any:
							return rescale(lowercase_    ,						scale=lowercase_    ,						data_format=lowercase_    ,						**lowercase_   )




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






					def   A  (				self     :						Optional[Any]    ,						A     :						ImageInput    ,						A     :						bool = None    ,						A     :						Dict[str, int] = None    ,						A     :						PILImageResampling = None    ,						A     :						bool = None    ,						A     :						int = None    ,						A     :						bool = None    ,						A     :						float = None    ,						A     :						bool = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						bool = None    ,						A     :						Optional[Union[str, TensorType]] = None    ,						A     :						Optional[ChannelDimension] = ChannelDimension.FIRST    ,						**A     :						Union[str, Any]    ,						)  ->  PIL.Image.Image:
							lowercase_ : int							       =   do_resize if do_resize is not None else self.do_resize
							lowercase_ : Dict							       =   size if size is not None else self.size
							lowercase_ : List[Any]							       =   get_size_dict(lowercase_    ,						param_name='''size'''    ,						default_to_square=lowercase_   )
							lowercase_ : Dict							       =   resample if resample is not None else self.resample
							lowercase_ : int							       =   do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase_ : Dict							       =   crop_size if crop_size is not None else self.crop_size
							lowercase_ : List[str]							       =   get_size_dict(lowercase_    ,						param_name='''crop_size'''    ,						default_to_square=lowercase_   )
							lowercase_ : int							       =   do_rescale if do_rescale is not None else self.do_rescale
							lowercase_ : Dict							       =   rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase_ : Union[str, Any]							       =   do_normalize if do_normalize is not None else self.do_normalize
							lowercase_ : int							       =   image_mean if image_mean is not None else self.image_mean
							lowercase_ : List[str]							       =   image_std if image_std is not None else self.image_std
							lowercase_ : Union[str, Any]							       =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							lowercase_ : Union[str, Any]							       =   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:
									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:
									lowercase_ : Dict							       =   [convert_to_rgb(lowercase_   ) for image in images]

							# All transformations expect numpy arrays.
							lowercase_ : Optional[Any]							       =   [to_numpy_array(lowercase_   ) for image in images]

							if do_resize:
									lowercase_ : List[Any]							       =   [self.resize(image=lowercase_    ,						size=lowercase_    ,						resample=lowercase_   ) for image in images]

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

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

							if do_normalize:
									lowercase_ : Optional[int]							       =   [self.normalize(image=lowercase_    ,						mean=lowercase_    ,						std=lowercase_   ) for image in images]

							lowercase_ : Optional[Any]							       =   [to_channel_dimension_format(lowercase_    ,						lowercase_   ) for image in images]

							lowercase_ : List[str]							       =   {"pixel_values": images}
							return BatchFeature(data=lowercase_    ,						tensor_type=lowercase_   )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCamelCase  							=						{}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_swa import GPTSwaTokenizer

else:
      import sys

      UpperCamelCase  							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



