"""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 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    List[str]	      =	["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__       ,  ):
       super().__init__(**a__						)
       _lowerCAmelCase		: List[str]						    =  size if size is not None else {"""shortest_edge""": 224}
       _lowerCAmelCase		: Union[str, Any]						    =  get_size_dict(a__       ,  default_to_square=a__						)
       _lowerCAmelCase		: int						    =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
       _lowerCAmelCase		: Optional[Any]						    =  get_size_dict(a__       ,  default_to_square=a__       ,  param_name="""crop_size"""						)

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






     def 					__A						(	self       ,  a__       ,  a__       ,  a__ = PILImageResampling.BICUBIC       ,  a__ = None       ,  **a__       ,  ):
       _lowerCAmelCase		: Optional[int]						    =  get_size_dict(a__       ,  default_to_square=a__						)
       if "shortest_edge" not in size:
         raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"						)
       _lowerCAmelCase		: str						    =  get_resize_output_image_size(a__       ,  size=size["""shortest_edge"""]       ,  default_to_square=a__						)
       return resize(a__       ,  size=a__       ,  resample=a__       ,  data_format=a__       ,  **a__						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__ = None       ,  **a__       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  get_size_dict(a__						)
       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(a__       ,  size=(size["""height"""], size["""width"""])       ,  data_format=a__       ,  **a__						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__ = None       ,  **a__       ,  ):
       return rescale(a__       ,  scale=a__       ,  data_format=a__       ,  **a__						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__ = None       ,  **a__       ,  ):
       return normalize(a__       ,  mean=a__       ,  std=a__       ,  data_format=a__       ,  **a__						)






     def 					__A						(	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__       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase		: str						    =  size if size is not None else self.size
       _lowerCAmelCase		: Dict						    =  get_size_dict(a__       ,  param_name="""size"""       ,  default_to_square=a__						)
       _lowerCAmelCase		: Tuple						    =  resample if resample is not None else self.resample
       _lowerCAmelCase		: List[str]						    =  do_center_crop if do_center_crop is not None else self.do_center_crop
       _lowerCAmelCase		: Union[str, Any]						    =  crop_size if crop_size is not None else self.crop_size
       _lowerCAmelCase		: Any						    =  get_size_dict(a__       ,  param_name="""crop_size"""       ,  default_to_square=a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  do_rescale if do_rescale is not None else self.do_rescale
       _lowerCAmelCase		: 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		: List[Any]						    =  image_mean if image_mean is not None else self.image_mean
       _lowerCAmelCase		: Optional[Any]						    =  image_std if image_std is not None else self.image_std
       _lowerCAmelCase		: Union[str, Any]						    =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _lowerCAmelCase		: Any						    =  make_list_of_images(a__						)

       if not valid_images(a__						):
         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(a__						) for image in images]

       # All transformations expect numpy arrays.
       _lowerCAmelCase		: List[str]						    =  [to_numpy_array(a__						) for image in images]

       if do_resize:
         _lowerCAmelCase		: Dict						    =  [self.resize(image=a__       ,  size=a__       ,  resample=a__						) for image in images]

       if do_center_crop:
         _lowerCAmelCase		: str						    =  [self.center_crop(image=a__       ,  size=a__						) for image in images]

       if do_rescale:
         _lowerCAmelCase		: Optional[Any]						    =  [self.rescale(image=a__       ,  scale=a__						) for image in images]

       if do_normalize:
         _lowerCAmelCase		: Union[str, Any]						    =  [self.normalize(image=a__       ,  mean=a__       ,  std=a__						) for image in images]

       _lowerCAmelCase		: Optional[int]						    =  [to_channel_dimension_format(a__       ,  a__						) for image in images]

       _lowerCAmelCase		: Dict						    =  {"""pixel_values""": images}
       return BatchFeature(data=a__       ,  tensor_type=a__						)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_a   :	Union[str, Any] 					=     {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Tuple 					=     ['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

     _a   :	Optional[int] 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




