"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a   :	Tuple 					=     logging.get_logger(__name__)
class 		__A							(				_UpperCAmelCase	):
     _UpperCamelCase							:    Union[str, Any]	      =	['pixel_values']






     def __init__(	self       ,  a__ = True       ,  a__ = None       ,  a__ = PILImageResampling.BICUBIC       ,  a__ = True       ,  a__ = True       ,  a__ = 1 / 255       ,  a__ = None       ,  a__ = True       ,  a__ = None       ,  a__ = None       ,  **a__       ,  ):
       super().__init__(**__UpperCamelCase						)
       _lowerCAmelCase		: Optional[Any]						    =  size if size is not None else {"""height""": 224, """width""": 224}
       _lowerCAmelCase		: str						    =  get_size_dict(__UpperCamelCase						)
       _lowerCAmelCase		: str						    =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
       _lowerCAmelCase		: List[str]						    =  get_size_dict(__UpperCamelCase       ,  default_to_square=__UpperCamelCase       ,  param_name="""crop_size"""						)

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






     def 					__A						(	self       ,  a__       ,  a__       ,  a__ = PILImageResampling.BILINEAR       ,  a__ = None       ,  **a__       ,  ):
       _lowerCAmelCase		: Union[str, Any]						    =  get_size_dict(__UpperCamelCase						)

       if "shortest_edge" in size:
         _lowerCAmelCase		: List[str]						    =  get_resize_output_image_size(__UpperCamelCase       ,  size=size["""shortest_edge"""]       ,  default_to_square=__UpperCamelCase						)
         # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
       elif "height" in size and "width" in size:
         _lowerCAmelCase		: Dict						    =  (size["""height"""], size["""width"""])
       else:
         raise ValueError(F"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"						)
       return resize(__UpperCamelCase       ,  size=__UpperCamelCase       ,  resample=__UpperCamelCase       ,  data_format=__UpperCamelCase       ,  **__UpperCamelCase						)






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






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






     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__ = ChannelDimension.FIRST       ,  **a__       ,  ):
       _lowerCAmelCase		: Dict						    =  do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase		: Union[str, Any]						    =  do_rescale if do_rescale is not None else self.do_rescale
       _lowerCAmelCase		: Union[str, Any]						    =  do_normalize if do_normalize is not None else self.do_normalize
       _lowerCAmelCase		: Dict						    =  do_center_crop if do_center_crop is not None else self.do_center_crop
       _lowerCAmelCase		: str						    =  crop_size if crop_size is not None else self.crop_size
       _lowerCAmelCase		: Any						    =  get_size_dict(__UpperCamelCase       ,  param_name="""crop_size"""       ,  default_to_square=__UpperCamelCase						)
       _lowerCAmelCase		: Optional[Any]						    =  resample if resample is not None else self.resample
       _lowerCAmelCase		: List[str]						    =  rescale_factor if rescale_factor is not None else self.rescale_factor
       _lowerCAmelCase		: Optional[Any]						    =  image_mean if image_mean is not None else self.image_mean
       _lowerCAmelCase		: Union[str, Any]						    =  image_std if image_std is not None else self.image_std

       _lowerCAmelCase		: Union[str, Any]						    =  size if size is not None else self.size
       _lowerCAmelCase		: Union[str, Any]						    =  get_size_dict(__UpperCamelCase						)

       if not is_batched(__UpperCamelCase						):
         _lowerCAmelCase		: Optional[Any]						    =  [images]

       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."""						)

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

       if do_resize:
         _lowerCAmelCase		: 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		: List[Any]						    =  [self.rescale(image=__UpperCamelCase       ,  scale=__UpperCamelCase						) for image in images]

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

       _lowerCAmelCase		: str						    =  [to_channel_dimension_format(__UpperCamelCase       ,  __UpperCamelCase						) for image in images]

       _lowerCAmelCase		: Optional[Any]						    =  {"""pixel_values""": images}
       return BatchFeature(data=__UpperCamelCase       ,  tensor_type=__UpperCamelCase						)


"""simple docstring"""




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel




