






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 convert_to_rgb, 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


if is_vision_available():
							import PIL


A__      :     Any       =					logging.get_logger(__name__)
class  _UpperCAmelCase   (			UpperCamelCase__      ):

				"""simple docstring"""




				lowercase__											=			["""pixel_values"""]




				def __init__(	self		:       Optional[int],    lowerCamelCase		:       bool = True,    lowerCamelCase		:       Dict[str, int] = None,    lowerCamelCase		:       PILImageResampling = PILImageResampling.BICUBIC,    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		:       Union[str, Any],    ):

								'''simple docstring'''
								super().__init__(**lowercase_    )
								lowercase__  					=     size if size is not None else {'''height''': 384, '''width''': 384}
								lowercase__  					=     get_size_dict(lowercase_,    default_to_square=lowercase_    )

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




				def 							lowercase__  (	self		:       List[str],    lowerCamelCase		:       np.ndarray,    lowerCamelCase		:       Dict[str, int],    lowerCamelCase		:       PILImageResampling = PILImageResampling.BICUBIC,    lowerCamelCase		:       Optional[Union[str, ChannelDimension]] = None,    **lowerCamelCase		:       Any,    ):

								'''simple docstring'''
								lowercase__  					=     get_size_dict(lowercase_,    default_to_square=lowercase_    )
								if "height" not in size or "width" not in size:
												raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""    )
								lowercase__  					=     (size['''height'''], size['''width'''])
								return resize(lowercase_,    size=lowercase_,    resample=lowercase_,    data_format=lowercase_,    **lowercase_    )




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       np.ndarray,    lowerCamelCase		:       Union[int, float],    lowerCamelCase		:       Optional[Union[str, ChannelDimension]] = None,    **lowerCamelCase		:       Dict,    ):

								'''simple docstring'''
								return rescale(lowercase_,    scale=lowercase_,    data_format=lowercase_,    **lowercase_    )




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       np.ndarray,    lowerCamelCase		:       Union[float, List[float]],    lowerCamelCase		:       Union[float, List[float]],    lowerCamelCase		:       Optional[Union[str, ChannelDimension]] = None,    **lowerCamelCase		:       str,    ):

								'''simple docstring'''
								return normalize(lowercase_,    mean=lowercase_,    std=lowercase_,    data_format=lowercase_,    **lowercase_    )




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       ImageInput,    lowerCamelCase		:       Optional[bool] = None,    lowerCamelCase		:       Optional[Dict[str, int]] = None,    lowerCamelCase		:       PILImageResampling = None,    lowerCamelCase		:       Optional[bool] = None,    lowerCamelCase		:       Optional[float] = None,    lowerCamelCase		:       Optional[bool] = None,    lowerCamelCase		:       Optional[Union[float, List[float]]] = None,    lowerCamelCase		:       Optional[Union[float, List[float]]] = None,    lowerCamelCase		:       Optional[Union[str, TensorType]] = None,    lowerCamelCase		:       bool = None,    lowerCamelCase		:       ChannelDimension = ChannelDimension.FIRST,    **lowerCamelCase		:       List[Any],    ):

								'''simple docstring'''
								lowercase__  					=     do_resize if do_resize is not None else self.do_resize
								lowercase__  					=     resample if resample is not None else self.resample
								lowercase__  					=     do_rescale if do_rescale is not None else self.do_rescale
								lowercase__  					=     rescale_factor if rescale_factor is not None else self.rescale_factor
								lowercase__  					=     do_normalize if do_normalize is not None else self.do_normalize
								lowercase__  					=     image_mean if image_mean is not None else self.image_mean
								lowercase__  					=     image_std if image_std is not None else self.image_std
								lowercase__  					=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowercase__  					=     size if size is not None else self.size
								lowercase__  					=     get_size_dict(lowercase_,    default_to_square=lowercase_    )

								lowercase__  					=     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 or resample is None:
												raise ValueError('''Size and resample must be specified if do_resize 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__  					=     [convert_to_rgb(lowercase_    ) for image in images]

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

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

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

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

								lowercase__  					=     [to_channel_dimension_format(lowercase_,    lowercase_    ) for image in images]

								lowercase__  					=     BatchFeature(data={'''pixel_values''': images},    tensor_type=lowercase_    )

								return encoded_outputs











'''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()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
  from .pipeline_kandinsky import KandinskyPipeline
  from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
  from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
  from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
  from .text_encoder import MultilingualCLIP



