


"""simple docstring"""







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a__    :							Union[str, Any]            =					logging.get_logger(__name__)




class 			UpperCamelCase_     (			_snake_case):

	"""simple docstring"""





	snake_case__    :  Tuple    =      ["pixel_values"]




	def __init__(			self      :    int      ,      UpperCAmelCase__      :    Dict = True      ,      UpperCAmelCase__      :    Optional[Any] = 3_2      ,      UpperCAmelCase__      :    Tuple=PILImageResampling.BILINEAR      ,      UpperCAmelCase__      :    Dict = True      ,      **UpperCAmelCase__      :    Any      ,      )	->     None:
						__SCREAMING_SNAKE_CASE						       =    do_resize
						__SCREAMING_SNAKE_CASE						       =    do_rescale
						__SCREAMING_SNAKE_CASE						       =    size_divisor
						__SCREAMING_SNAKE_CASE						       =    resample
						super().__init__(**UpperCamelCase__       )




	def   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    Tuple = None      ,      **UpperCAmelCase__      :    Optional[Any]       )	->     np.ndarray:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    get_image_size(UpperCamelCase__       )
						# Rounds the height and width down to the closest multiple of size_divisor
						__SCREAMING_SNAKE_CASE						       =    height // size_divisor * size_divisor
						__SCREAMING_SNAKE_CASE						       =    width // size_divisor * size_divisor
						__SCREAMING_SNAKE_CASE						       =    resize(UpperCamelCase__      ,      (new_h, new_w)      ,      resample=UpperCamelCase__      ,      data_format=UpperCamelCase__      ,      **UpperCamelCase__       )
						return image




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    str = None      ,      **UpperCAmelCase__      :    Tuple       )	->     np.ndarray:
						return rescale(image=UpperCamelCase__      ,      scale=UpperCamelCase__      ,      data_format=UpperCamelCase__      ,      **UpperCamelCase__       )




	def   UpperCAmelCase_				(			self      :    List[str]      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    Dict = None      ,      UpperCAmelCase__      :    int = None      ,      UpperCAmelCase__      :    int=None      ,      UpperCAmelCase__      :    Any = None      ,      UpperCAmelCase__      :    List[Any] = None      ,      UpperCAmelCase__      :    Tuple = ChannelDimension.FIRST      ,      **UpperCAmelCase__      :    Optional[int]      ,      )	->     BatchFeature:
						__SCREAMING_SNAKE_CASE						       =    do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE						       =    do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE						       =    size_divisor if size_divisor is not None else self.size_divisor
						__SCREAMING_SNAKE_CASE						       =    resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
											raise ValueError("size_divisor is required for resizing"       )

						__SCREAMING_SNAKE_CASE						       =    make_list_of_images(UpperCamelCase__       )

						if not valid_images(UpperCamelCase__       ):
											raise ValueError("Invalid image(s)"       )

						# All transformations expect numpy arrays.
						__SCREAMING_SNAKE_CASE						       =    [to_numpy_array(UpperCamelCase__       ) for img in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE						       =    [self.resize(UpperCamelCase__      ,      size_divisor=UpperCamelCase__      ,      resample=UpperCamelCase__       ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE						       =    [self.rescale(UpperCamelCase__      ,      scale=1 / 2_5_5       ) for image in images]

						__SCREAMING_SNAKE_CASE						       =    [to_channel_dimension_format(UpperCamelCase__      ,      UpperCamelCase__       ) for image in images]

						__SCREAMING_SNAKE_CASE						       =    {"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()):
				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
