
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


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







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				["pixel_values"]





	def __init__(			self				,       UpperCamelCase__ = True				,       UpperCamelCase__ = None				,       UpperCamelCase__ = PILImageResampling.BILINEAR				,       UpperCamelCase__ = True				,       UpperCamelCase__ = 1 / 255				,       UpperCamelCase__ = True				,       UpperCamelCase__ = None				,       UpperCamelCase__ = True				,       **UpperCamelCase__				,       ):
					super().__init__(**UpperCamelCase__		)
					A__  :	Optional[Any]         =							size if size is not None else {'''shortest_edge''': 224}
					A__  :	Optional[int]         =							get_size_dict(UpperCamelCase__				,       default_to_square=UpperCamelCase__		)
					A__  :	Union[str, Any]         =							crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
					A__  :	int         =							get_size_dict(UpperCamelCase__				,       param_name='''crop_size'''		)

					A__  :	Union[str, Any]         =							do_resize
					A__  :	str         =							size
					A__  :	List[str]         =							resample
					A__  :	Dict         =							do_rescale
					A__  :	Optional[Any]         =							rescale_factor
					A__  :	List[str]         =							do_center_crop
					A__  :	Tuple         =							crop_size
					A__  :	Dict         =							do_flip_channel_order





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__ = PIL.Image.BILINEAR				,       UpperCamelCase__ = None				,       **UpperCamelCase__				,       ):
					A__  :	Tuple         =							get_size_dict(UpperCamelCase__				,       default_to_square=UpperCamelCase__		)
					if "shortest_edge" not in size:
									raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"		)
					A__  :	Any         =							get_resize_output_image_size(UpperCamelCase__				,       size=size['''shortest_edge''']				,       default_to_square=UpperCamelCase__		)
					return resize(UpperCamelCase__				,       size=UpperCamelCase__				,       resample=UpperCamelCase__				,       data_format=UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__ = None				,       **UpperCamelCase__				,       ):
					A__  :	str         =							get_size_dict(UpperCamelCase__		)
					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()}"		)
					return center_crop(UpperCamelCase__				,       size=(size['''height'''], size['''width'''])				,       data_format=UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__ = None				,       **UpperCamelCase__				,       ):
					return rescale(UpperCamelCase__				,       scale=UpperCamelCase__				,       data_format=UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					return flip_channel_order(UpperCamelCase__				,       data_format=UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = ChannelDimension.FIRST				,       **UpperCamelCase__				,       ):
					A__  :	Optional[Any]         =							do_resize if do_resize is not None else self.do_resize
					A__  :	List[Any]         =							resample if resample is not None else self.resample
					A__  :	List[str]         =							do_rescale if do_rescale is not None else self.do_rescale
					A__  :	List[str]         =							rescale_factor if rescale_factor is not None else self.rescale_factor
					A__  :	Optional[int]         =							do_center_crop if do_center_crop is not None else self.do_center_crop
					A__  :	Union[str, Any]         =							(
					    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
					)

					A__  :	List[Any]         =							size if size is not None else self.size
					A__  :	Dict         =							get_size_dict(UpperCamelCase__				,       default_to_square=UpperCamelCase__		)
					A__  :	Any         =							crop_size if crop_size is not None else self.crop_size
					A__  :	Optional[Any]         =							get_size_dict(UpperCamelCase__				,       param_name='''crop_size'''		)

					A__  :	Optional[int]         =							make_list_of_images(UpperCamelCase__		)

					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_rescale and rescale_factor is None:
									raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

					if do_center_crop and crop_size is None:
									raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

					# All transformations expect numpy arrays.
					A__  :	List[Any]         =							[to_numpy_array(UpperCamelCase__		) for image in images]

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

					if do_center_crop:
									A__  :	Optional[Any]         =							[self.center_crop(image=UpperCamelCase__				,       size=UpperCamelCase__		) for image in images]

					if do_rescale:
									A__  :	Union[str, Any]         =							[self.rescale(image=UpperCamelCase__				,       scale=UpperCamelCase__		) for image in images]

					# the pretrained checkpoints assume images are BGR, not RGB
					if do_flip_channel_order:
									A__  :	Dict         =							[self.flip_channel_order(image=UpperCamelCase__		) for image in images]

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

					A__  :	Tuple         =							{'''pixel_values''': images}
					return BatchFeature(data=UpperCamelCase__				,       tensor_type=UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	List[str]         =							outputs.logits

					# Resize logits and compute semantic segmentation maps
					if target_sizes is not None:
									if len(UpperCamelCase__		) != len(UpperCamelCase__		):
													raise ValueError(
													    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''		)

									if is_torch_tensor(UpperCamelCase__		):
													A__  :	List[str]         =							target_sizes.numpy()

									A__  :	int         =							[]

									for idx in range(len(UpperCamelCase__		)		):
													A__  :	List[str]         =							torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0		)				,       size=target_sizes[idx]				,       mode='''bilinear'''				,       align_corners=UpperCamelCase__		)
													A__  :	Union[str, Any]         =							resized_logits[0].argmax(dim=0		)
													semantic_segmentation.append(UpperCamelCase__		)
					else:
									A__  :	Dict         =							logits.argmax(dim=1		)
									A__  :	int         =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

					return semantic_segmentation

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