

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


A_ :				Tuple						 =    logging.get_logger(__name__)



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[str]													= ['''pixel_values''']






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

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






					def 						__A    (				self					, A__					, A__					, A__ = PIL.Image.BILINEAR					, A__ = None					, **A__					, ):
						A__							:					Any       =	get_size_dict(A__					, default_to_square=A__    )
						if "shortest_edge" not in size:
							raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""    )
						A__							:					Union[str, Any]       =	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__					, ):
						A__							:					Optional[Any]       =	get_size_dict(A__    )
						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(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__ = None    ):
						return flip_channel_order(A__					, data_format=A__    )






					def 						__A    (				self					, A__					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = ChannelDimension.FIRST					, **A__					, ):
						A__							:					List[str]       =	do_resize if do_resize is not None else self.do_resize
						A__							:					Union[str, Any]       =	resample if resample is not None else self.resample
						A__							:					Optional[Any]       =	do_rescale if do_rescale is not None else self.do_rescale
						A__							:					int       =	rescale_factor if rescale_factor is not None else self.rescale_factor
						A__							:					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__							:					Union[str, Any]       =	size if size is not None else self.size
						A__							:					Any       =	get_size_dict(A__					, default_to_square=A__    )
						A__							:					Optional[Any]       =	crop_size if crop_size is not None else self.crop_size
						A__							:					List[Any]       =	get_size_dict(A__					, param_name="""crop_size"""    )

						A__							:					Dict       =	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_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__							:					str       =	[to_numpy_array(A__    ) for image in images]

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

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

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

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

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

						A__							:					Any       =	{"""pixel_values""": images}
						return BatchFeature(data=A__					, tensor_type=A__    )






					def 						__A    (				self					, A__					, A__ = None    ):
						A__							:					Union[str, Any]       =	outputs.logits

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

							if is_torch_tensor(A__    ):
								A__							:					Tuple       =	target_sizes.numpy()

							A__							:					List[Any]       =	[]

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

						return semantic_segmentation






import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 		UpperCamelCase      (*lowercase_:				Optional[int]					,   lowercase_:				Optional[Union[Dict, Any]] = None					,   lowercase_:				Dict=True					,   lowercase_:				Tuple=2      )  ->   Dict:
	from .. import __version__

	A__							:					Dict       =	take_from
	A__							:					str       =	()
	if not isinstance(args[0]					,   lowercase_      ):
		A__							:					int       =	(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(lowercase_      ).base_version      ) >= version.parse(lowercase_      ):
			raise ValueError(
			    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
			    f""" version {__version__} is >= {version_name}"""      )

		A__							:					Any       =	None
		if isinstance(lowercase_					,   lowercase_      ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(lowercase_      ),)
			A__							:					List[str]       =	f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
		elif hasattr(lowercase_					,   lowercase_      ):
			values += (getattr(lowercase_					,   lowercase_      ),)
			A__							:					Optional[Any]       =	f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
		elif deprecated_kwargs is None:
			A__							:					int       =	f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

		if warning is not None:
			A__							:					int       =	warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message					,   lowercase_					,   stacklevel=lowercase_      )

	if isinstance(lowercase_					,   lowercase_      ) and len(lowercase_      ) > 0:
		A__							:					Union[str, Any]       =	inspect.getouterframes(inspect.currentframe()      )[1]
		A__							:					Optional[Any]       =	call_frame.filename
		A__							:					Optional[int]       =	call_frame.lineno
		A__							:					Any       =	call_frame.function
		A__     ,     A__							:					List[str]       =	next(iter(deprecated_kwargs.items()      )      )
		raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""      )

	if len(lowercase_      ) == 0:
		return
	elif len(lowercase_      ) == 1:
		return values[0]
	return values



