


"""simple docstring"""






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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


UpperCamelCase__				:List[str]			   =	logging.get_logger(__name__)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple[int, int]:
			def constraint_to_multiple_of(snake_case__       ,					snake_case__       ,					snake_case__=0       ,					snake_case__=None		):
						_UpperCamelCase					:List[str]					     =	round(val / multiple		) * multiple

						if max_val is not None and x > max_val:
									_UpperCamelCase					:Dict					     =	math.floor(val / multiple		) * multiple

						if x < min_val:
									_UpperCamelCase					:List[Any]					     =	math.ceil(val / multiple		) * multiple

						return x

			_UpperCamelCase					:Tuple					     =	(output_size, output_size) if isinstance(snake_case__       ,					snake_case__		) else output_size

			_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	get_image_size(snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	output_size

			# determine new height and width
			_UpperCamelCase					:List[Any]					     =	output_height / input_height
			_UpperCamelCase					:List[str]					     =	output_width / input_width

			if keep_aspect_ratio:
						# scale as little as possible
						if abs(1 - scale_width		) < abs(1 - scale_height		):
									# fit width
									_UpperCamelCase					:Optional[int]					     =	scale_width
						else:
									# fit height
									_UpperCamelCase					:str					     =	scale_height

			_UpperCamelCase					:List[Any]					     =	constraint_to_multiple_of(scale_height * input_height       ,					multiple=snake_case__		)
			_UpperCamelCase					:Dict					     =	constraint_to_multiple_of(scale_width * input_width       ,					multiple=snake_case__		)

			return (new_height, new_width)






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							["pixel_values"]
		def __init__( self					,				SCREAMING_SNAKE_CASE__ = True					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR					,				SCREAMING_SNAKE_CASE__ = False					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = True					,				SCREAMING_SNAKE_CASE__ = 1 / 2_55					,				SCREAMING_SNAKE_CASE__ = True					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	None:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:int					     =	size if size is not None else {'''height''': 3_84, '''width''': 3_84}
					_UpperCamelCase					:Union[str, Any]					     =	get_size_dict(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	do_resize
					_UpperCamelCase					:Tuple					     =	size
					_UpperCamelCase					:Union[str, Any]					     =	keep_aspect_ratio
					_UpperCamelCase					:Dict					     =	ensure_multiple_of
					_UpperCamelCase					:Tuple					     =	resample
					_UpperCamelCase					:List[str]					     =	do_rescale
					_UpperCamelCase					:List[Any]					     =	rescale_factor
					_UpperCamelCase					:Optional[Any]					     =	do_normalize
					_UpperCamelCase					:List[Any]					     =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_UpperCamelCase					:Any					     =	image_std if image_std is not None else IMAGENET_STANDARD_STD
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = False					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC					,				SCREAMING_SNAKE_CASE__ = None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	np.ndarray:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	get_size_dict(SCREAMING_SNAKE_CASE__   )
					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()}"   )
					_UpperCamelCase					:Optional[int]					     =	get_resize_output_image_size(
					    SCREAMING_SNAKE_CASE__					,				output_size=(size['''height'''], size['''width'''])					,				keep_aspect_ratio=SCREAMING_SNAKE_CASE__					,				multiple=SCREAMING_SNAKE_CASE__					,				)
					return resize(SCREAMING_SNAKE_CASE__					,				size=SCREAMING_SNAKE_CASE__					,				resample=SCREAMING_SNAKE_CASE__					,				data_format=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Tuple:

					"""simple docstring"""



					return rescale(SCREAMING_SNAKE_CASE__					,				scale=SCREAMING_SNAKE_CASE__					,				data_format=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	np.ndarray:

					"""simple docstring"""



					return normalize(SCREAMING_SNAKE_CASE__					,				mean=SCREAMING_SNAKE_CASE__					,				std=SCREAMING_SNAKE_CASE__					,				data_format=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST					,				**SCREAMING_SNAKE_CASE__					,				)      ->	PIL.Image.Image:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	do_resize if do_resize is not None else self.do_resize
					_UpperCamelCase					:Union[str, Any]					     =	size if size is not None else self.size
					_UpperCamelCase					:Optional[int]					     =	get_size_dict(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
					_UpperCamelCase					:Optional[int]					     =	ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
					_UpperCamelCase					:Tuple					     =	resample if resample is not None else self.resample
					_UpperCamelCase					:Optional[int]					     =	do_rescale if do_rescale is not None else self.do_rescale
					_UpperCamelCase					:List[str]					     =	rescale_factor if rescale_factor is not None else self.rescale_factor
					_UpperCamelCase					:Optional[int]					     =	do_normalize if do_normalize is not None else self.do_normalize
					_UpperCamelCase					:List[Any]					     =	image_mean if image_mean is not None else self.image_mean
					_UpperCamelCase					:Tuple					     =	image_std if image_std is not None else self.image_std

					_UpperCamelCase					:Any					     =	make_list_of_images(SCREAMING_SNAKE_CASE__   )

					if not valid_images(SCREAMING_SNAKE_CASE__   ):
								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.'''   )

					# All transformations expect numpy arrays.
					_UpperCamelCase					:List[str]					     =	[to_numpy_array(SCREAMING_SNAKE_CASE__   ) for image in images]

					if do_resize:
								_UpperCamelCase					:int					     =	[self.resize(image=SCREAMING_SNAKE_CASE__					,				size=SCREAMING_SNAKE_CASE__					,				resample=SCREAMING_SNAKE_CASE__   ) for image in images]

					if do_rescale:
								_UpperCamelCase					:Any					     =	[self.rescale(image=SCREAMING_SNAKE_CASE__					,				scale=SCREAMING_SNAKE_CASE__   ) for image in images]

					if do_normalize:
								_UpperCamelCase					:List[Any]					     =	[self.normalize(image=SCREAMING_SNAKE_CASE__					,				mean=SCREAMING_SNAKE_CASE__					,				std=SCREAMING_SNAKE_CASE__   ) for image in images]

					_UpperCamelCase					:Optional[int]					     =	[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) for image in images]

					_UpperCamelCase					:Dict					     =	{'''pixel_values''': images}
					return BatchFeature(data=SCREAMING_SNAKE_CASE__					,				tensor_type=SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	outputs.logits

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

								if is_torch_tensor(SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase					:str					     =	target_sizes.numpy()

								_UpperCamelCase					:List[str]					     =	[]

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

					return semantic_segmentation





"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							["image_processor", "tokenizer"]
		A    =							"ViTImageProcessor"
		A    =							("CLIPTokenizer", "CLIPTokenizerFast")
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
								    ''' instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
								_UpperCamelCase					:List[str]					     =	kwargs.pop('''feature_extractor'''   )

					_UpperCamelCase					:Optional[Any]					     =	image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
								raise ValueError('''You need to specify an `image_processor`.'''   )
					if tokenizer is None:
								raise ValueError('''You need to specify a `tokenizer`.'''   )

					super().__init__(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def __call__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					if text is None and visual_prompt is None and images is None:
								raise ValueError('''You have to specify either text, visual prompt or images.'''   )

					if text is not None and visual_prompt is not None:
								raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

					if text is not None:
								_UpperCamelCase					:Dict					     =	self.tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if images is not None:
								_UpperCamelCase					:Union[str, Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None and images is not None:
								_UpperCamelCase					:List[str]					     =	{
								    '''pixel_values''': image_features.pixel_values,
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					elif text is not None and images is not None:
								_UpperCamelCase					:List[Any]					     =	image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					elif visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	{
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					else:
								return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__   )					,				tensor_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor_class





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor

