



'''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


snake_case_  	=  logging.get_logger(__name__)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						def constraint_to_multiple_of(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=0  ,  UpperCAmelCase_=None							):
												_UpperCamelCase :		List[str]			    =       round(val / multiple							) * multiple

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

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

												return x

						_UpperCamelCase :		Dict			    =       (output_size, output_size) if isinstance(lowercase_  ,  lowercase_							) else output_size

						_UpperCamelCase    ,      _UpperCamelCase :		Union[str, Any]			    =       get_image_size(lowercase_							)
						_UpperCamelCase    ,      _UpperCamelCase :		List[str]			    =       output_size

						# determine new height and width
						_UpperCamelCase :		Optional[int]			    =       output_height / input_height
						_UpperCamelCase :		Dict			    =       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 :		List[Any]			    =       scale_width
												else:
																		# fit height
																		_UpperCamelCase :		Optional[Any]			    =       scale_height

						_UpperCamelCase :		int			    =       constraint_to_multiple_of(scale_height * input_height  ,  multiple=lowercase_							)
						_UpperCamelCase :		Union[str, Any]			    =       constraint_to_multiple_of(scale_width * input_width  ,  multiple=lowercase_							)

						return (new_height, new_width)






class 							lowercase__     (							A__       ):
	lowercase__      =      ['pixel_values']





	def __init__(			self :    int			,lowerCamelCase__ :    List[Any] = True			,lowerCamelCase__ :    Any = None			,lowerCamelCase__ :    Optional[int] = PILImageResampling.BILINEAR			,lowerCamelCase__ :    List[str] = False			,lowerCamelCase__ :    Optional[int] = 1			,lowerCamelCase__ :    List[Any] = True			,lowerCamelCase__ :    str = 1 / 255			,lowerCamelCase__ :    Optional[Any] = True			,lowerCamelCase__ :    str = None			,lowerCamelCase__ :    Any = None			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       size if size is not None else {'height': 384, 'width': 384}
							_UpperCamelCase :		Tuple			    =       get_size_dict(lowerCamelCase__			)
							_UpperCamelCase :		Any			    =       do_resize
							_UpperCamelCase :		int			    =       size
							_UpperCamelCase :		Dict			    =       keep_aspect_ratio
							_UpperCamelCase :		str			    =       ensure_multiple_of
							_UpperCamelCase :		Optional[Any]			    =       resample
							_UpperCamelCase :		List[str]			    =       do_rescale
							_UpperCamelCase :		Dict			    =       rescale_factor
							_UpperCamelCase :		str			    =       do_normalize
							_UpperCamelCase :		Any			    =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							_UpperCamelCase :		List[str]			    =       image_std if image_std is not None else IMAGENET_STANDARD_STD





	def      UpperCamelCase_  (			self :    Optional[int]			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    List[Any] = False			,lowerCamelCase__ :    List[Any] = 1			,lowerCamelCase__ :    int = PILImageResampling.BICUBIC			,lowerCamelCase__ :    Union[str, Any] = None			,**lowerCamelCase__ :    List[str]			,):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       get_size_dict(lowerCamelCase__			)
							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 :		int			    =       get_resize_output_image_size(
							    lowerCamelCase__			,output_size=(size['height'], size['width'])			,keep_aspect_ratio=lowerCamelCase__			,multiple=lowerCamelCase__			,)
							return resize(lowerCamelCase__			,size=lowerCamelCase__			,resample=lowerCamelCase__			,data_format=lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    Optional[Any] = None			,**lowerCamelCase__ :    str			,):







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





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Tuple			,lowerCamelCase__ :    Any			,lowerCamelCase__ :    Union[str, Any] = None			,**lowerCamelCase__ :    Dict			,):







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





	def      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    int			,lowerCamelCase__ :    str = None			,lowerCamelCase__ :    str = None			,lowerCamelCase__ :    Any = None			,lowerCamelCase__ :    List[str] = None			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    Any = None			,lowerCamelCase__ :    List[str] = None			,lowerCamelCase__ :    Any = None			,lowerCamelCase__ :    Any = None			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    Any = ChannelDimension.FIRST			,**lowerCamelCase__ :    Union[str, Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase :		Optional[Any]			    =       size if size is not None else self.size
							_UpperCamelCase :		List[str]			    =       get_size_dict(lowerCamelCase__			)
							_UpperCamelCase :		str			    =       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							_UpperCamelCase :		Dict			    =       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							_UpperCamelCase :		Union[str, Any]			    =       resample if resample is not None else self.resample
							_UpperCamelCase :		Union[str, Any]			    =       do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase :		Union[str, Any]			    =       rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase :		Tuple			    =       do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase :		Tuple			    =       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 :		List[Any]			    =       make_list_of_images(lowerCamelCase__			)

							if not valid_images(lowerCamelCase__			):
													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 :		Any			    =       [to_numpy_array(lowerCamelCase__			) for image in images]

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

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

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

							_UpperCamelCase :		Tuple			    =       [to_channel_dimension_format(lowerCamelCase__			,lowerCamelCase__			) for image in images]

							_UpperCamelCase :		Optional[int]			    =       {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__			,tensor_type=lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Dict			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    List[Any] = None			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       outputs.logits

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

													if is_torch_tensor(lowerCamelCase__			):
																			_UpperCamelCase :		List[str]			    =       target_sizes.numpy()

													_UpperCamelCase :		int			    =       []

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

							return semantic_segmentation







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_			:     Any  	=  {
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Dict  	=  ['MvpTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MvpForCausalLM',
					    'MvpForConditionalGeneration',
					    'MvpForQuestionAnswering',
					    'MvpForSequenceClassification',
					    'MvpModel',
					    'MvpPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					snake_case_			:     Optional[Any]  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


