

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCAmelCase_							:					Any		 =   {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					int		 =   ['''BartphoTokenizer''']

if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bartpho import BartphoTokenizer

else:
			import sys

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








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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
			import torch


UpperCAmelCase_							:					Optional[int]		 =   logging.get_logger(__name__)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[str]     					=				['''pixel_values''']




	def __init__(     self						:     Dict					,      __A						:     bool = True					,      __A						:     Optional[Dict[str, int]] = None					,      __A						:     PILImageResampling = PILImageResampling.BILINEAR					,      __A						:     bool = True					,      __A						:     Dict[str, int] = None					,      __A						:     bool = True					,      __A						:     Union[int, float] = 1 / 255					,      __A						:     bool = True					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      **__A						:     int					,      ):
		super().__init__(**__A							)
		__A :			Union[str, Any]		    =			size if size is not None else {"""shortest_edge""": 256}
		__A :			Dict		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			str		    =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			str		    =			do_resize
		__A :			Dict		    =			size
		__A :			Any		    =			resample
		__A :			Optional[Any]		    =			do_center_crop
		__A :			List[str]		    =			crop_size
		__A :			Optional[int]		    =			do_rescale
		__A :			int		    =			rescale_factor
		__A :			Union[str, Any]		    =			do_normalize
		__A :			int		    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		__A :			Dict		    =			image_std if image_std is not None else IMAGENET_STANDARD_STD




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     PILImageResampling = PILImageResampling.BICUBIC					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     List[Any]					,      ):
		__A :			str		    =			get_size_dict(__A					,      default_to_square=__A							)
		if "shortest_edge" not in size:
			raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
		__A :			Dict		    =			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        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     str					,      ):
		__A :			str		    =			get_size_dict(__A							)
		if "height" not in size or "width" not in size:
			raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""							)
		return center_crop(__A					,      size=(size["""height"""], size["""width"""])					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     np.ndarray					,      __A						:     float					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Optional[int]							):
		return rescale(__A					,      scale=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     np.ndarray					,      __A						:     Union[float, List[float]]					,      __A						:     Union[float, List[float]]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Tuple					,      ):
		return normalize(__A					,      mean=__A					,      std=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     ImageInput					,      __A						:     Optional[bool] = None					,      __A						:     Dict[str, int] = None					,      __A						:     PILImageResampling = None					,      __A						:     bool = None					,      __A						:     Dict[str, int] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[float] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[str, TensorType]] = None					,      __A						:     Union[str, ChannelDimension] = ChannelDimension.FIRST					,      **__A						:     Optional[int]					,      ):
		__A :			List[str]		    =			do_resize if do_resize is not None else self.do_resize
		__A :			Any		    =			size if size is not None else self.size
		__A :			Union[str, Any]		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			Tuple		    =			resample if resample is not None else self.resample
		__A :			Tuple		    =			do_center_crop if do_center_crop is not None else self.do_center_crop
		__A :			List[Any]		    =			crop_size if crop_size is not None else self.crop_size
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			Tuple		    =			do_rescale if do_rescale is not None else self.do_rescale
		__A :			Optional[Any]		    =			rescale_factor if rescale_factor is not None else self.rescale_factor
		__A :			Optional[int]		    =			do_normalize if do_normalize is not None else self.do_normalize
		__A :			Optional[int]		    =			image_mean if image_mean is not None else self.image_mean
		__A :			List[str]		    =			image_std if image_std is not None else self.image_std

		__A :			Union[str, Any]		    =			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_center_crop and crop_size is None:
			raise ValueError("""Crop size must be specified if do_center_crop 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.
		__A :			Union[str, Any]		    =			[to_numpy_array(__A							) for image in images]

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

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

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

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

		__A :			int		    =			[to_channel_dimension_format(__A					,      __A							) for image in images]

		__A :			Tuple		    =			{"""pixel_values""": images}
		return BatchFeature(data=__A					,      tensor_type=__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     List[str]					,      __A						:     List[Tuple] = 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 :			str		    =			target_sizes.numpy()

			__A :			int		    =			[]

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

		return semantic_segmentation





