




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


_snake_case				:				List[Any]       							=		logging.get_logger(__name__)

class       A   (		_a   ):
							lowercase_          =    ['pixel_values']







							def __init__(  self	:  str							,      lowerCAmelCase_	:  bool = True							,      lowerCAmelCase_	:  Optional[Dict[str, int]] = None							,      lowerCAmelCase_	:  PILImageResampling = PILImageResampling.BILINEAR							,      lowerCAmelCase_	:  bool = True							,      lowerCAmelCase_	:  Dict[str, int] = None							,      lowerCAmelCase_	:  bool = True							,      lowerCAmelCase_	:  Union[int, float] = 1 / 2_55							,      lowerCAmelCase_	:  bool = True							,      lowerCAmelCase_	:  Optional[Union[float, List[float]]] = None							,      lowerCAmelCase_	:  Optional[Union[float, List[float]]] = None							,      **lowerCAmelCase_	:  List[str]							,      )      ->							None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )
														_a     					=    size if size is not None else {'''shortest_edge''': 2_56}
														_a     					=    get_size_dict(lowerCAmelCase_							,      default_to_square=lowerCAmelCase_ )
														_a     					=    crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a     					=    get_size_dict(lowerCAmelCase_							,      param_name='''crop_size''' )
														_a     					=    do_resize
														_a     					=    size
														_a     					=    resample
														_a     					=    do_center_crop
														_a     					=    crop_size
														_a     					=    do_rescale
														_a     					=    rescale_factor
														_a     					=    do_normalize
														_a     					=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a     					=    image_std if image_std is not None else IMAGENET_STANDARD_STD







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  np.ndarray							,      lowerCAmelCase_	:  Dict[str, int]							,      lowerCAmelCase_	:  PILImageResampling = PILImageResampling.BICUBIC							,      lowerCAmelCase_	:  Optional[Union[str, ChannelDimension]] = None							,      **lowerCAmelCase_	:  Dict							,      )      ->							np.ndarray:


														"""simple docstring"""


														_a     					=    get_size_dict(lowerCAmelCase_							,      default_to_square=lowerCAmelCase_ )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
														_a     					=    get_resize_output_image_size(lowerCAmelCase_							,      size=size['''shortest_edge''']							,      default_to_square=lowerCAmelCase_ )
														return resize(lowerCAmelCase_							,      size=lowerCAmelCase_							,      resample=lowerCAmelCase_							,      data_format=lowerCAmelCase_							,      **lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  np.ndarray							,      lowerCAmelCase_	:  Dict[str, int]							,      lowerCAmelCase_	:  Optional[Union[str, ChannelDimension]] = None							,      **lowerCAmelCase_	:  int							,      )      ->							np.ndarray:


														"""simple docstring"""


														_a     					=    get_size_dict(lowerCAmelCase_ )
														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(lowerCAmelCase_							,      size=(size['''height'''], size['''width'''])							,      data_format=lowerCAmelCase_							,      **lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  np.ndarray							,      lowerCAmelCase_	:  float							,      lowerCAmelCase_	:  Optional[Union[str, ChannelDimension]] = None							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							np.ndarray:


														"""simple docstring"""


														return rescale(lowerCAmelCase_							,      scale=lowerCAmelCase_							,      data_format=lowerCAmelCase_							,      **lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  np.ndarray							,      lowerCAmelCase_	:  Union[float, List[float]]							,      lowerCAmelCase_	:  Union[float, List[float]]							,      lowerCAmelCase_	:  Optional[Union[str, ChannelDimension]] = None							,      **lowerCAmelCase_	:  Tuple							,      )      ->							np.ndarray:


														"""simple docstring"""


														return normalize(lowerCAmelCase_							,      mean=lowerCAmelCase_							,      std=lowerCAmelCase_							,      data_format=lowerCAmelCase_							,      **lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  ImageInput							,      lowerCAmelCase_	:  Optional[bool] = None							,      lowerCAmelCase_	:  Dict[str, int] = None							,      lowerCAmelCase_	:  PILImageResampling = None							,      lowerCAmelCase_	:  bool = None							,      lowerCAmelCase_	:  Dict[str, int] = None							,      lowerCAmelCase_	:  Optional[bool] = None							,      lowerCAmelCase_	:  Optional[float] = None							,      lowerCAmelCase_	:  Optional[bool] = None							,      lowerCAmelCase_	:  Optional[Union[float, List[float]]] = None							,      lowerCAmelCase_	:  Optional[Union[float, List[float]]] = None							,      lowerCAmelCase_	:  Optional[Union[str, TensorType]] = None							,      lowerCAmelCase_	:  Union[str, ChannelDimension] = ChannelDimension.FIRST							,      **lowerCAmelCase_	:  Union[str, Any]							,      )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    do_resize if do_resize is not None else self.do_resize
														_a     					=    size if size is not None else self.size
														_a     					=    get_size_dict(lowerCAmelCase_							,      default_to_square=lowerCAmelCase_ )
														_a     					=    resample if resample is not None else self.resample
														_a     					=    do_center_crop if do_center_crop is not None else self.do_center_crop
														_a     					=    crop_size if crop_size is not None else self.crop_size
														_a     					=    get_size_dict(lowerCAmelCase_							,      param_name='''crop_size''' )
														_a     					=    do_rescale if do_rescale is not None else self.do_rescale
														_a     					=    rescale_factor if rescale_factor is not None else self.rescale_factor
														_a     					=    do_normalize if do_normalize is not None else self.do_normalize
														_a     					=    image_mean if image_mean is not None else self.image_mean
														_a     					=    image_std if image_std is not None else self.image_std

														_a     					=    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:
																					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     					=    [to_numpy_array(lowerCAmelCase_ ) for image in images]

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

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

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

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

														_a     					=    [to_channel_dimension_format(lowerCAmelCase_							,      lowerCAmelCase_ ) for image in images]

														_a     					=    {'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_							,      tensor_type=lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  List[Tuple] = None )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    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_ ):
																												_a     					=    target_sizes.numpy()

																					_a     					=    []

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

														return semantic_segmentation







'''simple docstring'''


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

