






'''simple docstring'''



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      :       List[Any]      			=      logging.get_logger(__name__)
class 							__SCREAMING_SNAKE_CASE       (							lowerCamelCase__     ):
      __a  					=["pixel_values"]




      def __init__(  self ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = True ,   lowerCamelCase = 1 / 255 ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->None:



         '''simple docstring'''
         super().__init__(**__lowerCamelCase      )
         __a				     =    size if size is not None else {'''shortest_edge''': 256}
         __a				     =    get_size_dict(__lowerCamelCase ,   default_to_square=__lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = PILImageResampling.BICUBIC ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase      )							->np.ndarray:



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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   **lowerCamelCase ,   )							->Optional[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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Dict      			=      ["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      [
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __UpperCamelCase      :       List[Any]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)