code: 
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str			,   _A   :				Any=2_56							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=min(img.size							)
  
    if s < target_image_size:
      raise ValueError(F"""min dim for image {s} < {target_image_size}"""							)
  
    a__						=target_image_size / s
    a__						=(round(r * img.size[1]							), round(r * img.size[0]							))
    a__						=TF.resize(snake_case_			,   snake_case_			,   interpolation=PIL.Image.LANCZOS							)
    a__						=TF.center_crop(snake_case_			,   output_size=2 * [target_image_size]							)
    a__						=torch.unsqueeze(T.ToTensor()(snake_case_							)			,   0							)
    return img
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				int							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=2.0 * x - 1.0
    return x
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				List[str]=True			,   _A   :				int="RGB"							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=x.detach().cpu()
    if process:
      a__						=post_process_tensor(snake_case_							)
    a__						=x.numpy()
    if process:
      a__						=(2_55 * x).astype(np.uinta							)
    a__						=Image.fromarray(snake_case_							)
    if not x.mode == mode:
      a__						=x.convert(snake_case_							)
    return x
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[str]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=torch.clamp(snake_case_			,   -1.0			,   1.0							)
    a__						=(x + 1.0) / 2.0
    a__						=x.permute(1			,   2			,   0							)
    return x
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=post_process_tensor(x.squeeze()							)
    return x.permute(2			,   0			,   1							).unsqueeze(0							)
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCamelCase     =      {
      '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
      '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
  }
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      ['''DebertaV2TokenizerFast''']
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''TFDebertaV2ForMaskedLM''',
             '''TFDebertaV2ForQuestionAnswering''',
             '''TFDebertaV2ForSequenceClassification''',
             '''TFDebertaV2ForTokenClassification''',
             '''TFDebertaV2Model''',
             '''TFDebertaV2PreTrainedModel''',
         ]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DebertaV2ForMaskedLM''',
             '''DebertaV2ForMultipleChoice''',
             '''DebertaV2ForQuestionAnswering''',
             '''DebertaV2ForSequenceClassification''',
             '''DebertaV2ForTokenClassification''',
             '''DebertaV2Model''',
             '''DebertaV2PreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_deberta_va import (
             DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             DebertaVaConfig,
             DebertaVaOnnxConfig,
         )
         from .tokenization_deberta_va import DebertaVaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_deberta_va import (
                    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    TFDebertaVaForMaskedLM,
                    TFDebertaVaForQuestionAnswering,
                    TFDebertaVaForSequenceClassification,
                    TFDebertaVaForTokenClassification,
                    TFDebertaVaModel,
                    TFDebertaVaPreTrainedModel,
                )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_deberta_va import (
                    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DebertaVaForMaskedLM,
                    DebertaVaForMultipleChoice,
                    DebertaVaForQuestionAnswering,
                    DebertaVaForSequenceClassification,
                    DebertaVaForTokenClassification,
                    DebertaVaModel,
                    DebertaVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase     =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  