





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase							:			Any				={
    "configuration_wav2vec2_conformer": [
        "WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Wav2Vec2ConformerConfig",
    ],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:			Union[str, Any]				=[
      "WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Wav2Vec2ConformerForAudioFrameClassification",
      "Wav2Vec2ConformerForCTC",
      "Wav2Vec2ConformerForPreTraining",
      "Wav2Vec2ConformerForSequenceClassification",
      "Wav2Vec2ConformerForXVector",
      "Wav2Vec2ConformerModel",
      "Wav2Vec2ConformerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_wavaveca_conformer import (
      WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      WavaVecaConformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavaveca_conformer import (
        WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavaVecaConformerForAudioFrameClassification,
        WavaVecaConformerForCTC,
        WavaVecaConformerForPreTraining,
        WavaVecaConformerForSequenceClassification,
        WavaVecaConformerForXVector,
        WavaVecaConformerModel,
        WavaVecaConformerPreTrainedModel,
    )

else:
  import sys

  __lowerCAmelCase							:			str				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''

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       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Union[str, Any]=2_56 ):
    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(_lowerCamelCase		, _lowerCamelCase		, interpolation=PIL.Image.LANCZOS )
    A__       =    TF.center_crop(_lowerCamelCase		, output_size=2 * [target_image_size] )
    A__       =    torch.unsqueeze(T.ToTensor()(_lowerCamelCase )		, 0 )
    return img





def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
    A__       =    2.0 * x - 1.0
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Tuple=True		, _lowerCamelCase      :      int="RGB" ):
    A__       =    x.detach().cpu()
    if process:
        A__       =    post_process_tensor(_lowerCamelCase )
    A__       =    x.numpy()
    if process:
        A__       =    (2_55 * x).astype(np.uinta )
    A__       =    Image.fromarray(_lowerCamelCase )
    if not x.mode == mode:
        A__       =    x.convert(_lowerCamelCase )
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
    A__       =    torch.clamp(_lowerCamelCase		, -1.0		, 1.0 )
    A__       =    (x + 1.0) / 2.0
    A__       =    x.permute(1		, 2		, 0 )
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
    A__       =    post_process_tensor(x.squeeze() )
    return x.permute(2		, 0		, 1 ).unsqueeze(0 )



