






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import 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, logging


_UpperCAmelCase				:	Any    						=    logging.get_logger(__name__)


class 		lowerCAmelCase_							(     UpperCamelCase   ):
     __lowerCamelCase					:						Tuple     =    ['''pixel_values''']





     def __init__(  self       ,   _UpperCAmelCase = True       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = PILImageResampling.BILINEAR       ,   _UpperCAmelCase = True       ,   _UpperCAmelCase = 1 / 255       ,   _UpperCAmelCase = True       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   **_UpperCAmelCase       ,   )		->							None:
        super().__init__(**_UpperCAmelCase     )
        lowerCamelCase											=size if size is not None else {"""height""": 224, """width""": 224}
        lowerCamelCase											=get_size_dict(_UpperCAmelCase     )
        lowerCamelCase											=do_resize
        lowerCamelCase											=do_rescale
        lowerCamelCase											=do_normalize
        lowerCamelCase											=size
        lowerCamelCase											=resample
        lowerCamelCase											=rescale_factor
        lowerCamelCase											=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowerCamelCase											=image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase = PILImageResampling.BILINEAR       ,   _UpperCAmelCase = None       ,   **_UpperCAmelCase       ,   )		->							np.ndarray:
        lowerCamelCase											=get_size_dict(_UpperCAmelCase     )
        if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''     )
        return resize(
            _UpperCAmelCase       ,   size=(size["""height"""], size["""width"""])       ,   resample=_UpperCAmelCase       ,   data_format=_UpperCAmelCase       ,   **_UpperCAmelCase     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase = None       ,   **_UpperCAmelCase     )		->							np.ndarray:
        return rescale(_UpperCAmelCase       ,   scale=_UpperCAmelCase       ,   data_format=_UpperCAmelCase       ,   **_UpperCAmelCase     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase = None       ,   **_UpperCAmelCase       ,   )		->							np.ndarray:
        return normalize(_UpperCAmelCase       ,   mean=_UpperCAmelCase       ,   std=_UpperCAmelCase       ,   data_format=_UpperCAmelCase       ,   **_UpperCAmelCase     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = ChannelDimension.FIRST       ,   **_UpperCAmelCase       ,   )		->							Tuple:
        lowerCamelCase											=do_resize if do_resize is not None else self.do_resize
        lowerCamelCase											=do_rescale if do_rescale is not None else self.do_rescale
        lowerCamelCase											=do_normalize if do_normalize is not None else self.do_normalize
        lowerCamelCase											=resample if resample is not None else self.resample
        lowerCamelCase											=rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCamelCase											=image_mean if image_mean is not None else self.image_mean
        lowerCamelCase											=image_std if image_std is not None else self.image_std

        lowerCamelCase											=size if size is not None else self.size
        lowerCamelCase											=get_size_dict(_UpperCAmelCase     )

        lowerCamelCase											=make_list_of_images(_UpperCAmelCase     )

        if not valid_images(_UpperCAmelCase     ):
           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_rescale and rescale_factor is None:
           raise ValueError("""Rescale factor must be specified if do_rescale is True."""     )

        # All transformations expect numpy arrays.
        lowerCamelCase											=[to_numpy_array(_UpperCAmelCase     ) for image in images]

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

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

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

        lowerCamelCase											=[to_channel_dimension_format(_UpperCAmelCase       ,   _UpperCAmelCase     ) for image in images]

        lowerCamelCase											={"""pixel_values""": images}
        return BatchFeature(data=_UpperCAmelCase       ,   tensor_type=_UpperCAmelCase     )







from typing import TYPE_CHECKING

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


_UpperCAmelCase				:	List[str]    						=    {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase				:	Optional[Any]    						=    [
       'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'UniSpeechSatForAudioFrameClassification',
       'UniSpeechSatForCTC',
       'UniSpeechSatForPreTraining',
       'UniSpeechSatForSequenceClassification',
       'UniSpeechSatForXVector',
       'UniSpeechSatModel',
       'UniSpeechSatPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

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