






'''simple docstring'''
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__	=     logging.get_logger(__name__)


class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				List[Any]						    =      ['pixel_values']




    def __init__(     self					:    Optional[int]					,       a					:    bool = True					,       a					:    Optional[Dict[str, int]] = None					,       a					:    PILImageResampling = PILImageResampling.BILINEAR					,       a					:    bool = True					,       a					:    Union[int, float] = 1 / 2_5_5					,       a					:    bool = True					,       a					:    Optional[Union[float, List[float]]] = None					,       a					:    Optional[Union[float, List[float]]] = None					,       **a					:    str					,       ):






       '''simple docstring'''






       super().__init__(**a     )
       lowercase_		:      List[Any]          =       size if size is not None else {"height": 2_2_4, "width": 2_2_4}
       lowercase_		:      Any          =       get_size_dict(a     )
       lowercase_		:      Any          =       do_resize
       lowercase_		:      Any          =       do_rescale
       lowercase_		:      Optional[int]          =       do_normalize
       lowercase_		:      Dict          =       size
       lowercase_		:      Tuple          =       resample
       lowercase_		:      Any          =       rescale_factor
       lowercase_		:      Any          =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase_		:      int          =       image_std if image_std is not None else IMAGENET_STANDARD_STD




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]					,       a					:    np.ndarray					,       a					:    Dict[str, int]					,       a					:    PILImageResampling = PILImageResampling.BILINEAR					,       a					:    Optional[Union[str, ChannelDimension]] = None					,       **a					:    Dict					,       ):






       '''simple docstring'''






       lowercase_		:      Tuple          =       get_size_dict(a     )
       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(
           a					,       size=(size["height"], size["width"])					,       resample=a					,       data_format=a					,       **a     )




    def 			lowerCAmelCase__			(     self					:    List[Any]					,       a					:    np.ndarray					,       a					:    float					,       a					:    Optional[Union[str, ChannelDimension]] = None					,       **a					:    Tuple     ):






       '''simple docstring'''






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




    def 			lowerCAmelCase__			(     self					:    Optional[Any]					,       a					:    np.ndarray					,       a					:    Union[float, List[float]]					,       a					:    Union[float, List[float]]					,       a					:    Optional[Union[str, ChannelDimension]] = None					,       **a					:    List[str]					,       ):






       '''simple docstring'''






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





    def 			lowerCAmelCase__			(     self					:    List[str]					,       a					:    ImageInput					,       a					:    Optional[bool] = None					,       a					:    Dict[str, int] = None					,       a					:    PILImageResampling = None					,       a					:    Optional[bool] = None					,       a					:    Optional[float] = None					,       a					:    Optional[bool] = None					,       a					:    Optional[Union[float, List[float]]] = None					,       a					:    Optional[Union[float, List[float]]] = None					,       a					:    Optional[Union[str, TensorType]] = None					,       a					:    Union[str, ChannelDimension] = ChannelDimension.FIRST					,       **a					:    Any					,       ):






       '''simple docstring'''






       lowercase_		:      int          =       do_resize if do_resize is not None else self.do_resize
       lowercase_		:      Tuple          =       do_rescale if do_rescale is not None else self.do_rescale
       lowercase_		:      Tuple          =       do_normalize if do_normalize is not None else self.do_normalize
       lowercase_		:      Tuple          =       resample if resample is not None else self.resample
       lowercase_		:      Optional[int]          =       rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase_		:      Optional[Any]          =       image_mean if image_mean is not None else self.image_mean
       lowercase_		:      int          =       image_std if image_std is not None else self.image_std

       lowercase_		:      Any          =       size if size is not None else self.size
       lowercase_		:      Union[str, Any]          =       get_size_dict(a     )

       lowercase_		:      int          =       make_list_of_images(a     )

       if not valid_images(a     ):
          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.
       lowercase_		:      List[Any]          =       [to_numpy_array(a     ) for image in images]

       if do_resize:
          lowercase_		:      Dict          =       [self.resize(image=a					,       size=a					,       resample=a     ) for image in images]

       if do_rescale:
          lowercase_		:      int          =       [self.rescale(image=a					,       scale=a     ) for image in images]

       if do_normalize:
          lowercase_		:      Tuple          =       [self.normalize(image=a					,       mean=a					,       std=a     ) for image in images]

       lowercase_		:      Optional[int]          =       [to_channel_dimension_format(a					,       a     ) for image in images]

       lowercase_		:      List[Any]          =       {"pixel_values": images}
       return BatchFeature(data=a					,       tensor_type=a     )












'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCamelCase__	=     {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__	=     [
     '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__	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




