'''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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase_ : int						  =      logging.get_logger(__name__)


if is_vision_available():
       import PIL





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Optional[Any]   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	PILImageResampling = PILImageResampling.BICUBIC   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Union[int, float] = 1 / 255   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Optional[Union[float, List[float]]] = None   ,		__lowerCAmelCase				:	Optional[Union[float, List[float]]] = None   ,		__lowerCAmelCase				:	bool = True   ,		**__lowerCAmelCase				:	Optional[Any]   ,		)      ->					None:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      size if size is not None else {"shortest_edge": 224}
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
         lowercase_     =      crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase   ,		param_name="crop_size")

         lowercase_     =      do_resize
         lowercase_     =      size
         lowercase_     =      resample
         lowercase_     =      do_center_crop
         lowercase_     =      crop_size
         lowercase_     =      do_rescale
         lowercase_     =      rescale_factor
         lowercase_     =      do_normalize
         lowercase_     =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase_     =      image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase_     =      do_convert_rgb


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict[str, int]   ,		__lowerCAmelCase				:	PILImageResampling = PILImageResampling.BICUBIC   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	List[str]   ,		)      ->					np.ndarray:
         lowercase_     =      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()}')
         lowercase_     =      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				:	Any   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict[str, int]   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Optional[int]   ,		)      ->					np.ndarray:
         lowercase_     =      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, width). Got {size.keys()}')
         return center_crop(__lowerCAmelCase   ,		size=(size["height"], size["width"])   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Union[int, float]   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Tuple   ,		)      ->					Optional[int]:
         return rescale(__lowerCAmelCase   ,		scale=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Union[float, List[float]]   ,		__lowerCAmelCase				:	Union[float, List[float]]   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Any   ,		)      ->					np.ndarray:
         return normalize(__lowerCAmelCase   ,		mean=__lowerCAmelCase   ,		std=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	ImageInput   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	PILImageResampling = None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	int = None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	float = None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	Optional[Union[float, List[float]]] = None   ,		__lowerCAmelCase				:	Optional[Union[float, List[float]]] = None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	Optional[Union[str, TensorType]] = None   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = ChannelDimension.FIRST   ,		**__lowerCAmelCase				:	Tuple   ,		)      ->					PIL.Image.Image:
         lowercase_     =      do_resize if do_resize is not None else self.do_resize
         lowercase_     =      size if size is not None else self.size
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		param_name="size"   ,		default_to_square=__lowerCAmelCase)
         lowercase_     =      resample if resample is not None else self.resample
         lowercase_     =      do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase_     =      crop_size if crop_size is not None else self.crop_size
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		param_name="crop_size"   ,		default_to_square=__lowerCAmelCase)
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase_     =      do_normalize if do_normalize is not None else self.do_normalize
         lowercase_     =      image_mean if image_mean is not None else self.image_mean
         lowercase_     =      image_std if image_std is not None else self.image_std
         lowercase_     =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         lowercase_     =      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.")

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
               lowercase_     =      [convert_to_rgb(__lowerCAmelCase) for image in images]

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

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

         if do_center_crop:
               lowercase_     =      [self.center_crop(image=__lowerCAmelCase   ,		size=__lowerCAmelCase) for image in images]

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

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

         lowercase_     =      [to_channel_dimension_format(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      {"pixel_values": images}
         return BatchFeature(data=__lowerCAmelCase   ,		tensor_type=__lowerCAmelCase)







'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase_ : str						  =      {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      [
           "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Wav2Vec2ForAudioFrameClassification",
           "Wav2Vec2ForCTC",
           "Wav2Vec2ForMaskedLM",
           "Wav2Vec2ForPreTraining",
           "Wav2Vec2ForSequenceClassification",
           "Wav2Vec2ForXVector",
           "Wav2Vec2Model",
           "Wav2Vec2PreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : int						  =      [
           "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFWav2Vec2ForCTC",
           "TFWav2Vec2Model",
           "TFWav2Vec2PreTrainedModel",
           "TFWav2Vec2ForSequenceClassification",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Dict						  =      [
           "FlaxWav2Vec2ForCTC",
           "FlaxWav2Vec2ForPreTraining",
           "FlaxWav2Vec2Model",
           "FlaxWav2Vec2PreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
       from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
       from .processing_wavaveca import WavaVecaProcessor
       from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavaveca import (
                  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavaVecaForAudioFrameClassification,
                  WavaVecaForCTC,
                  WavaVecaForMaskedLM,
                  WavaVecaForPreTraining,
                  WavaVecaForSequenceClassification,
                  WavaVecaForXVector,
                  WavaVecaModel,
                  WavaVecaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWavaVecaForCTC,
                  TFWavaVecaForSequenceClassification,
                  TFWavaVecaModel,
                  TFWavaVecaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  FlaxWavaVecaForCTC,
                  FlaxWavaVecaForPreTraining,
                  FlaxWavaVecaModel,
                  FlaxWavaVecaPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_ : Optional[int]						  =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






