



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, 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, is_vision_available, logging


if is_vision_available():
 import PIL


A__			   = logging.get_logger(__name__)





class       __lowerCAmelCase				(  UpperCamelCase_ ):
    __lowerCamelCase				=       ['''pixel_values''']




    def __init__(							self		,       _snake_case = True		,       _snake_case = None		,       _snake_case = PIL.Image.NEAREST		,       _snake_case = False		,       _snake_case = None		,       _snake_case = 1 / 255		,       _snake_case = False		,       _snake_case = True		,       _snake_case = True		,       _snake_case = None		,       _snake_case = None		,       _snake_case = True		,       **_snake_case		,       ):



         """simple docstring"""





         super().__init__(**__UpperCamelCase	)
         _lowerCAmelCase      			=					size if size is not None else {'''height''': 346, '''width''': 346}
         _lowerCAmelCase      			=					get_size_dict(__UpperCamelCase	)
         _lowerCAmelCase      			=					crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
         _lowerCAmelCase      			=					get_size_dict(__UpperCamelCase		,       param_name="""crop_size"""	)

         _lowerCAmelCase      			=					do_resize
         _lowerCAmelCase      			=					size
         _lowerCAmelCase      			=					resample
         _lowerCAmelCase      			=					do_center_crop
         _lowerCAmelCase      			=					crop_size
         _lowerCAmelCase      			=					do_rescale
         _lowerCAmelCase      			=					rescale_factor
         _lowerCAmelCase      			=					rescale_offset
         _lowerCAmelCase      			=					do_normalize
         _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
         _lowerCAmelCase      			=					include_top




    def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case = PIL.Image.NEAREST		,       _snake_case = None		,       **_snake_case		,       ):



         """simple docstring"""





         _lowerCAmelCase      			=					get_size_dict(__UpperCamelCase	)
         if "height" not in size or "width" not in size:
              raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'	)
         return resize(
             __UpperCamelCase		,       size=(size["""height"""], size["""width"""])		,       resample=__UpperCamelCase		,       data_format=__UpperCamelCase		,       **__UpperCamelCase	)




    def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case = None		,       **_snake_case		,       ):



         """simple docstring"""





         _lowerCAmelCase      			=					get_size_dict(__UpperCamelCase	)
         if "height" not in size or "width" not in size:
              raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'	)
         return center_crop(__UpperCamelCase		,       size=(size["""height"""], size["""width"""])		,       data_format=__UpperCamelCase		,       **__UpperCamelCase	)




    def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case = True		,       _snake_case = None		,       **_snake_case		,       ):



         """simple docstring"""





         if offset:
              _lowerCAmelCase      			=					(image - 127.5) * scale
              if data_format is not None:
                   _lowerCAmelCase      			=					to_channel_dimension_format(__UpperCamelCase		,       __UpperCamelCase	)
              _lowerCAmelCase      			=					rescaled_image.astype(np.floataa	)
         else:
              _lowerCAmelCase      			=					rescale(__UpperCamelCase		,       scale=__UpperCamelCase		,       data_format=__UpperCamelCase		,       **__UpperCamelCase	)
         return rescaled_image




    def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case = None		,       **_snake_case		,       ):



         """simple docstring"""





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




    def 							snake_case     (							self		,       _snake_case		,       _snake_case = None		,       _snake_case = None		,       _snake_case=None		,       _snake_case = None		,       _snake_case = None		,       _snake_case = None		,       _snake_case = None		,       _snake_case = None		,       _snake_case = None		,       _snake_case = None		,       _snake_case = None		,       _snake_case = None		,       _snake_case = None		,       _snake_case = ChannelDimension.FIRST		,       **_snake_case		,       ):



         """simple docstring"""





         _lowerCAmelCase      			=					do_resize if do_resize is not None else self.do_resize
         _lowerCAmelCase      			=					resample if resample is not None else self.resample
         _lowerCAmelCase      			=					do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowerCAmelCase      			=					do_rescale if do_rescale is not None else self.do_rescale
         _lowerCAmelCase      			=					rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowerCAmelCase      			=					rescale_offset if rescale_offset is not None else self.rescale_offset
         _lowerCAmelCase      			=					do_normalize if do_normalize is not None else self.do_normalize
         _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      			=					include_top if include_top is not None else self.include_top

         _lowerCAmelCase      			=					size if size is not None else self.size
         _lowerCAmelCase      			=					get_size_dict(__UpperCamelCase	)
         _lowerCAmelCase      			=					crop_size if crop_size is not None else self.crop_size
         _lowerCAmelCase      			=					get_size_dict(__UpperCamelCase		,       param_name="""crop_size"""	)

         _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 or resample is None:
              raise ValueError("""Size and resample 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."""	)

         # 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_center_crop:
              _lowerCAmelCase      			=					[self.center_crop(image=__UpperCamelCase		,       size=__UpperCamelCase	) for image in images]

         if do_rescale:
              _lowerCAmelCase      			=					[self.rescale(image=__UpperCamelCase		,       scale=__UpperCamelCase		,       offset=__UpperCamelCase	) for image in images]

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

         if include_top:
              _lowerCAmelCase      			=					[self.normalize(image=__UpperCamelCase		,       mean=[0, 0, 0]		,       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_tf_available, is_torch_available


A__			   = {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """HubertForCTC""",
	    """HubertForSequenceClassification""",
	    """HubertModel""",
	    """HubertPreTrainedModel""",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFHubertForCTC""",
	    """TFHubertModel""",
	    """TFHubertPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_hubert import (
		    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    HubertForCTC,
		    HubertForSequenceClassification,
		    HubertModel,
		    HubertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_hubert import (
		    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFHubertForCTC,
		    TFHubertModel,
		    TFHubertPreTrainedModel,
		)


else:
	import sys

	A__			   = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



