


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


__snake_case   							=						logging.get_logger(__name__)



class 	__lowerCamelCase			(_a						):
       _lowercase											=     ["""pixel_values"""]







       def __init__(			self: str,A_: bool = True,A_: Dict[str, int] = None,A_: PILImageResampling = PIL.Image.BICUBIC,A_: bool = True,A_: Dict[str, int] = None,A_: Union[int, float] = 1 / 255,A_: bool = True,A_: bool = True,A_: Optional[Union[float, List[float]]] = None,A_: Optional[Union[float, List[float]]] = None,**A_: int,):






            '''simple docstring'''




            super().__init__(**A_					)
            __UpperCamelCase =			size if size is not None else {'height': 256, 'width': 256}
            __UpperCamelCase =			get_size_dict(A_					)
            __UpperCamelCase =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
            __UpperCamelCase =			get_size_dict(A_,param_name='crop_size'					)

            __UpperCamelCase =			do_resize
            __UpperCamelCase =			size
            __UpperCamelCase =			resample
            __UpperCamelCase =			do_center_crop
            __UpperCamelCase =			crop_size
            __UpperCamelCase =			do_rescale
            __UpperCamelCase =			rescale_factor
            __UpperCamelCase =			do_normalize
            __UpperCamelCase =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __UpperCamelCase =			image_std if image_std is not None else IMAGENET_STANDARD_STD







       def 		snake_case_	(			self: Tuple,A_: np.ndarray,A_: Dict[str, int],A_: PILImageResampling = PIL.Image.BICUBIC,A_: Optional[Union[str, ChannelDimension]] = None,**A_: Union[str, Any],):






            '''simple docstring'''




            __UpperCamelCase =			get_size_dict(A_					)
            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(
                A_,size=(size['height'], size['width']),resample=A_,data_format=A_,**A_					)







       def 		snake_case_	(			self: List[str],A_: np.ndarray,A_: Dict[str, int],A_: Optional[Union[str, ChannelDimension]] = None,**A_: Union[str, Any],):






            '''simple docstring'''




            __UpperCamelCase =			get_size_dict(A_					)
            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(A_,size=(size['height'], size['width']),data_format=A_,**A_					)







       def 		snake_case_	(			self: Optional[int],A_: np.ndarray,A_: Union[int, float],A_: Optional[Union[str, ChannelDimension]] = None,**A_: Union[str, Any],):






            '''simple docstring'''




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







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






            '''simple docstring'''




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







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






            '''simple docstring'''




            __UpperCamelCase =			do_resize if do_resize is not None else self.do_resize
            __UpperCamelCase =			resample if resample is not None else self.resample
            __UpperCamelCase =			do_center_crop if do_center_crop is not None else self.do_center_crop
            __UpperCamelCase =			do_rescale if do_rescale is not None else self.do_rescale
            __UpperCamelCase =			rescale_factor if rescale_factor is not None else self.rescale_factor
            __UpperCamelCase =			do_normalize if do_normalize is not None else self.do_normalize
            __UpperCamelCase =			image_mean if image_mean is not None else self.image_mean
            __UpperCamelCase =			image_std if image_std is not None else self.image_std

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

            __UpperCamelCase =			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 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.
            __UpperCamelCase =			[to_numpy_array(A_					) for image in images]

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

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

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

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

            __UpperCamelCase =			[to_channel_dimension_format(A_,A_					) for image in images]

            __UpperCamelCase =			{'pixel_values': images}
            return BatchFeature(data=A_,tensor_type=A_					)






from typing import TYPE_CHECKING

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


__snake_case   							=						{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''UniSpeechForCTC''',
        '''UniSpeechForPreTraining''',
        '''UniSpeechForSequenceClassification''',
        '''UniSpeechModel''',
        '''UniSpeechPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_unispeech import (
            UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
            UniSpeechForCTC,
            UniSpeechForPreTraining,
            UniSpeechForSequenceClassification,
            UniSpeechModel,
            UniSpeechPreTrainedModel,
        )

else:
    import sys

    __snake_case   							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


