






'''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,
    get_resize_output_image_size,
    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


a_							=    logging.get_logger(__name__)


class      lowercase__     (			_snake_case	):
	a_         =["""pixel_values"""]

	def __init__(							self		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    __UpperCAmelCase = PILImageResampling.BILINEAR		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    __UpperCAmelCase = True		,    __UpperCAmelCase = 1 / 255		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					None:







							'''simple docstring'''





							super().__init__(**lowerCAmelCase__       )
							lowerCAmelCase__       =						size if size is not None else {"shortest_edge": 256}
							lowerCAmelCase__       =						get_size_dict(lowerCAmelCase__		,    default_to_square=lowerCAmelCase__       )
							lowerCAmelCase__       =						crop_size if crop_size is not None else {"height": 224, "width": 224}
							lowerCAmelCase__       =						get_size_dict(lowerCAmelCase__       )
							lowerCAmelCase__       =						do_resize
							lowerCAmelCase__       =						size
							lowerCAmelCase__       =						resample
							lowerCAmelCase__       =						do_center_crop
							lowerCAmelCase__       =						crop_size
							lowerCAmelCase__       =						do_rescale
							lowerCAmelCase__       =						rescale_factor
							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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = PILImageResampling.BICUBIC		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					np.ndarray:







							'''simple docstring'''





							lowerCAmelCase__       =						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()}"       )
							lowerCAmelCase__       =						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		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					np.ndarray:







							'''simple docstring'''





							lowerCAmelCase__       =						get_size_dict(lowerCAmelCase__       )
							return center_crop(lowerCAmelCase__		,    size=(size["height"], size["width"])		,    data_format=lowerCAmelCase__		,    **lowerCAmelCase__       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    **__UpperCAmelCase       )->					np.ndarray:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    **__UpperCAmelCase		,    )->					np.ndarray:







							'''simple docstring'''





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


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = ChannelDimension.FIRST		,    **__UpperCAmelCase		,    )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						do_resize if do_resize is not None else self.do_resize
							lowerCAmelCase__       =						size if size is not None else self.size
							lowerCAmelCase__       =						get_size_dict(lowerCAmelCase__		,    default_to_square=lowerCAmelCase__       )
							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__       =						crop_size if crop_size is not None else self.crop_size
							lowerCAmelCase__       =						get_size_dict(lowerCAmelCase__       )
							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__       =						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__       =						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."       )

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

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

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

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

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

							lowerCAmelCase__       =						[to_channel_dimension_format(lowerCAmelCase__		,    lowerCAmelCase__       ) for image in images]

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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SEWForCTC''',
	    '''SEWForSequenceClassification''',
	    '''SEWModel''',
	    '''SEWPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sew import (
		    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SEWForCTC,
		    SEWForSequenceClassification,
		    SEWModel,
		    SEWPreTrainedModel,
		)


else:
	import sys

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






