



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, is_vision_available, logging


if is_vision_available():
		import PIL


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)



class 	lowerCamelCase      (						__UpperCAmelCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  ['pixel_values']



					def __init__(			self    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = PILImageResampling.BILINEAR    ,	lowerCAmelCase = True    ,	lowerCAmelCase = 1 / 255    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							super().__init__(**lowerCAmelCase_     )
							UpperCAmelCase_						=				size if size is not None else {"shortest_edge": 384}
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase_    ,	default_to_square=lowerCAmelCase_     )

							UpperCAmelCase_						=				do_resize
							UpperCAmelCase_						=				size
							# Default value set here for backwards compatibility where the value in config is None
							UpperCAmelCase_						=				crop_pct if crop_pct is not None else 224 / 256
							UpperCAmelCase_						=				resample
							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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = PILImageResampling.BICUBIC    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase_    ,	default_to_square=lowerCAmelCase_     )
							if "shortest_edge" not in size:
									raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''     )
							UpperCAmelCase_						=				size["shortest_edge"]

							if shortest_edge < 384:
									# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
									UpperCAmelCase_						=				int(shortest_edge / crop_pct     )
									UpperCAmelCase_						=				get_resize_output_image_size(lowerCAmelCase_    ,	size=lowerCAmelCase_    ,	default_to_square=lowerCAmelCase_     )
									UpperCAmelCase_						=				resize(image=lowerCAmelCase_    ,	size=lowerCAmelCase_    ,	resample=lowerCAmelCase_    ,	data_format=lowerCAmelCase_    ,	**lowerCAmelCase_     )
									# then crop to (shortest_edge, shortest_edge)
									return center_crop(image=lowerCAmelCase_    ,	size=(shortest_edge, shortest_edge)    ,	data_format=lowerCAmelCase_    ,	**lowerCAmelCase_     )
							else:
									# warping (no cropping) when evaluated at 384 or larger
									return resize(
									    lowerCAmelCase_    ,	size=(shortest_edge, shortest_edge)    ,	resample=lowerCAmelCase_    ,	data_format=lowerCAmelCase_    ,	**lowerCAmelCase_     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return rescale(lowerCAmelCase_    ,	scale=lowerCAmelCase_    ,	data_format=lowerCAmelCase_    ,	**lowerCAmelCase_     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return normalize(lowerCAmelCase_    ,	mean=lowerCAmelCase_    ,	std=lowerCAmelCase_    ,	data_format=lowerCAmelCase_    ,	**lowerCAmelCase_     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = ChannelDimension.FIRST    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				do_resize if do_resize is not None else self.do_resize
							UpperCAmelCase_						=				crop_pct if crop_pct is not None else self.crop_pct
							UpperCAmelCase_						=				resample if resample is not None else self.resample
							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(lowerCAmelCase_    ,	default_to_square=lowerCAmelCase_     )

							UpperCAmelCase_						=				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 or resample is None:
									raise ValueError("Size and resample must be specified if do_resize is True."     )

							if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
									raise ValueError("crop_pct must be specified if size < 384."     )

							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(lowerCAmelCase_     ) for image in images]

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

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

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

							UpperCAmelCase_						=				[to_channel_dimension_format(lowerCAmelCase_    ,	lowerCAmelCase_     ) for image in images]

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE     				=						{"tokenization_herbert": ["HerbertTokenizer"]}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["HerbertTokenizerFast"]


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

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

