


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


lowerCamelCase						=		logging.get_logger(__name__)





class  _a							(    _lowercase):
	_a  :  List[str]				   =							['''pixel_values''']




	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Dict[str, int] = None		, _SCREAMING_SNAKE_CASE						:		float = None		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = PILImageResampling.BILINEAR		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Union[int, float] = 1 / 255		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Optional[Union[float, List[float]]] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[float, List[float]]] = None		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	None:
			super().__init__(**_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[str]   					= size if size is not None else {'''shortest_edge''': 384}
			lowerCAmelCase__       :      Optional[int]   					= get_size_dict(_SCREAMING_SNAKE_CASE		, default_to_square=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[str]   					= do_resize
			lowerCAmelCase__       :      Union[str, Any]   					= size
			# Default value set here for backwards compatibility where the value in config is None
			lowerCAmelCase__       :      str   					= crop_pct if crop_pct is not None else 224 / 256
			lowerCAmelCase__       :      Tuple   					= resample
			lowerCAmelCase__       :      Optional[Any]   					= do_rescale
			lowerCAmelCase__       :      List[Any]   					= rescale_factor
			lowerCAmelCase__       :      Any   					= do_normalize
			lowerCAmelCase__       :      Dict   					= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			lowerCAmelCase__       :      Any   					= image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Dict[str, int]		, _SCREAMING_SNAKE_CASE						:		float		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = PILImageResampling.BICUBIC		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		List[Any]		, )->	np.ndarray:
			lowerCAmelCase__       :      Dict   					= get_size_dict(_SCREAMING_SNAKE_CASE		, default_to_square=_SCREAMING_SNAKE_CASE		)
			if "shortest_edge" not in size:
					raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'		)
			lowerCAmelCase__       :      Dict   					= size['''shortest_edge''']

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




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




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




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		ImageInput		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		Dict[str, int] = None		, _SCREAMING_SNAKE_CASE						:		float = None		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = None		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		float = None		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[float, List[float]]] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[float, List[float]]] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, TensorType]] = None		, _SCREAMING_SNAKE_CASE						:		ChannelDimension = ChannelDimension.FIRST		, **_SCREAMING_SNAKE_CASE						:		Dict		, )->	PIL.Image.Image:
			lowerCAmelCase__       :      Optional[int]   					= do_resize if do_resize is not None else self.do_resize
			lowerCAmelCase__       :      Dict   					= crop_pct if crop_pct is not None else self.crop_pct
			lowerCAmelCase__       :      Union[str, Any]   					= resample if resample is not None else self.resample
			lowerCAmelCase__       :      str   					= do_rescale if do_rescale is not None else self.do_rescale
			lowerCAmelCase__       :      List[str]   					= rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCAmelCase__       :      Any   					= do_normalize if do_normalize is not None else self.do_normalize
			lowerCAmelCase__       :      List[Any]   					= image_mean if image_mean is not None else self.image_mean
			lowerCAmelCase__       :      List[Any]   					= image_std if image_std is not None else self.image_std

			lowerCAmelCase__       :      Optional[int]   					= size if size is not None else self.size
			lowerCAmelCase__       :      Tuple   					= get_size_dict(_SCREAMING_SNAKE_CASE		, default_to_square=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      int   					= make_list_of_images(_SCREAMING_SNAKE_CASE		)

			if not valid_images(_SCREAMING_SNAKE_CASE		):
					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.
			lowerCAmelCase__       :      Optional[Any]   					= [to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

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

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

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

			lowerCAmelCase__       :      Union[str, Any]   					= [to_channel_dimension_format(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		) for image in images]

			lowerCAmelCase__       :      Union[str, Any]   					= {'''pixel_values''': images}
			return BatchFeature(data=_SCREAMING_SNAKE_CASE		, tensor_type=_SCREAMING_SNAKE_CASE		)








import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		def wrapper(*_a     ,       **_a	):
				lowerCAmelCase__       :      List[str]   					= timeit.default_timer()
				lowerCAmelCase__       :      List[Any]   					= func(*_a     ,       **_a	)
				lowerCAmelCase__       :      Any   					= timeit.default_timer() - starttime
				return delta

		lowerCAmelCase__       :      Any   					= func.__name__

		return wrapper






def 					lowerCamelCase_   (	_a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__       :      str   					= seq_shapes or {}
		for i in range(_a	):
				lowerCAmelCase__       :      List[str]   					= {}
				for col_id, (k, v) in enumerate(features.items()	):
						if isinstance(_a     ,       _ArrayXD	):
								lowerCAmelCase__       :      List[str]   					= np.random.rand(*v.shape	).astype(v.dtype	)
						elif isinstance(_a     ,       datasets.Value	):
								if v.dtype == "string":
										lowerCAmelCase__       :      Dict   					= '''The small grey turtle was surprisingly fast when challenged.'''
								else:
										lowerCAmelCase__       :      Any   					= np.random.randint(10     ,       size=1	).astype(v.dtype	).item()
						elif isinstance(_a     ,       datasets.Sequence	):
								while isinstance(_a     ,       datasets.Sequence	):
										lowerCAmelCase__       :      Optional[int]   					= v.feature
								lowerCAmelCase__       :      str   					= seq_shapes[k]
								lowerCAmelCase__       :      Any   					= np.random.rand(*_a	).astype(v.dtype	)
						lowerCAmelCase__       :      int   					= data

				dummy_data.append((i, example)	)

		return dummy_data






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= generate_examples(_a     ,       num_examples=_a     ,       seq_shapes=_a	)

		with ArrowWriter(features=_a     ,       path=_a	) as writer:
				for key, record in dummy_data:
						lowerCAmelCase__       :      Optional[int]   					= features.encode_example(_a	)
						writer.write(_a	)

				lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= writer.finalize()

		if not num_final_examples == num_examples:
				raise ValueError(
				    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'	)

		lowerCAmelCase__       :      List[Any]   					= datasets.Dataset.from_file(filename=_a     ,       info=datasets.DatasetInfo(features=_a	)	)

		return dataset




