



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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__UpperCAmelCase						  =     logging.get_logger(__name__)


if is_vision_available():
		import PIL


class 	lowerCamelCase__ (		_a   ):
						_lowerCAmelCase								= ['''pixel_values''']


						def __init__( self						:  str      ,       _a						:  bool = True      ,       _a						:  Dict[str, int] = None      ,       _a						:  PILImageResampling = PILImageResampling.BILINEAR      ,       _a						:  bool = True      ,       _a						:  bool = False      ,       _a						:  bool = True      ,       _a						:  bool = True      ,       _a						:  Union[int, float] = 1 / 2_5_5      ,       _a						:  bool = True      ,       _a						:  Optional[Union[float, List[float]]] = None      ,       _a						:  Optional[Union[float, List[float]]] = None      ,       **_a						:  str      ,       ):
													super().__init__(**_a			)

													a__:       str											=size if size is not None else {"height": 2_5_6_0, "width": 1_9_2_0}
													if isinstance(_a      ,       (tuple, list)			):
																				# The previous feature extractor size parameter was in (width, height) format
																				a__:       int											=size[::-1]
													a__:       int											=get_size_dict(_a			)

													a__:       str											=do_resize
													a__:       List[str]											=size
													a__:       int											=resample
													a__:       Optional[int]											=do_thumbnail
													a__:       int											=do_align_long_axis
													a__:       Dict											=do_pad
													a__:       str											=do_rescale
													a__:       List[str]											=rescale_factor
													a__:       List[str]											=do_normalize
													a__:       Optional[int]											=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													a__:       Any											=image_std if image_std is not None else IMAGENET_STANDARD_STD


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  np.ndarray      ,       _a						:  Dict[str, int]      ,       _a						:  Optional[Union[str, ChannelDimension]] = None			):
													a__							,			a__:       str											=get_image_size(_a			)
													a__							,			a__:       Union[str, Any]											=size["height"], size["width"]

													if (output_width < output_height and input_width > input_height) or (
													    output_width > output_height and input_width < input_height
													):
																				a__:       List[Any]											=np.rotaa(_a      ,       3			)

													if data_format is not None:
																				a__:       Optional[Any]											=to_channel_dimension_format(_a      ,       _a			)

													return image


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  np.ndarray      ,       _a						:  Dict[str, int]      ,       _a						:  bool = False      ,       _a						:  Optional[Union[str, ChannelDimension]] = None      ,       ):
													a__							,			a__:       Any											=size["height"], size["width"]
													a__							,			a__:       Optional[Any]											=get_image_size(_a			)

													a__:       List[Any]											=output_width - input_width
													a__:       Optional[int]											=output_height - input_height

													if random_padding:
																				a__:       List[Any]											=np.random.randint(low=0      ,       high=delta_height + 1			)
																				a__:       str											=np.random.randint(low=0      ,       high=delta_width + 1			)
													else:
																				a__:       List[str]											=delta_height // 2
																				a__:       int											=delta_width // 2

													a__:       Optional[Any]											=delta_height - pad_top
													a__:       Optional[int]											=delta_width - pad_left

													a__:       str											=((pad_top, pad_bottom), (pad_left, pad_right))
													return pad(_a      ,       _a      ,       data_format=_a			)


						def   _lowerCamelCase			( self						:  List[str]      ,       *_a						:  Any      ,       **_a						:  Union[str, Any]			):
													logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."			)
													return self.pad_image(*_a      ,       **_a			)


						def   _lowerCamelCase			( self						:  Optional[int]      ,       _a						:  np.ndarray      ,       _a						:  Dict[str, int]      ,       _a						:  PILImageResampling = PILImageResampling.BICUBIC      ,       _a						:  Optional[Union[str, ChannelDimension]] = None      ,       **_a						:  Optional[Any]      ,       ):
													a__							,			a__:       int											=get_image_size(_a			)
													a__							,			a__:       Dict											=size["height"], size["width"]

													# We always resize to the smallest of either the input or output size.
													a__:       str											=min(_a      ,       _a			)
													a__:       str											=min(_a      ,       _a			)

													if height == input_height and width == input_width:
																				return image

													if input_height > input_width:
																				a__:       Tuple											=int(input_width * height / input_height			)
													elif input_width > input_height:
																				a__:       Tuple											=int(input_height * width / input_width			)

													return resize(
													    _a      ,       size=(height, width)      ,       resample=_a      ,       reducing_gap=2.0      ,       data_format=_a      ,       **_a			)


						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  np.ndarray      ,       _a						:  Dict[str, int]      ,       _a						:  PILImageResampling = PILImageResampling.BICUBIC      ,       _a						:  Optional[Union[str, ChannelDimension]] = None      ,       **_a						:  Tuple      ,       ):
													a__:       List[Any]											=get_size_dict(_a			)
													a__:       str											=min(size["height"]      ,       size["width"]			)
													a__:       List[str]											=get_resize_output_image_size(_a      ,       size=_a      ,       default_to_square=_a			)
													a__:       List[str]											=resize(_a      ,       size=_a      ,       resample=_a      ,       data_format=_a      ,       **_a			)
													return resized_image


						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  np.ndarray      ,       _a						:  Union[int, float]      ,       _a						:  Optional[Union[str, ChannelDimension]] = None      ,       **_a						:  List[str]      ,       ):
													return rescale(_a      ,       scale=_a      ,       data_format=_a      ,       **_a			)


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  np.ndarray      ,       _a						:  Union[float, List[float]]      ,       _a						:  Union[float, List[float]]      ,       _a						:  Optional[Union[str, ChannelDimension]] = None      ,       **_a						:  Tuple      ,       ):
													return normalize(_a      ,       mean=_a      ,       std=_a      ,       data_format=_a      ,       **_a			)







						def   _lowerCamelCase			( self						:  int      ,       _a						:  ImageInput      ,       _a						:  bool = None      ,       _a						:  Dict[str, int] = None      ,       _a						:  PILImageResampling = None      ,       _a						:  bool = None      ,       _a						:  bool = None      ,       _a						:  bool = None      ,       _a						:  bool = False      ,       _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						:  Optional[ChannelDimension] = ChannelDimension.FIRST      ,       **_a						:  List[str]      ,       ):
													a__:       Dict											=do_resize if do_resize is not None else self.do_resize
													a__:       Optional[Any]											=size if size is not None else self.size
													if isinstance(_a      ,       (tuple, list)			):
																				# Previous feature extractor had size in (width, height) format
																				a__:       int											=size[::-1]
													a__:       Any											=get_size_dict(_a			)
													a__:       Any											=resample if resample is not None else self.resample
													a__:       str											=do_thumbnail if do_thumbnail is not None else self.do_thumbnail
													a__:       Optional[Any]											=do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
													a__:       List[str]											=do_pad if do_pad is not None else self.do_pad
													a__:       Optional[int]											=do_rescale if do_rescale is not None else self.do_rescale
													a__:       Dict											=rescale_factor if rescale_factor is not None else self.rescale_factor
													a__:       Union[str, Any]											=do_normalize if do_normalize is not None else self.do_normalize
													a__:       Optional[int]											=image_mean if image_mean is not None else self.image_mean
													a__:       Any											=image_std if image_std is not None else self.image_std

													a__:       List[Any]											=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:
																				raise ValueError("Size must be specified if do_resize is True."			)

													if do_rescale and rescale_factor is None:
																				raise ValueError("Rescale factor must be specified if do_rescale is True."			)

													if do_pad and size is None:
																				raise ValueError("Size must be specified if do_pad 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.
													a__:       List[str]											=[to_numpy_array(_a			) for image in images]

													if do_align_long_axis:
																				a__:       int											=[self.align_long_axis(_a      ,       size=_a			) for image in images]

													if do_resize:
																				a__:       List[str]											=[self.resize(image=_a      ,       size=_a      ,       resample=_a			) for image in images]

													if do_thumbnail:
																				a__:       Any											=[self.thumbnail(image=_a      ,       size=_a			) for image in images]

													if do_pad:
																				a__:       Dict											=[self.pad_image(image=_a      ,       size=_a      ,       random_padding=_a			) for image in images]

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

													if do_normalize:
																				a__:       List[str]											=[self.normalize(image=_a      ,       mean=_a      ,       std=_a			) for image in images]

													a__:       Dict											=[to_channel_dimension_format(_a      ,       _a			) for image in images]

													a__:       Dict											={"pixel_values": images}
													return BatchFeature(data=_a      ,       tensor_type=_a			)










import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__UpperCAmelCase						  =     10_00_00_00_00_00
__UpperCAmelCase						  =     1_00_00

__UpperCAmelCase  ,							__UpperCAmelCase						  =     os.path.split(__file__)
__UpperCAmelCase						  =     os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  int						):
							a__:       Any											=pa.Table.from_pydict({"col": [0] * chunk_size}						)
							a__:       Optional[Any]											=pa.concat_tables([table] * (num_examples // chunk_size)						)
							return datasets.Dataset(__magic_name__     , fingerprint="table_100B"						)


@dataclass
class 	lowerCamelCase__ :
						_lowerCAmelCase								= 42
						_lowerCAmelCase								= 42
						_lowerCAmelCase								= 42
						_lowerCAmelCase								= 42


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													a__:       Tuple											=np.random.default_rng(self.seed			)
													a__:       Tuple											=rng.integers(low=self.low      ,       high=self.high      ,       size=self.size			).tolist()


						def __iter__( self						:  Any			):
													return iter(self._sampled_values			)







						def __len__( self						:  Any			):
													return self.size





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       Union[str, Any]											=dataset[0]





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       Optional[Any]											=dataset[-1]





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       List[str]											=dataset[range(len(__magic_name__						) // 2     , len(__magic_name__						) // 2 + 1_024						)]





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       Optional[int]											=dataset[RandIter(0     , len(__magic_name__						)     , 1_024     , seed=42						)]





def 						__lowerCamelCase       (      ):
							a__:       Dict											={"num examples": SPEED_TEST_N_EXAMPLES}
							a__:       Union[str, Any]											=(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
							print("generating dataset"						)
							a__:       Dict											=generate_100B_dataset(num_examples=__magic_name__     , chunk_size=__magic_name__						)
							print("Functions"						)
							for func in functions:
														print(func.__name__						)
														a__:       Dict											=func(__magic_name__						)

							with open(__magic_name__     , "wb"						) as f:
														f.write(json.dumps(__magic_name__						).encode("utf-8"						)						)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()





