
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     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = ['pixel_values']



	def __init__(    self						,  lowercase = True						,  lowercase = None						,  lowercase = PILImageResampling.BILINEAR						,  lowercase = True						,  lowercase = None						,  lowercase = True						,  lowercase = 1 / 255						,  lowercase = True						,  lowercase = None						,  lowercase = None						,  **lowercase						,  )				->       None:
					super().__init__(**lowercase )
					lowerCamelCase_						=			size if size is not None else {"shortest_edge": 256}
					lowerCamelCase_						=			get_size_dict(lowercase						,  default_to_square=lowercase )
					lowerCamelCase_						=			crop_size if crop_size is not None else {"height": 224, "width": 224}
					lowerCamelCase_						=			get_size_dict(lowercase )
					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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase = PILImageResampling.BICUBIC						,  lowercase = None						,  **lowercase						,  )				->       np.ndarray:
					lowerCamelCase_						=			get_size_dict(lowercase						,  default_to_square=lowercase )
					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(lowercase						,  size=size["shortest_edge"]						,  default_to_square=lowercase )
					return resize(lowercase						,  size=lowercase						,  resample=lowercase						,  data_format=lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase = None						,  **lowercase						,  )				->       np.ndarray:
					lowerCamelCase_						=			get_size_dict(lowercase )
					return center_crop(lowercase						,  size=(size["height"], size["width"])						,  data_format=lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase = None						,  **lowercase )				->       np.ndarray:
					return rescale(lowercase						,  scale=lowercase						,  data_format=lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase = None						,  **lowercase						,  )				->       np.ndarray:
					return normalize(lowercase						,  mean=lowercase						,  std=lowercase						,  data_format=lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = ChannelDimension.FIRST						,  **lowercase						,  )				->       Optional[Any]:
					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(lowercase						,  default_to_square=lowercase )
					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(lowercase )
					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(lowercase )

					if not valid_images(lowercase ):
									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(lowercase ) for image in images]

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

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

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

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

					lowerCamelCase_						=			[to_channel_dimension_format(lowercase						,  lowercase ) for image in images]

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




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__A				='''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
			raise ImportWarning(
			    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
			)

if version.parse(pyarrow.__version__).major < 8:
			raise ImportWarning(
			    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
			    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
			)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__A				=concatenate_datasets
__A				=DownloadConfig
__A				=DownloadManager
__A				=DownloadMode
__A				=DownloadConfig
__A				=DownloadMode
__A				=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


