



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, 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


UpperCAmelCase_							:      Optional[int]      =  logging.get_logger(__name__)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Optional[Any]				=	["""pixel_values"""]



	def __init__(       self			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = PIL.Image.BICUBIC			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = 1 / 255			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							super().__init__(**UpperCAmelCase__		)
							A__							=       size if size is not None else {"height": 256, "width": 256}
							A__							=       get_size_dict(UpperCAmelCase__		)
							A__							=       crop_size if crop_size is not None else {"height": 224, "width": 224}
							A__							=       get_size_dict(UpperCAmelCase__			,	param_name="crop_size"		)

							A__							=       do_resize
							A__							=       size
							A__							=       resample
							A__							=       do_center_crop
							A__							=       crop_size
							A__							=       do_rescale
							A__							=       rescale_factor
							A__							=       do_normalize
							A__							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A__							=       image_std if image_std is not None else IMAGENET_STANDARD_STD



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = PIL.Image.BICUBIC			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							A__							=       get_size_dict(UpperCAmelCase__		)
							if "height" not in size or "width" not in size:
													raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""		)
							return resize(
							    UpperCAmelCase__			,	size=(size["height"], size["width"])			,	resample=UpperCAmelCase__			,	data_format=UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							A__							=       get_size_dict(UpperCAmelCase__		)
							if "height" not in size or "width" not in size:
													raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""		)
							return center_crop(UpperCAmelCase__			,	size=(size["height"], size["width"])			,	data_format=UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							return rescale(UpperCAmelCase__			,	scale=UpperCAmelCase__			,	data_format=UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							return normalize(UpperCAmelCase__			,	mean=UpperCAmelCase__			,	std=UpperCAmelCase__			,	data_format=UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       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__			,	):
							A__							=       do_resize if do_resize is not None else self.do_resize
							A__							=       resample if resample is not None else self.resample
							A__							=       do_center_crop if do_center_crop is not None else self.do_center_crop
							A__							=       do_rescale if do_rescale is not None else self.do_rescale
							A__							=       rescale_factor if rescale_factor is not None else self.rescale_factor
							A__							=       do_normalize if do_normalize is not None else self.do_normalize
							A__							=       image_mean if image_mean is not None else self.image_mean
							A__							=       image_std if image_std is not None else self.image_std

							A__							=       size if size is not None else self.size
							A__							=       get_size_dict(UpperCAmelCase__		)
							A__							=       crop_size if crop_size is not None else self.crop_size
							A__							=       get_size_dict(UpperCAmelCase__			,	param_name="crop_size"		)

							A__							=       make_list_of_images(UpperCAmelCase__		)

							if not valid_images(UpperCAmelCase__		):
													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_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.
							A__							=       [to_numpy_array(UpperCAmelCase__		) for image in images]

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

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

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

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

							A__							=       [to_channel_dimension_format(UpperCAmelCase__			,	UpperCAmelCase__		) for image in images]

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










from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCAmelCase_							:      List[Any]      =  [
    "python",
    "tqdm",
    "regex",
    "requests",
    "packaging",
    "filelock",
    "numpy",
    "tokenizers",
    "huggingface-hub",
    "safetensors",
    "accelerate",
    "pyyaml",
]

for pkg in pkgs_to_check_at_runtime:
						if pkg in deps:
												if pkg == "tokenizers":
																		# must be loaded here, or else tqdm check may fail
																		from .utils import is_tokenizers_available

																		if not is_tokenizers_available():
																								continue  # not required, check version only if installed
												elif pkg == "accelerate":
																		# must be loaded here, or else tqdm check may fail
																		from .utils import is_accelerate_available

																		# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
																		# Transformers with PyTorch
																		if not is_accelerate_available():
																								continue  # not required, check version only if installed

												require_version_core(deps[pkg])
						else:
												raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				int=None    )->       Optional[int]:





						"""simple docstring"""





						require_version(deps[pkg]			,							_A    )





