



'''simple docstring'''

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCAmelCase				: Union[str, Any]							    =				logging.get_logger(__name__)


if is_vision_available():
	import PIL


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		Optional[int]	=	['pixel_values']




				def __init__(self       ,			__lowercase = True       ,			__lowercase = None       ,			__lowercase = PILImageResampling.BICUBIC       ,			__lowercase = True       ,			__lowercase = None       ,			__lowercase = True       ,			__lowercase = 1 / 2_55       ,			__lowercase = True       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = True       ,			**__lowercase       ,			):
										super().__init__(**__lowercase		)
										__lowerCAmelCase							       =				size if size is not None else {'''shortest_edge''': 2_24}
										__lowerCAmelCase							       =				get_size_dict(__lowercase       ,			default_to_square=__lowercase		)
										__lowerCAmelCase							       =				crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
										__lowerCAmelCase							       =				get_size_dict(__lowercase       ,			default_to_square=__lowercase       ,			param_name='''crop_size'''		)

										__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 OPENAI_CLIP_MEAN
										__lowerCAmelCase							       =				image_std if image_std is not None else OPENAI_CLIP_STD
										__lowerCAmelCase							       =				do_convert_rgb




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = PILImageResampling.BICUBIC       ,			__lowercase = None       ,			**__lowercase       ,			):
										__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 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				get_size_dict(__lowercase		)
										if "height" not in size or "width" not in size:
																raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
										return center_crop(__lowercase       ,			size=(size['''height'''], size['''width'''])       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase       ,			):
										return rescale(__lowercase       ,			scale=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase       ,			):
										return normalize(__lowercase       ,			mean=__lowercase       ,			std=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_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 = None       ,			__lowercase = ChannelDimension.FIRST       ,			**__lowercase       ,			):
										__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       ,			param_name='''size'''       ,			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       ,			param_name='''crop_size'''       ,			default_to_square=__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							       =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										__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.'''		)

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																__lowerCAmelCase							       =				[convert_to_rgb(__lowercase		) for image in images]

										# 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		)





'''simple docstring'''

import sys

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				: Tuple							    =				"""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
	pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
	pkgs_to_check_at_runtime.append("""importlib_metadata""")

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

		require_version_core(deps[pkg])
	else:
		raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=None):
						require_version(deps[pkg],				lowerCamelCase)

