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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


SCREAMING_SNAKE_CASE		:     Dict         =	logging.get_logger(__name__)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple			):
						if isinstance(_SCREAMING_SNAKE_CASE		, (list, tuple)			) and isinstance(videos[0]		, (list, tuple)			) and is_valid_image(videos[0][0]			):
												return videos

						elif isinstance(_SCREAMING_SNAKE_CASE		, (list, tuple)			) and is_valid_image(videos[0]			):
												return [videos]

						elif is_valid_image(_SCREAMING_SNAKE_CASE			):
												return [[videos]]

						raise ValueError(f'''Could not make batched video from {videos}'''			)







class        UpperCamelCase						(				__a				):
						a__		:str      =			['''pixel_values''']





						def __init__(self			,     __UpperCamelCase = True			,     __UpperCamelCase = None			,     __UpperCamelCase = PILImageResampling.BILINEAR			,     __UpperCamelCase = True			,     __UpperCamelCase = None			,     __UpperCamelCase = True			,     __UpperCamelCase = 1 / 255			,     __UpperCamelCase = True			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				None:
												super().__init__(**__UpperCamelCase						)
												UpperCamelCase_		:							Optional[int]  =		size if size is not None else {"""shortest_edge""": 224}
												UpperCamelCase_		:							List[str]  =		get_size_dict(__UpperCamelCase			,     default_to_square=__UpperCamelCase						)
												UpperCamelCase_		:							Optional[int]  =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
												UpperCamelCase_		:							int  =		get_size_dict(__UpperCamelCase			,     param_name="""crop_size"""						)

												UpperCamelCase_		:							List[Any]  =		do_resize
												UpperCamelCase_		:							str  =		size
												UpperCamelCase_		:							Tuple  =		do_center_crop
												UpperCamelCase_		:							int  =		crop_size
												UpperCamelCase_		:							List[str]  =		resample
												UpperCamelCase_		:							Dict  =		do_rescale
												UpperCamelCase_		:							List[str]  =		rescale_factor
												UpperCamelCase_		:							List[str]  =		do_normalize
												UpperCamelCase_		:							int  =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												UpperCamelCase_		:							List[str]  =		image_std if image_std is not None else IMAGENET_STANDARD_STD





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = PILImageResampling.BILINEAR			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				np.ndarray:
												UpperCamelCase_		:							Union[str, Any]  =		get_size_dict(__UpperCamelCase			,     default_to_square=__UpperCamelCase						)
												if "shortest_edge" in size:
																		UpperCamelCase_		:							List[Any]  =		get_resize_output_image_size(__UpperCamelCase			,     size["""shortest_edge"""]			,     default_to_square=__UpperCamelCase						)
												elif "height" in size and "width" in size:
																		UpperCamelCase_		:							Tuple  =		(size["""height"""], size["""width"""])
												else:
																		raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''						)
												return resize(__UpperCamelCase			,     size=__UpperCamelCase			,     resample=__UpperCamelCase			,     data_format=__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				np.ndarray:
												UpperCamelCase_		:							Any  =		get_size_dict(__UpperCamelCase						)
												if "height" not in size or "width" not in size:
																		raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''						)
												return center_crop(__UpperCamelCase			,     size=(size["""height"""], size["""width"""])			,     data_format=__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				int:
												return rescale(__UpperCamelCase			,     scale=__UpperCamelCase			,     data_format=__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				np.ndarray:
												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 = ChannelDimension.FIRST			,     )				->				np.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.
												UpperCamelCase_		:							Dict  =		to_numpy_array(__UpperCamelCase						)

												if do_resize:
																		UpperCamelCase_		:							Dict  =		self.resize(image=__UpperCamelCase			,     size=__UpperCamelCase			,     resample=__UpperCamelCase						)

												if do_center_crop:
																		UpperCamelCase_		:							List[Any]  =		self.center_crop(__UpperCamelCase			,     size=__UpperCamelCase						)

												if do_rescale:
																		UpperCamelCase_		:							str  =		self.rescale(image=__UpperCamelCase			,     scale=__UpperCamelCase						)

												if do_normalize:
																		UpperCamelCase_		:							str  =		self.normalize(image=__UpperCamelCase			,     mean=__UpperCamelCase			,     std=__UpperCamelCase						)

												UpperCamelCase_		:							List[Any]  =		to_channel_dimension_format(__UpperCamelCase			,     __UpperCamelCase						)
												return image





						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			,     )				->				PIL.Image.Image:
												UpperCamelCase_		:							str  =		do_resize if do_resize is not None else self.do_resize
												UpperCamelCase_		:							int  =		resample if resample is not None else self.resample
												UpperCamelCase_		:							Any  =		do_center_crop if do_center_crop is not None else self.do_center_crop
												UpperCamelCase_		:							Dict  =		do_rescale if do_rescale is not None else self.do_rescale
												UpperCamelCase_		:							Union[str, Any]  =		rescale_factor if rescale_factor is not None else self.rescale_factor
												UpperCamelCase_		:							List[Any]  =		do_normalize if do_normalize is not None else self.do_normalize
												UpperCamelCase_		:							Union[str, Any]  =		image_mean if image_mean is not None else self.image_mean
												UpperCamelCase_		:							Optional[int]  =		image_std if image_std is not None else self.image_std

												UpperCamelCase_		:							Optional[int]  =		size if size is not None else self.size
												UpperCamelCase_		:							Any  =		get_size_dict(__UpperCamelCase			,     default_to_square=__UpperCamelCase						)
												UpperCamelCase_		:							str  =		crop_size if crop_size is not None else self.crop_size
												UpperCamelCase_		:							Any  =		get_size_dict(__UpperCamelCase			,     param_name="""crop_size"""						)

												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."""						)

												UpperCamelCase_		:							Any  =		make_batched(__UpperCamelCase						)

												UpperCamelCase_		:							Any  =		[
												    [
												        self._preprocess_image(
												            image=__UpperCamelCase			,     do_resize=__UpperCamelCase			,     size=__UpperCamelCase			,     resample=__UpperCamelCase			,     do_center_crop=__UpperCamelCase			,     crop_size=__UpperCamelCase			,     do_rescale=__UpperCamelCase			,     rescale_factor=__UpperCamelCase			,     do_normalize=__UpperCamelCase			,     image_mean=__UpperCamelCase			,     image_std=__UpperCamelCase			,     data_format=__UpperCamelCase			,     )
												        for img in video
												    ]
												    for video in videos
												]

												UpperCamelCase_		:							Any  =		{"""pixel_values""": videos}
												return BatchFeature(data=__UpperCamelCase			,     tensor_type=__UpperCamelCase						)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






