
"""simple docstring"""

import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
class      lowerCamelCase__      (					A__     ):
			a__	: List[Any]        =       CONFIG_NAME


			@register_to_config
			def __init__(       self			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = 8			,	SCREAMING_SNAKE_CASE = "lanczos"			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = False			,	):



					"""simple docstring"""







					super().__init__()


			@staticmethod
			def 							lowerCamelCase_			(       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if images.ndim == 3:
							snake_case						:  Optional[Any]        =			images[None, ...]
					snake_case						:  Optional[int]        =			(images * 255).round().astype("uint8"							)
					if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							snake_case						:  int        =			[Image.fromarray(image.squeeze()			,	mode="L"							) for image in images]
					else:
							snake_case						:  Any        =			[Image.fromarray(snake_case_							) for image in images]

					return pil_images


			@staticmethod
			def 							lowerCamelCase_			(       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if not isinstance(snake_case_			,	snake_case_							):
							snake_case						:  int        =			[images]
					snake_case						:  Optional[int]        =			[np.array(snake_case_							).astype(np.floataa							) / 2_55.0 for image in images]
					snake_case						:  Union[str, Any]        =			np.stack(snake_case_			,	axis=0							)

					return images


			@staticmethod
			def 							lowerCamelCase_			(       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if images.ndim == 3:
							snake_case						:  Union[str, Any]        =			images[..., None]

					snake_case						:  Tuple        =			torch.from_numpy(images.transpose(0			,	3			,	1			,	2							)							)
					return images


			@staticmethod
			def 							lowerCamelCase_			(       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			images.cpu().permute(0			,	2			,	3			,	1							).float().numpy()
					return images


			@staticmethod
			def 							lowerCamelCase_			(       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return 2.0 * images - 1.0


			@staticmethod
			def 							lowerCamelCase_			(       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return (images / 2 + 0.5).clamp(0			,	1							)


			@staticmethod
			def 							lowerCamelCase_			(       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Dict        =			image.convert("RGB"							)
					return image


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	):



					"""simple docstring"""







					if height is None:
							snake_case						:  Optional[int]        =			image.height
					if width is None:
							snake_case						:  int        =			image.width

					snake_case     ,			snake_case						:  Union[str, Any]        =			(
					    x - x % self.config.vae_scale_factor for x in (width, height)
					)  # resize to integer multiple of vae_scale_factor
					snake_case						:  str        =			image.resize((width, height)			,	resample=PIL_INTERPOLATION[self.config.resample]							)
					return image


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			(PIL.Image.Image, np.ndarray, torch.Tensor)
					if isinstance(snake_case_			,	snake_case_							):
							snake_case						:  Union[str, Any]        =			[image]
					elif not (isinstance(snake_case_			,	snake_case_							) and all(isinstance(snake_case_			,	snake_case_							) for i in image							)):
							raise ValueError(
							    F'''Input is in incorrect format: {[type(snake_case_							) for i in image]}. Currently, we only support {', '.join(snake_case_							)}'''							)

					if isinstance(image[0]			,	PIL.Image.Image							):
							if self.config.do_convert_rgb:
									snake_case						:  List[str]        =			[self.convert_to_rgb(snake_case_							) for i in image]
							if self.config.do_resize:
									snake_case						:  Union[str, Any]        =			[self.resize(snake_case_			,	snake_case_			,	snake_case_							) for i in image]
							snake_case						:  Optional[Any]        =			self.pil_to_numpy(snake_case_							)  # to np
							snake_case						:  List[Any]        =			self.numpy_to_pt(snake_case_							)  # to pt

					elif isinstance(image[0]			,	np.ndarray							):
							snake_case						:  str        =			np.concatenate(snake_case_			,	axis=0							) if image[0].ndim == 4 else np.stack(snake_case_			,	axis=0							)
							snake_case						:  Dict        =			self.numpy_to_pt(snake_case_							)
							snake_case     ,			snake_case     ,			snake_case     ,			snake_case						:  Dict        =			image.shape
							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
									raise ValueError(
									    F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
									    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''							)

					elif isinstance(image[0]			,	torch.Tensor							):
							snake_case						:  Optional[int]        =			torch.cat(snake_case_			,	axis=0							) if image[0].ndim == 4 else torch.stack(snake_case_			,	axis=0							)
							snake_case     ,			snake_case     ,			snake_case     ,			snake_case						:  str        =			image.shape

							# don't need any preprocess if the image is latents
							if channel == 4:
									return image

							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
									raise ValueError(
									    F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
									    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''							)

        # expected range [0,1], normalize to [-1,1]
					snake_case						:  Optional[Any]        =			self.config.do_normalize
					if image.min() < 0:
							warnings.warn(
							    "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
							    F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''			,	snake_case_			,	)
							snake_case						:  Dict        =			False

					if do_normalize:
							snake_case						:  List[str]        =			self.normalize(snake_case_							)

					return image



			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = "pil"			,	SCREAMING_SNAKE_CASE = None			,	):



					"""simple docstring"""







					if not isinstance(snake_case_			,	torch.Tensor							):
							raise ValueError(
							    F'''Input for postprocessing is in incorrect format: {type(snake_case_							)}. We only support pytorch tensor'''							)
					if output_type not in ["latent", "pt", "np", "pil"]:
							snake_case						:  int        =			(
							    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
							    "`pil`, `np`, `pt`, `latent`"
							)
							deprecate("Unsupported output_type"			,	"1.0.0"			,	snake_case_			,	standard_warn=snake_case_							)
							snake_case						:  int        =			"np"

					if output_type == "latent":
							return image

					if do_denormalize is None:
							snake_case						:  Any        =			[self.config.do_normalize] * image.shape[0]

					snake_case						:  List[str]        =			torch.stack(
					    [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

					if output_type == "pt":
							return image

					snake_case						:  Dict        =			self.pt_to_numpy(snake_case_							)

					if output_type == "np":
							return image

					if output_type == "pil":
							return self.numpy_to_pil(snake_case_							)







class      lowerCamelCase__      (					A__     ):
			a__	: Union[str, Any]        =       CONFIG_NAME


			@register_to_config
			def __init__(       self			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = 8			,	SCREAMING_SNAKE_CASE = "lanczos"			,	SCREAMING_SNAKE_CASE = True			,	):



					"""simple docstring"""







					super().__init__()


			@staticmethod
			def 							lowerCamelCase_			(       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if images.ndim == 3:
							snake_case						:  str        =			images[None, ...]
					snake_case						:  str        =			(images * 255).round().astype("uint8"							)
					if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							snake_case						:  Union[str, Any]        =			[Image.fromarray(image.squeeze()			,	mode="L"							) for image in images]
					else:
							snake_case						:  Union[str, Any]        =			[Image.fromarray(image[:, :, :3]							) for image in images]

					return pil_images


			@staticmethod
			def 							lowerCamelCase_			(       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return image[:, :, 1] * 2**8 + image[:, :, 2]


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if images.ndim == 3:
							snake_case						:  str        =			images[None, ...]
					snake_case						:  Optional[int]        =			images[:, :, :, 3:]
					if images.shape[-1] == 6:
							snake_case						:  str        =			(images_depth * 255).round().astype("uint8"							)
							snake_case						:  List[Any]        =			[
							    Image.fromarray(self.rgblike_to_depthmap(snake_case_							)			,	mode="I;16"							) for image_depth in images_depth
							]
					elif images.shape[-1] == 4:
							snake_case						:  Dict        =			(images_depth * 65_535.0).astype(np.uintaa							)
							snake_case						:  List[str]        =			[Image.fromarray(snake_case_			,	mode="I;16"							) for image_depth in images_depth]
					else:
							raise Exception("Not supported"							)

					return pil_images



			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = "pil"			,	SCREAMING_SNAKE_CASE = None			,	):



					"""simple docstring"""







					if not isinstance(snake_case_			,	torch.Tensor							):
							raise ValueError(
							    F'''Input for postprocessing is in incorrect format: {type(snake_case_							)}. We only support pytorch tensor'''							)
					if output_type not in ["latent", "pt", "np", "pil"]:
							snake_case						:  Optional[Any]        =			(
							    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
							    "`pil`, `np`, `pt`, `latent`"
							)
							deprecate("Unsupported output_type"			,	"1.0.0"			,	snake_case_			,	standard_warn=snake_case_							)
							snake_case						:  Union[str, Any]        =			"np"

					if do_denormalize is None:
							snake_case						:  Tuple        =			[self.config.do_normalize] * image.shape[0]

					snake_case						:  str        =			torch.stack(
					    [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

					snake_case						:  Dict        =			self.pt_to_numpy(snake_case_							)

					if output_type == "np":
							if image.shape[-1] == 6:
									snake_case						:  str        =			np.stack([self.rgblike_to_depthmap(im[:, :, 3:]							) for im in image]			,	axis=0							)
							else:
									snake_case						:  Tuple        =			image[:, :, :, 3:]
							return image[:, :, :, :3], image_depth

					if output_type == "pil":
							return self.numpy_to_pil(snake_case_							), self.numpy_to_depth(snake_case_							)
					else:
							raise Exception(F'''This type {output_type} is not supported'''							)







"""simple docstring"""

from typing import TYPE_CHECKING

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


__A							=     {
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     ["FlavaFeatureExtractor"]
							__A							=     ["FlavaImageProcessor"]
							__A							=     ["FlavaProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "FlavaForPreTraining",
							    "FlavaImageCodebook",
							    "FlavaImageModel",
							    "FlavaModel",
							    "FlavaMultimodalModel",
							    "FlavaPreTrainedModel",
							    "FlavaTextModel",
							]

if TYPE_CHECKING:
							from .configuration_flava import (
							    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    FlavaConfig,
							    FlavaImageCodebookConfig,
							    FlavaImageConfig,
							    FlavaMultimodalConfig,
							    FlavaTextConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_flava import FlavaFeatureExtractor
														from .image_processing_flava import FlavaImageProcessor
														from .processing_flava import FlavaProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flava import (
														    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlavaForPreTraining,
														    FlavaImageCodebook,
														    FlavaImageModel,
														    FlavaModel,
														    FlavaMultimodalModel,
														    FlavaPreTrainedModel,
														    FlavaTextModel,
														)

else:
							import sys

							__A							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

