


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      snake_case__	(   _A	):
				__lowerCAmelCase		=   CONFIG_NAME
				@register_to_config
				def __init__(	self  ,   _UpperCAmelCase = True  ,   _UpperCAmelCase = 8  ,   _UpperCAmelCase = "lanczos"  ,   _UpperCAmelCase = True  ,   _UpperCAmelCase = False  ,   ):




										"""simple docstring"""




										super().__init__()
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




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

										return pil_images
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




										if not isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																__magic_name__					:				Optional[int]				=   [images]
										__magic_name__					:				Optional[Any]				=   [np.array(_UpperCAmelCase   ).astype(np.floataa   ) / 255.0 for image in images]
										__magic_name__					:				Union[str, Any]				=   np.stack(_UpperCAmelCase  ,   axis=0   )

										return images
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




										if images.ndim == 3:
																__magic_name__					:				Any				=   images[..., None]

										__magic_name__					:				Dict				=   torch.from_numpy(images.transpose(0  ,   3  ,   1  ,   2   )   )
										return images
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   images.cpu().permute(0  ,   2  ,   3  ,   1   ).float().numpy()
										return images
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




										return 2.0 * images - 1.0
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




										return (images / 2 + 0.5).clamp(0  ,   1   )
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   image.convert('RGB'   )
										return image
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




										if height is None:
																__magic_name__					:				Dict				=   image.height
										if width is None:
																__magic_name__					:				Any				=   image.width

										__magic_name__		,			__magic_name__					:				int				=   (
										    x - x % self.config.vae_scale_factor for x in (width, height)
										)  # resize to integer multiple of vae_scale_factor
										__magic_name__					:				Dict				=   image.resize((width, height)  ,   resample=PIL_INTERPOLATION[self.config.resample]   )
										return image
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




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

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

										elif isinstance(image[0]  ,   np.ndarray   ):
																__magic_name__					:				Optional[Any]				=   np.concatenate(_UpperCAmelCase  ,   axis=0   ) if image[0].ndim == 4 else np.stack(_UpperCAmelCase  ,   axis=0   )
																__magic_name__					:				int				=   self.numpy_to_pt(_UpperCAmelCase   )
																__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__					:				Tuple				=   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   ):
																__magic_name__					:				List[Any]				=   torch.cat(_UpperCAmelCase  ,   axis=0   ) if image[0].ndim == 4 else torch.stack(_UpperCAmelCase  ,   axis=0   )
																__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__					:				Tuple				=   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]
										__magic_name__					:				Tuple				=   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()}]"  ,   _UpperCAmelCase  ,   )
																__magic_name__					:				List[str]				=   False

										if do_normalize:
																__magic_name__					:				Any				=   self.normalize(_UpperCAmelCase   )

										return image





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = "pil"  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




										if not isinstance(_UpperCAmelCase  ,   torch.Tensor   ):
																raise ValueError(
																    F"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase   )}. We only support pytorch tensor"   )
										if output_type not in ["latent", "pt", "np", "pil"]:
																__magic_name__					:				Union[str, 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'  ,   _UpperCAmelCase  ,   standard_warn=_UpperCAmelCase   )
																__magic_name__					:				Tuple				=   'np'

										if output_type == "latent":
																return image

										if do_denormalize is None:
																__magic_name__					:				Union[str, Any]				=   [self.config.do_normalize] * image.shape[0]

										__magic_name__					:				Tuple				=   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

										__magic_name__					:				Any				=   self.pt_to_numpy(_UpperCAmelCase   )

										if output_type == "np":
																return image

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






class      snake_case__	(   _A	):
				__lowerCAmelCase		=   CONFIG_NAME
				@register_to_config
				def __init__(	self  ,   _UpperCAmelCase = True  ,   _UpperCAmelCase = 8  ,   _UpperCAmelCase = "lanczos"  ,   _UpperCAmelCase = True  ,   ):




										"""simple docstring"""




										super().__init__()
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




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

										return pil_images
				@staticmethod
				def 			UpperCamelCase_    (	_UpperCAmelCase   ):




										"""simple docstring"""




										return image[:, :, 1] * 2**8 + image[:, :, 2]
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										if images.ndim == 3:
																__magic_name__					:				Optional[Any]				=   images[None, ...]
										__magic_name__					:				Union[str, Any]				=   images[:, :, :, 3:]
										if images.shape[-1] == 6:
																__magic_name__					:				int				=   (images_depth * 255).round().astype('uint8'   )
																__magic_name__					:				Union[str, Any]				=   [
																    Image.fromarray(self.rgblike_to_depthmap(_UpperCAmelCase   )  ,   mode='I;16'   ) for image_depth in images_depth
																]
										elif images.shape[-1] == 4:
																__magic_name__					:				Any				=   (images_depth * 65535.0).astype(np.uintaa   )
																__magic_name__					:				Union[str, Any]				=   [Image.fromarray(_UpperCAmelCase  ,   mode='I;16'   ) for image_depth in images_depth]
										else:
																raise Exception('Not supported'   )

										return pil_images





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = "pil"  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




										if not isinstance(_UpperCAmelCase  ,   torch.Tensor   ):
																raise ValueError(
																    F"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase   )}. We only support pytorch tensor"   )
										if output_type not in ["latent", "pt", "np", "pil"]:
																__magic_name__					:				Union[str, 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'  ,   _UpperCAmelCase  ,   standard_warn=_UpperCAmelCase   )
																__magic_name__					:				Optional[int]				=   'np'

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

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

										__magic_name__					:				List[str]				=   self.pt_to_numpy(_UpperCAmelCase   )

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

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__  				=			{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'QDQBertForMaskedLM',
			    'QDQBertForMultipleChoice',
			    'QDQBertForNextSentencePrediction',
			    'QDQBertForQuestionAnswering',
			    'QDQBertForSequenceClassification',
			    'QDQBertForTokenClassification',
			    'QDQBertLayer',
			    'QDQBertLMHeadModel',
			    'QDQBertModel',
			    'QDQBertPreTrainedModel',
			    'load_tf_weights_in_qdqbert',
			]


if TYPE_CHECKING:
			from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_qdqbert import (
						    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    QDQBertForMaskedLM,
						    QDQBertForMultipleChoice,
						    QDQBertForNextSentencePrediction,
						    QDQBertForQuestionAnswering,
						    QDQBertForSequenceClassification,
						    QDQBertForTokenClassification,
						    QDQBertLayer,
						    QDQBertLMHeadModel,
						    QDQBertModel,
						    QDQBertPreTrainedModel,
						    load_tf_weights_in_qdqbert,
						)


else:
			import sys

			snake_case__  				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


