




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 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					CONFIG_NAME

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







						'''simple docstring'''




						super().__init__()

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




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

						return pil_images

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						if not isinstance(__lowercase					,			__lowercase   ):
							lowerCamelCase_				:						Tuple				  =				[images]
						lowerCamelCase_				:						Union[str, Any]				  =				[np.array(__lowercase   ).astype(np.floataa   ) / 2_55.0 for image in images]
						lowerCamelCase_				:						List[Any]				  =				np.stack(__lowercase					,			axis=0   )

						return images

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						if images.ndim == 3:
							lowerCamelCase_				:						Tuple				  =				images[..., None]

						lowerCamelCase_				:						List[str]				  =				torch.from_numpy(images.transpose(0					,			3					,			1					,			2   )   )
						return images

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




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

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						return 2.0 * images - 1.0

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




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

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				image.convert('RGB'   )
						return image

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None					,			__lowercase = None					,			):







						'''simple docstring'''




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

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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None					,			__lowercase = None					,			):







						'''simple docstring'''




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

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

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

						if do_normalize:
							lowerCamelCase_				:						int				  =				self.normalize(__lowercase   )

						return image





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = "pil"					,			__lowercase = None					,			):







						'''simple docstring'''




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

						if output_type == "latent":
							return image

						if do_denormalize is None:
							lowerCamelCase_				:						Optional[int]				  =				[self.config.do_normalize] * image.shape[0]

						lowerCamelCase_				:						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

						lowerCamelCase_				:						Tuple				  =				self.pt_to_numpy(__lowercase   )

						if output_type == "np":
							return image

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


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					CONFIG_NAME

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







						'''simple docstring'''




						super().__init__()

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




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

						return pil_images

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						if images.ndim == 3:
							lowerCamelCase_				:						Union[str, Any]				  =				images[None, ...]
						lowerCamelCase_				:						Union[str, Any]				  =				images[:, :, :, 3:]
						if images.shape[-1] == 6:
							lowerCamelCase_				:						int				  =				(images_depth * 255).round().astype('uint8'   )
							lowerCamelCase_				:						int				  =				[
							    Image.fromarray(self.rgblike_to_depthmap(__lowercase   )					,			mode='I;16'   ) for image_depth in images_depth
							]
						elif images.shape[-1] == 4:
							lowerCamelCase_				:						List[Any]				  =				(images_depth * 6_55_35.0).astype(np.uintaa   )
							lowerCamelCase_				:						List[Any]				  =				[Image.fromarray(__lowercase					,			mode='I;16'   ) for image_depth in images_depth]
						else:
							raise Exception('Not supported'   )

						return pil_images





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = "pil"					,			__lowercase = None					,			):







						'''simple docstring'''




						if not isinstance(__lowercase					,			torch.Tensor   ):
							raise ValueError(
							    f"Input for postprocessing is in incorrect format: {type(__lowercase   )}. We only support pytorch tensor"   )
						if output_type not in ["latent", "pt", "np", "pil"]:
							lowerCamelCase_				:						List[str]				  =				(
							    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'					,			__lowercase					,			standard_warn=__lowercase   )
							lowerCamelCase_				:						List[Any]				  =				'np'

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

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

						lowerCamelCase_				:						Union[str, Any]				  =				self.pt_to_numpy(__lowercase   )

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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__						=		{
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['LayoutLMTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'LayoutLMForMaskedLM',
							    'LayoutLMForSequenceClassification',
							    'LayoutLMForTokenClassification',
							    'LayoutLMForQuestionAnswering',
							    'LayoutLMModel',
							    'LayoutLMPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFLayoutLMForMaskedLM',
							    'TFLayoutLMForSequenceClassification',
							    'TFLayoutLMForTokenClassification',
							    'TFLayoutLMForQuestionAnswering',
							    'TFLayoutLMMainLayer',
							    'TFLayoutLMModel',
							    'TFLayoutLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

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