
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       a_   ( _a						):
   a  : List[Any]				      =       CONFIG_NAME






   @register_to_config
   def __init__(			self							, __UpperCamelCase = True							, __UpperCamelCase = 8							, __UpperCamelCase = "lanczos"							, __UpperCamelCase = True							, __UpperCamelCase = False							, ):
         super().__init__()






   @staticmethod
   def       UpperCamelCase_							(			__UpperCamelCase     ):
         if images.ndim == 3:
               _lowercase									=				images[None, ...]
         _lowercase									=				(images * 255).round().astype("""uint8"""     )
         if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               _lowercase									=				[Image.fromarray(image.squeeze()							, mode="""L"""     ) for image in images]
         else:
               _lowercase									=				[Image.fromarray(__UpperCamelCase     ) for image in images]

         return pil_images






   @staticmethod
   def       UpperCamelCase_							(			__UpperCamelCase     ):
         if not isinstance(__UpperCamelCase							, __UpperCamelCase     ):
               _lowercase									=				[images]
         _lowercase									=				[np.array(__UpperCamelCase     ).astype(np.floataa     ) / 255.0 for image in images]
         _lowercase									=				np.stack(__UpperCamelCase							, axis=0     )

         return images






   @staticmethod
   def       UpperCamelCase_							(			__UpperCamelCase     ):
         if images.ndim == 3:
               _lowercase									=				images[..., None]

         _lowercase									=				torch.from_numpy(images.transpose(0							, 3							, 1							, 2     )     )
         return images






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






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






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






   @staticmethod
   def       UpperCamelCase_							(			__UpperCamelCase     ):
         _lowercase									=				image.convert("""RGB"""     )
         return image






   def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase = None							, __UpperCamelCase = None							, ):
         if height is None:
               _lowercase									=				image.height
         if width is None:
               _lowercase									=				image.width

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






   def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase = None							, __UpperCamelCase = None							, ):
         _lowercase									=				(PIL.Image.Image, np.ndarray, torch.Tensor)
         if isinstance(__UpperCamelCase							, __UpperCamelCase     ):
               _lowercase									=				[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:
                     _lowercase									=				[self.convert_to_rgb(__UpperCamelCase     ) for i in image]
               if self.config.do_resize:
                     _lowercase									=				[self.resize(__UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase     ) for i in image]
               _lowercase									=				self.pil_to_numpy(__UpperCamelCase     )  # to np
               _lowercase									=				self.numpy_to_pt(__UpperCamelCase     )  # to pt

         elif isinstance(image[0]							, np.ndarray     ):
               _lowercase									=				np.concatenate(__UpperCamelCase							, axis=0     ) if image[0].ndim == 4 else np.stack(__UpperCamelCase							, axis=0     )
               _lowercase									=				self.numpy_to_pt(__UpperCamelCase     )
               _lowercase					,	_lowercase					,	_lowercase					,	_lowercase									=				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     ):
               _lowercase									=				torch.cat(__UpperCamelCase							, axis=0     ) if image[0].ndim == 4 else torch.stack(__UpperCamelCase							, axis=0     )
               _lowercase					,	_lowercase					,	_lowercase					,	_lowercase									=				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]
         _lowercase									=				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							, )
               _lowercase									=				False

         if do_normalize:
               _lowercase									=				self.normalize(__UpperCamelCase     )

         return image






   def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase = "pil"							, __UpperCamelCase = None							, ):
         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"]:
               _lowercase									=				(
                   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     )
               _lowercase									=				"""np"""

         if output_type == "latent":
               return image

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

         _lowercase									=				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

         _lowercase									=				self.pt_to_numpy(__UpperCamelCase     )

         if output_type == "np":
               return image

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







class       a_   ( _a						):
   a  : List[Any]				      =       CONFIG_NAME






   @register_to_config
   def __init__(			self							, __UpperCamelCase = True							, __UpperCamelCase = 8							, __UpperCamelCase = "lanczos"							, __UpperCamelCase = True							, ):
         super().__init__()






   @staticmethod
   def       UpperCamelCase_							(			__UpperCamelCase     ):
         if images.ndim == 3:
               _lowercase									=				images[None, ...]
         _lowercase									=				(images * 255).round().astype("""uint8"""     )
         if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               _lowercase									=				[Image.fromarray(image.squeeze()							, mode="""L"""     ) for image in images]
         else:
               _lowercase									=				[Image.fromarray(image[:, :, :3]     ) for image in images]

         return pil_images






   @staticmethod
   def       UpperCamelCase_							(			__UpperCamelCase     ):
         return image[:, :, 1] * 2**8 + image[:, :, 2]






   def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
         if images.ndim == 3:
               _lowercase									=				images[None, ...]
         _lowercase									=				images[:, :, :, 3:]
         if images.shape[-1] == 6:
               _lowercase									=				(images_depth * 255).round().astype("""uint8"""     )
               _lowercase									=				[
                   Image.fromarray(self.rgblike_to_depthmap(__UpperCamelCase     )							, mode="""I;16"""     ) for image_depth in images_depth
               ]
         elif images.shape[-1] == 4:
               _lowercase									=				(images_depth * 65_535.0).astype(np.uintaa     )
               _lowercase									=				[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							, ):
         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"]:
               _lowercase									=				(
                   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     )
               _lowercase									=				"""np"""

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

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

         _lowercase									=				self.pt_to_numpy(__UpperCamelCase     )

         if output_type == "np":
               if image.shape[-1] == 6:
                     _lowercase									=				np.stack([self.rgblike_to_depthmap(im[:, :, 3:]     ) for im in image]							, axis=0     )
               else:
                     _lowercase									=				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, is_vision_available


A		:   int         =    {'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   int         =    ['''ViltFeatureExtractor''']
		A		:   List[Any]         =    ['''ViltImageProcessor''']
		A		:   Dict         =    ['''ViltProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Tuple         =    [
		    '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViltForImageAndTextRetrieval''',
		    '''ViltForImagesAndTextClassification''',
		    '''ViltForTokenClassification''',
		    '''ViltForMaskedLM''',
		    '''ViltForQuestionAnswering''',
		    '''ViltLayer''',
		    '''ViltModel''',
		    '''ViltPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		A		:   Dict         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)