"""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   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     lowercase = False       ,     )      ->     Any:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	List[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	Tuple     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	str     				=      [Image.fromarray(lowercase) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





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

        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     torch.FloatTensor:





        '''simple docstring'''





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

        a__:	Any     				=      torch.from_numpy(images.transpose(0       ,     3       ,     1       ,     2))
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





        a__:	Tuple     				=      images.cpu().permute(0       ,     2       ,     3       ,     1).float().numpy()
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Any:





        '''simple docstring'''





        return 2.0 * images - 1.0



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





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



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     PIL.Image.Image:





        '''simple docstring'''





        if height is None:
          a__:	Union[str, Any]     				=      image.height
        if width is None:
          a__:	Optional[Any]     				=      image.width

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



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     torch.Tensor:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(lowercase       ,     lowercase):
          a__:	str     				=      [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:
            a__:	Any     				=      [self.convert_to_rgb(lowercase) for i in image]
          if self.config.do_resize:
            a__:	Any     				=      [self.resize(lowercase       ,     lowercase       ,     lowercase) for i in image]
          a__:	Any     				=      self.pil_to_numpy(lowercase)  # to np
          a__:	Tuple     				=      self.numpy_to_pt(lowercase)  # to pt

        elif isinstance(image[0]       ,     np.ndarray):
          a__:	Any     				=      np.concatenate(lowercase       ,     axis=0) if image[0].ndim == 4 else np.stack(lowercase       ,     axis=0)
          a__:	List[Any]     				=      self.numpy_to_pt(lowercase)
          a__				,       a__				,       a__				,       a__:	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):
          a__:	int     				=      torch.cat(lowercase       ,     axis=0) if image[0].ndim == 4 else torch.stack(lowercase       ,     axis=0)
          a__				,       a__				,       a__				,       a__:	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]
        a__:	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()}]'       ,     lowercase       ,     )
          a__:	Union[str, Any]     				=      False

        if do_normalize:
          a__:	Optional[Any]     				=      self.normalize(lowercase)

        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     Dict:





        '''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"]:
          a__:	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)
          a__:	int     				=      'np'

        if output_type == "latent":
          return image

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

        a__:	int     				=      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

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

        if output_type == "np":
          return image

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







class   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     )      ->     Union[str, Any]:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Tuple     				=      images[None, ...]
        a__:	Optional[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	int     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	Optional[Any]     				=      [Image.fromarray(image[:, :, :3]) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Optional[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      images[:, :, :, 3:]
        if images.shape[-1] == 6:
          a__:	List[str]     				=      (images_depth * 2_55).round().astype('uint8')
          a__:	Any     				=      [
              Image.fromarray(self.rgblike_to_depthmap(lowercase)       ,     mode='I;16') for image_depth in images_depth
          ]
        elif images.shape[-1] == 4:
          a__:	Dict     				=      (images_depth * 65535.0).astype(np.uintaa)
          a__:	Optional[Any]     				=      [Image.fromarray(lowercase       ,     mode='I;16') for image_depth in images_depth]
        else:
          raise Exception('Not supported')

        return pil_images



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     List[str]:





        '''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"]:
          a__:	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'       ,     lowercase       ,     standard_warn=lowercase)
          a__:	List[str]     				=      'np'

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

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

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

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







'''simple docstring'''



import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


_lowercase       :						int						=[nn.LayerNorm]

_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						List[Any]						=version.parse(version.parse(torch.__version__).base_version)

_lowercase       :						int						=parsed_torch_version_base >= version.parse("2.0")
_lowercase       :						Tuple						=parsed_torch_version_base >= version.parse("1.12")
_lowercase       :						int						=parsed_torch_version_base >= version.parse("1.11")
_lowercase       :						Dict						=parsed_torch_version_base < version.parse("1.11")


def 							__UpperCAmelCase				(				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:List[str]   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:Any   ,				self				:Optional[int]			)		->						Union[str, Any]:
				from torch import _softmax_backward_data

				if is_torch_less_than_1_11:
								return _softmax_backward_data(UpperCamelCase__   ,				UpperCamelCase__   ,				parent.dim   ,				self			)
				else:
								return _softmax_backward_data(UpperCamelCase__   ,				UpperCamelCase__   ,				parent.dim   ,				self.dtype			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:nn.Linear   ,				UpperCamelCase__				:torch.LongTensor   ,				UpperCamelCase__				:int = 0			)		->						nn.Linear:
				snake_case__      :   Optional[Any]      =    index.to(layer.weight.device			)
				snake_case__      :   int      =    layer.weight.index_select(UpperCamelCase__   ,				UpperCamelCase__			).clone().detach()
				if layer.bias is not None:
								if dim == 1:
												snake_case__      :   Dict      =    layer.bias.clone().detach()
								else:
												snake_case__      :   Optional[Any]      =    layer.bias[index].clone().detach()
				snake_case__      :   Union[str, Any]      =    list(layer.weight.size()			)
				snake_case__      :   int      =    len(UpperCamelCase__			)
				snake_case__      :   int      =    nn.Linear(new_size[1]   ,				new_size[0]   ,				bias=layer.bias is not None			).to(layer.weight.device			)
				snake_case__      :   Union[str, Any]      =    False
				new_layer.weight.copy_(W.contiguous()			)
				snake_case__      :   List[Any]      =    True
				if layer.bias is not None:
								snake_case__      :   Union[str, Any]      =    False
								new_layer.bias.copy_(b.contiguous()			)
								snake_case__      :   Dict      =    True
				return new_layer






class 			_SCREAMING_SNAKE_CASE				(nn.Module      ):



			def __init__(     self		:						int    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Optional[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__()
							snake_case__      :   str      =    nf
							snake_case__      :   Tuple      =    nn.Parameter(torch.empty(__UpperCamelCase    ,			__UpperCamelCase			)			)
							snake_case__      :   Optional[Any]      =    nn.Parameter(torch.zeros(__UpperCamelCase			)			)
							nn.init.normal_(self.weight    ,			std=0.02			)







			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						Optional[int]			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Dict      =    x.size()[:-1] + (self.nf,)
							snake_case__      :   List[str]      =    torch.addmm(self.bias    ,			x.view(-1    ,			x.size(-1			)			)    ,			self.weight			)
							snake_case__      :   Dict      =    x.view(__UpperCamelCase			)
							return x



def 							__UpperCAmelCase				(				UpperCamelCase__				:ConvaD   ,				UpperCamelCase__				:torch.LongTensor   ,				UpperCamelCase__				:int = 1			)		->						ConvaD:
				snake_case__      :   Optional[Any]      =    index.to(layer.weight.device			)
				snake_case__      :   List[Any]      =    layer.weight.index_select(UpperCamelCase__   ,				UpperCamelCase__			).clone().detach()
				if dim == 0:
								snake_case__      :   Any      =    layer.bias.clone().detach()
				else:
								snake_case__      :   Optional[Any]      =    layer.bias[index].clone().detach()
				snake_case__      :   Union[str, Any]      =    list(layer.weight.size()			)
				snake_case__      :   List[str]      =    len(UpperCamelCase__			)
				snake_case__      :   Optional[Any]      =    ConvaD(new_size[1]   ,				new_size[0]			).to(layer.weight.device			)
				snake_case__      :   List[str]      =    False
				new_layer.weight.copy_(W.contiguous()			)
				snake_case__      :   List[Any]      =    True
				snake_case__      :   List[str]      =    False
				new_layer.bias.copy_(b.contiguous()			)
				snake_case__      :   str      =    True
				return new_layer


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[nn.Linear, ConvaD]   ,				UpperCamelCase__				:torch.LongTensor   ,				UpperCamelCase__				:Optional[int] = None			)		->						Union[nn.Linear, ConvaD]:
				if isinstance(UpperCamelCase__   ,				nn.Linear			):
								return prune_linear_layer(UpperCamelCase__   ,				UpperCamelCase__   ,				dim=0 if dim is None else dim			)
				elif isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								return prune_convad_layer(UpperCamelCase__   ,				UpperCamelCase__   ,				dim=1 if dim is None else dim			)
				else:
								raise ValueError(F'''Can\'t prune layer of class {layer.__class__}'''			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Callable[..., torch.Tensor]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:int   ,				*UpperCamelCase__				:List[str]			)		->						torch.Tensor:
				assert len(UpperCamelCase__			) > 0, F'''{input_tensors} has to be a tuple/list of tensors'''

				# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
				snake_case__      :   str      =    len(inspect.signature(UpperCamelCase__			).parameters			)
				if num_args_in_forward_chunk_fn != len(UpperCamelCase__			):
								raise ValueError(
								    F'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase__			)} input '''
								    '''tensors are given'''			)

				if chunk_size > 0:
								snake_case__      :   Tuple      =    input_tensors[0].shape[chunk_dim]
								for input_tensor in input_tensors:
												if input_tensor.shape[chunk_dim] != tensor_shape:
																raise ValueError(
																    F'''All input tenors have to be of the same shape: {tensor_shape}, '''
																    F'''found shape {input_tensor.shape[chunk_dim]}'''			)

								if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
												raise ValueError(
												    F'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
												    F'''size {chunk_size}'''			)

								snake_case__      :   Optional[Any]      =    input_tensors[0].shape[chunk_dim] // chunk_size

								# chunk input tensor into tuples
								snake_case__      :   List[Any]      =    tuple(input_tensor.chunk(UpperCamelCase__   ,				dim=UpperCamelCase__			) for input_tensor in input_tensors			)
								# apply forward fn to every tuple
								snake_case__      :   List[Any]      =    tuple(forward_fn(*UpperCamelCase__			) for input_tensors_chunk in zip(*UpperCamelCase__			)			)
								# concatenate output at same dimension
								return torch.cat(UpperCamelCase__   ,				dim=UpperCamelCase__			)

				return forward_fn(*UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[int]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Set[int]			)		->						Tuple[Set[int], torch.LongTensor]:
				snake_case__      :   str      =    torch.ones(UpperCamelCase__   ,				UpperCamelCase__			)
				snake_case__      :   int      =    set(UpperCamelCase__			) - already_pruned_heads  # Convert to set and remove already pruned heads
				for head in heads:
								# Compute how many pruned heads are before the head and move the index accordingly
								snake_case__      :   str      =    head - sum(1 if h < head else 0 for h in already_pruned_heads			)
								snake_case__      :   Optional[int]      =    0
				snake_case__      :   Optional[Any]      =    mask.view(-1			).contiguous().eq(1			)
				snake_case__      :   torch.LongTensor      =    torch.arange(len(UpperCamelCase__			)			)[mask].long()
				return heads, index


def 							__UpperCAmelCase				(				*UpperCamelCase__				:Union[torch.Tensor, List[torch.Tensor]]   ,				UpperCamelCase__				:Optional[str] = None			)		->						Tuple[torch.Tensor, ...]:
				return torch.meshgrid(*UpperCamelCase__   ,				indexing=UpperCamelCase__			)



def 							__UpperCAmelCase				(				UpperCamelCase__				:torch.Tensor			)		->						Tuple[torch.device, int, int]:
				return tensor.device, storage_ptr(UpperCamelCase__			), storage_size(UpperCamelCase__			)




