
'''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


UpperCAmelCase            = [nn.LayerNorm]

UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = version.parse(version.parse(torch.__version__).base_version)

UpperCAmelCase            = parsed_torch_version_base >= version.parse("2.0")
UpperCAmelCase            = parsed_torch_version_base >= version.parse("1.12")
UpperCAmelCase            = parsed_torch_version_base >= version.parse("1.11")
UpperCAmelCase            = parsed_torch_version_base < version.parse("1.11")

def   A			(							A_				:       Union[str, Any]     ,				A_				:       int     ,				A_				:       Optional[int]     ,				A_				:       Any     ,				self				:       Optional[Any]							):
						from torch import _softmax_backward_data

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

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

class     a (					nn.Module     ):




				def __init__(				self						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[Any]   ):
										super().__init__()
										snake_case							:  Union[str, Any]			=     nf
										snake_case							:  Union[str, Any]			=     nn.Parameter(torch.empty(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  List[Any]			=     nn.Parameter(torch.zeros(SCREAMING_SNAKE_CASE_   )   )
										nn.init.normal_(self.weight,     std=0.02   )




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										snake_case							:  Dict			=     x.size()[:-1] + (self.nf,)
										snake_case							:  Any			=     torch.addmm(self.bias,     x.view(-1,     x.size(-1   )   ),     self.weight   )
										snake_case							:  int			=     x.view(SCREAMING_SNAKE_CASE_   )
										return x




def   A			(							A_				:       ConvaD     ,				A_				:       torch.LongTensor     ,				A_				:       int = 1							):
						snake_case							:  int			=     index.to(layer.weight.device							)
						snake_case							:  List[str]			=     layer.weight.index_select(A_     ,				A_							).clone().detach()
						if dim == 0:
												snake_case							:  Any			=     layer.bias.clone().detach()
						else:
												snake_case							:  int			=     layer.bias[index].clone().detach()
						snake_case							:  Optional[Any]			=     list(layer.weight.size()							)
						snake_case							:  Tuple			=     len(A_							)
						snake_case							:  List[str]			=     ConvaD(new_size[1]     ,				new_size[0]							).to(layer.weight.device							)
						snake_case							:  Optional[Any]			=     False
						new_layer.weight.copy_(W.contiguous()							)
						snake_case							:  Dict			=     True
						snake_case							:  str			=     False
						new_layer.bias.copy_(b.contiguous()							)
						snake_case							:  Any			=     True
						return new_layer

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

def   A			(							A_				:       Callable[..., torch.Tensor]     ,				A_				:       int     ,				A_				:       int     ,				*A_				:       Optional[int]							):
						assert len(A_							) > 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							:  Optional[Any]			=     len(inspect.signature(A_							).parameters							)
						if num_args_in_forward_chunk_fn != len(A_							):
												raise ValueError(
												    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(A_							)} input """
												    '''tensors are given'''							)

						if chunk_size > 0:
												snake_case							:  Union[str, Any]			=     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[int]			=     input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												snake_case							:  str			=     tuple(input_tensor.chunk(A_     ,				dim=A_							) for input_tensor in input_tensors							)
												# apply forward fn to every tuple
												snake_case							:  int			=     tuple(forward_fn(*A_							) for input_tensors_chunk in zip(*A_							)							)
												# concatenate output at same dimension
												return torch.cat(A_     ,				dim=A_							)

						return forward_fn(*A_							)

def   A			(							A_				:       List[int]     ,				A_				:       int     ,				A_				:       int     ,				A_				:       Set[int]							):
						snake_case							:  Any			=     torch.ones(A_     ,				A_							)
						snake_case							:  Optional[Any]			=     set(A_							) - 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							:  List[str]			=     head - sum(1 if h < head else 0 for h in already_pruned_heads							)
												snake_case							:  Union[str, Any]			=     0
						snake_case							:  Tuple			=     mask.view(-1							).contiguous().eq(1							)
						snake_case							:  torch.LongTensor			=     torch.arange(len(A_							)							)[mask].long()
						return heads, index

def   A			(							*A_				:       Union[torch.Tensor, List[torch.Tensor]]     ,				A_				:       Optional[str] = None							):
						return torch.meshgrid(*A_     ,				indexing=A_							)





def   A			(							A_				:       torch.Tensor							):
						return tensor.device, storage_ptr(A_							), storage_size(A_							)








"""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 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Union[str, Any]             =  CONFIG_NAME

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

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			int									=    images[None, ...]
        A_    :			Union[str, Any]									=    (images * 2_5_5).round().astype('uint8'    )
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          A_    :			List[str]									=    [Image.fromarray(image.squeeze()   , mode='L'    ) for image in images]
        else:
          A_    :			Tuple									=    [Image.fromarray(snake_case_    ) for image in images]

        return pil_images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if not isinstance(snake_case_   , snake_case_    ):
          A_    :			int									=    [images]
        A_    :			int									=    [np.array(snake_case_    ).astype(np.floataa    ) / 2_55.0 for image in images]
        A_    :			Tuple									=    np.stack(snake_case_   , axis=0    )

        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

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

        A_    :			Dict									=    torch.from_numpy(images.transpose(0   , 3   , 1   , 2    )    )
        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

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

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return 2.0 * images - 1.0

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

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

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

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

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

        """simple docstring"""

        if height is None:
          A_    :			int									=    image.height
        if width is None:
          A_    :			List[Any]									=    image.width

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

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

        """simple docstring"""

        A_    :			Dict									=    (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(snake_case_   , snake_case_    ):
          A_    :			Dict									=    [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:
            A_    :			Dict									=    [self.convert_to_rgb(snake_case_    ) for i in image]
          if self.config.do_resize:
            A_    :			Any									=    [self.resize(snake_case_   , snake_case_   , snake_case_    ) for i in image]
          A_    :			Optional[int]									=    self.pil_to_numpy(snake_case_    )  # to np
          A_    :			Dict									=    self.numpy_to_pt(snake_case_    )  # to pt

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

        if do_normalize:
          A_    :			Any									=    self.normalize(snake_case_    )

        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , 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"]:
          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'   , snake_case_   , standard_warn=snake_case_    )
          A_    :			Dict									=    'np'

        if output_type == "latent":
          return image

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

        A_    :			List[Any]									=    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(snake_case_    )

        if output_type == "np":
          return image

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


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					str             =  CONFIG_NAME

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

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			int									=    images[None, ...]
        A_    :			Optional[int]									=    (images * 2_5_5).round().astype('uint8'    )
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          A_    :			Optional[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_     (	snake_case_    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			Union[str, Any]									=    images[None, ...]
        A_    :			Tuple									=    images[:, :, :, 3:]
        if images.shape[-1] == 6:
          A_    :			Dict									=    (images_depth * 2_5_5).round().astype('uint8'    )
          A_    :			List[Any]									=    [
              Image.fromarray(self.rgblike_to_depthmap(snake_case_    )   , mode='I;16'    ) for image_depth in images_depth
          ]
        elif images.shape[-1] == 4:
          A_    :			Optional[int]									=    (images_depth * 6_55_35.0).astype(np.uintaa    )
          A_    :			Union[str, Any]									=    [Image.fromarray(snake_case_   , mode='I;16'    ) for image_depth in images_depth]
        else:
          raise Exception('Not supported'    )

        return pil_images

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , 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"]:
          A_    :			Optional[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_    )
          A_    :			Any									=    'np'

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

        A_    :			List[str]									=    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(snake_case_    )

        if output_type == "np":
          if image.shape[-1] == 6:
            A_    :			Dict									=    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(snake_case_    ), self.numpy_to_depth(snake_case_    )
        else:
          raise Exception(F"""This type {output_type} is not supported"""    )