

from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin


@dataclass
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
    __lowerCamelCase		: torch.FloatTensor



class    SCREAMING_SNAKE_CASE__      (__snake_case	, __snake_case  ):





    @register_to_config
    def __init__(   self		,     a = 16		,     a = 88		,     a = None		,     a = None		,     a = 1		,     a = 0.0		,     a = 32		,     a = None		,     a = False		,     a = None		,     a = None		,     a = None		,     a = "geglu"		,     a = None		,     a = False		,     a = False		,     a = False		,     a = "layer_norm"		,     a = True		,     ):
         super().__init__()
         lowercase__					:     List[str]   = use_linear_projection
         lowercase__					:     int   = num_attention_heads
         lowercase__					:     str   = attention_head_dim
         lowercase__					:     Union[str, Any]   = num_attention_heads * attention_head_dim

         # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
         # Define whether input is continuous or discrete depending on configuration
         lowercase__					:     str   = (in_channels is not None) and (patch_size is None)
         lowercase__					:     Tuple   = num_vector_embeds is not None
         lowercase__					:     str   = in_channels is not None and patch_size is not None

         if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
              lowercase__					:     List[str]   = (
                  f"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
                  ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
                  ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
                  ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
                  ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
              )
              deprecate('norm_type!=num_embeds_ada_norm'		,     '1.0.0'		,     a		,     standard_warn=a)
              lowercase__					:     str   = 'ada_norm'

         if self.is_input_continuous and self.is_input_vectorized:
              raise ValueError(
                  f"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
                  ' sure that either `in_channels` or `num_vector_embeds` is None.')
         elif self.is_input_vectorized and self.is_input_patches:
              raise ValueError(
                  f"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
                  ' sure that either `num_vector_embeds` or `num_patches` is None.')
         elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
              raise ValueError(
                  f"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
                  f""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.""")

         # 2. Define input layers
         if self.is_input_continuous:
              lowercase__					:     List[str]   = in_channels

              lowercase__					:     Optional[Any]   = torch.nn.GroupNorm(num_groups=a		,     num_channels=a		,     eps=1e-6		,     affine=a)
              if use_linear_projection:
                   lowercase__					:     Union[str, Any]   = nn.Linear(a		,     a)
              else:
                   lowercase__					:     List[str]   = nn.Convad(a		,     a		,     kernel_size=1		,     stride=1		,     padding=0)
         elif self.is_input_vectorized:
              assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
              assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

              lowercase__					:     Optional[Any]   = sample_size
              lowercase__					:     Optional[int]   = sample_size
              lowercase__					:     Union[str, Any]   = num_vector_embeds
              lowercase__					:     Any   = self.height * self.width

              lowercase__					:     Optional[Any]   = ImagePositionalEmbeddings(
                  num_embed=a		,     embed_dim=a		,     height=self.height		,     width=self.width)
         elif self.is_input_patches:
              assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

              lowercase__					:     Optional[int]   = sample_size
              lowercase__					:     Optional[int]   = sample_size

              lowercase__					:     Optional[int]   = patch_size
              lowercase__					:     List[Any]   = PatchEmbed(
                  height=a		,     width=a		,     patch_size=a		,     in_channels=a		,     embed_dim=a		,     )

         # 3. Define transformers blocks
         lowercase__					:     Dict   = nn.ModuleList(
             [
                 BasicTransformerBlock(
                     a		,     a		,     a		,     dropout=a		,     cross_attention_dim=a		,     activation_fn=a		,     num_embeds_ada_norm=a		,     attention_bias=a		,     only_cross_attention=a		,     upcast_attention=a		,     norm_type=a		,     norm_elementwise_affine=a		,     )
                 for d in range(a)
             ])

         # 4. Define output layers
         lowercase__					:     Tuple   = in_channels if out_channels is None else out_channels
         if self.is_input_continuous:
              # TODO: should use out_channels for continuous projections
              if use_linear_projection:
                   lowercase__					:     List[str]   = nn.Linear(a		,     a)
              else:
                   lowercase__					:     str   = nn.Convad(a		,     a		,     kernel_size=1		,     stride=1		,     padding=0)
         elif self.is_input_vectorized:
              lowercase__					:     List[Any]   = nn.LayerNorm(a)
              lowercase__					:     List[Any]   = nn.Linear(a		,     self.num_vector_embeds - 1)
         elif self.is_input_patches:
              lowercase__					:     Any   = nn.LayerNorm(a		,     elementwise_affine=a		,     eps=1e-6)
              lowercase__					:     Optional[Any]   = nn.Linear(a		,     2 * inner_dim)
              lowercase__					:     Optional[Any]   = nn.Linear(a		,     patch_size * patch_size * self.out_channels)





    def    snake_case_       (   self		,     a		,     a = None		,     a = None		,     a = None		,     a = None		,     a = None		,     a = None		,     a = True		,     ):
         # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
         #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
         #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
         # expects mask of shape:
         #   [batch, key_tokens]
         # adds singleton query_tokens dimension:
         #   [batch,                    1, key_tokens]
         # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
         #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
         #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
         if attention_mask is not None and attention_mask.ndim == 2:
              # assume that mask is expressed as:
              #   (1 = keep,      0 = discard)
              # convert mask into a bias that can be added to attention scores:
              #       (keep = +0,     discard = -10000.0)
              lowercase__					:     Tuple   = (1 - attention_mask.to(hidden_states.dtype)) * -10_000.0
              lowercase__					:     int   = attention_mask.unsqueeze(1)

         # convert encoder_attention_mask to a bias the same way we do for attention_mask
         if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
              lowercase__					:     List[Any]   = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10_000.0
              lowercase__					:     Any   = encoder_attention_mask.unsqueeze(1)

         # 1. Input
         if self.is_input_continuous:
              lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     List[Any]   = hidden_states.shape
              lowercase__					:     Tuple   = hidden_states

              lowercase__					:     str   = self.norm(a)
              if not self.use_linear_projection:
                   lowercase__					:     Optional[int]   = self.proj_in(a)
                   lowercase__					:     Dict   = hidden_states.shape[1]
                   lowercase__					:     Tuple   = hidden_states.permute(0		,     2		,     3		,     1).reshape(a		,     height * width		,     a)
              else:
                   lowercase__					:     List[str]   = hidden_states.shape[1]
                   lowercase__					:     str   = hidden_states.permute(0		,     2		,     3		,     1).reshape(a		,     height * width		,     a)
                   lowercase__					:     int   = self.proj_in(a)
         elif self.is_input_vectorized:
              lowercase__					:     Tuple   = self.latent_image_embedding(a)
         elif self.is_input_patches:
              lowercase__					:     Any   = self.pos_embed(a)

         # 2. Blocks
         for block in self.transformer_blocks:
              lowercase__					:     Any   = block(
                  a		,     attention_mask=a		,     encoder_hidden_states=a		,     encoder_attention_mask=a		,     timestep=a		,     cross_attention_kwargs=a		,     class_labels=a		,     )

         # 3. Output
         if self.is_input_continuous:
              if not self.use_linear_projection:
                   lowercase__					:     Union[str, Any]   = hidden_states.reshape(a		,     a		,     a		,     a).permute(0		,     3		,     1		,     2).contiguous()
                   lowercase__					:     Optional[int]   = self.proj_out(a)
              else:
                   lowercase__					:     Tuple   = self.proj_out(a)
                   lowercase__					:     Optional[int]   = hidden_states.reshape(a		,     a		,     a		,     a).permute(0		,     3		,     1		,     2).contiguous()

              lowercase__					:     Tuple   = hidden_states + residual
         elif self.is_input_vectorized:
              lowercase__					:     List[Any]   = self.norm_out(a)
              lowercase__					:     Tuple   = self.out(a)
              # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
              lowercase__					:     int   = logits.permute(0		,     2		,     1)

              # log(p(x_0))
              lowercase__					:     Union[str, Any]   = F.log_softmax(logits.double()		,     dim=1).float()
         elif self.is_input_patches:
              # TODO: cleanup!
              lowercase__					:     Optional[Any]   = self.transformer_blocks[0].norma.emb(
                  a		,     a		,     hidden_dtype=hidden_states.dtype)
              lowercase__   ,				lowercase__					:     str   = self.proj_out_a(F.silu(a)).chunk(2		,     dim=1)
              lowercase__					:     Any   = self.norm_out(a) * (1 + scale[:, None]) + shift[:, None]
              lowercase__					:     Optional[int]   = self.proj_out_a(a)

              # unpatchify
              lowercase__					:     Optional[int]   = int(hidden_states.shape[1] ** 0.5)
              lowercase__					:     Optional[Any]   = hidden_states.reshape(
                  shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels))
              lowercase__					:     str   = torch.einsum('nhwpqc->nchpwq'		,     a)
              lowercase__					:     Union[str, Any]   = hidden_states.reshape(
                  shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size))

         if not return_dict:
              return (output,)

         return TransformeraDModelOutput(sample=a)




from cva import destroyAllWindows, imread, imshow, waitKey

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     lowercase__   ,				lowercase__					:     Optional[int]   = img.shape[0], img.shape[1]

     def to_grayscale(SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(SCREAMING_SNAKE_CASE_	:					int    ):
          return min(SCREAMING_SNAKE_CASE_			,  255    )

     for i in range(SCREAMING_SNAKE_CASE_    ):
          for j in range(SCREAMING_SNAKE_CASE_    ):
               lowercase__					:     str   = int(to_grayscale(*img[i][j]    )    )
               lowercase__					:     Dict   = [
                   normalize(SCREAMING_SNAKE_CASE_    ),
                   normalize(greyscale + factor    ),
                   normalize(greyscale + 2 * factor    ),
               ]

     return img


if __name__ == "__main__":
       # read original image
       snake_case_    						=				{
           percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F'''Original image with sepia (factor: {percentage})''', img)

       waitKey(0)
       destroyAllWindows()

