
'''simple docstring'''





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 					__lowercase		(					a__       ):
					__magic_name__						:							torch.FloatTensor






class 					__lowercase		(					a__							,       a__       ):


					@register_to_config
					def __init__(	self       ,       a__ = 1_6       ,       a__ = 8_8       ,       a__ = None       ,       a__ = None       ,       a__ = 1       ,       a__ = 0.0       ,       a__ = 3_2       ,       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       ,       )		->							Union[str, Any]:





									'''simple docstring'''
									super().__init__()
									A_       	=  use_linear_projection
									A_       	=  num_attention_heads
									A_       	=  attention_head_dim
									A_       	=  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
									A_       	=  (in_channels is not None) and (patch_size is None)
									A_       	=  num_vector_embeds is not None
									A_       	=  in_channels is not None and patch_size is not None

									if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
													A_       	=  (
													    F"The configuration file of this model: {self.__class__} is outdated.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'''       ,       SCREAMING_SNAKE_CASE_       ,       standard_warn=SCREAMING_SNAKE_CASE_						)
													A_       	=  '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:
													A_       	=  in_channels

													A_       	=  torch.nn.GroupNorm(num_groups=SCREAMING_SNAKE_CASE_       ,       num_channels=SCREAMING_SNAKE_CASE_       ,       eps=1E-6       ,       affine=SCREAMING_SNAKE_CASE_						)
													if use_linear_projection:
																	A_       	=  nn.Linear(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_						)
													else:
																	A_       	=  nn.Convad(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       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"

													A_       	=  sample_size
													A_       	=  sample_size
													A_       	=  num_vector_embeds
													A_       	=  self.height * self.width

													A_       	=  ImagePositionalEmbeddings(
													    num_embed=SCREAMING_SNAKE_CASE_       ,       embed_dim=SCREAMING_SNAKE_CASE_       ,       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"

													A_       	=  sample_size
													A_       	=  sample_size

													A_       	=  patch_size
													A_       	=  PatchEmbed(
													    height=SCREAMING_SNAKE_CASE_       ,       width=SCREAMING_SNAKE_CASE_       ,       patch_size=SCREAMING_SNAKE_CASE_       ,       in_channels=SCREAMING_SNAKE_CASE_       ,       embed_dim=SCREAMING_SNAKE_CASE_       ,       )

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

									# 4. Define output layers
									A_       	=  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:
																	A_       	=  nn.Linear(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_						)
													else:
																	A_       	=  nn.Convad(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       kernel_size=1       ,       stride=1       ,       padding=0						)
									elif self.is_input_vectorized:
													A_       	=  nn.LayerNorm(SCREAMING_SNAKE_CASE_						)
													A_       	=  nn.Linear(SCREAMING_SNAKE_CASE_       ,       self.num_vector_embeds - 1						)
									elif self.is_input_patches:
													A_       	=  nn.LayerNorm(SCREAMING_SNAKE_CASE_       ,       elementwise_affine=SCREAMING_SNAKE_CASE_       ,       eps=1E-6						)
													A_       	=  nn.Linear(SCREAMING_SNAKE_CASE_       ,       2 * inner_dim						)
													A_       	=  nn.Linear(SCREAMING_SNAKE_CASE_       ,       patch_size * patch_size * self.out_channels						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = True       ,       )		->							Optional[Any]:





									'''simple docstring'''
									# 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)
													A_       	=  (1 - attention_mask.to(hidden_states.dtype						)) * -1_0_0_0_0.0
													A_       	=  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:
													A_       	=  (1 - encoder_attention_mask.to(hidden_states.dtype						)) * -1_0_0_0_0.0
													A_       	=  encoder_attention_mask.unsqueeze(1						)

									# 1. Input
									if self.is_input_continuous:
													A_       	=  hidden_states.shape
													A_       	=  hidden_states

													A_       	=  self.norm(SCREAMING_SNAKE_CASE_						)
													if not self.use_linear_projection:
																	A_       	=  self.proj_in(SCREAMING_SNAKE_CASE_						)
																	A_       	=  hidden_states.shape[1]
																	A_       	=  hidden_states.permute(0       ,       2       ,       3       ,       1						).reshape(SCREAMING_SNAKE_CASE_       ,       height * width       ,       SCREAMING_SNAKE_CASE_						)
													else:
																	A_       	=  hidden_states.shape[1]
																	A_       	=  hidden_states.permute(0       ,       2       ,       3       ,       1						).reshape(SCREAMING_SNAKE_CASE_       ,       height * width       ,       SCREAMING_SNAKE_CASE_						)
																	A_       	=  self.proj_in(SCREAMING_SNAKE_CASE_						)
									elif self.is_input_vectorized:
													A_       	=  self.latent_image_embedding(SCREAMING_SNAKE_CASE_						)
									elif self.is_input_patches:
													A_       	=  self.pos_embed(SCREAMING_SNAKE_CASE_						)

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

									# 3. Output
									if self.is_input_continuous:
													if not self.use_linear_projection:
																	A_       	=  hidden_states.reshape(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_						).permute(0       ,       3       ,       1       ,       2						).contiguous()
																	A_       	=  self.proj_out(SCREAMING_SNAKE_CASE_						)
													else:
																	A_       	=  self.proj_out(SCREAMING_SNAKE_CASE_						)
																	A_       	=  hidden_states.reshape(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_						).permute(0       ,       3       ,       1       ,       2						).contiguous()

													A_       	=  hidden_states + residual
									elif self.is_input_vectorized:
													A_       	=  self.norm_out(SCREAMING_SNAKE_CASE_						)
													A_       	=  self.out(SCREAMING_SNAKE_CASE_						)
													# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
													A_       	=  logits.permute(0       ,       2       ,       1						)

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

													# unpatchify
													A_       	=  int(hidden_states.shape[1] ** 0.5						)
													A_       	=  hidden_states.reshape(
													    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)						)
													A_       	=  torch.einsum('''nhwpqc->nchpwq'''       ,       SCREAMING_SNAKE_CASE_						)
													A_       	=  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=SCREAMING_SNAKE_CASE_						)

from cva import destroyAllWindows, imread, imshow, waitKey



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_       	=  img.shape[0], img.shape[1]

				def to_grayscale(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
								return 0.2126 * red + 0.587 * green + 0.114 * blue

				def normalize(__UpperCamelCase     ):
								return min(__UpperCamelCase ,						2_55     )

				for i in range(__UpperCamelCase     ):
								for j in range(__UpperCamelCase     ):
												A_       	=  int(to_grayscale(*img[i][j]     )     )
												A_       	=  [
												    normalize(__UpperCamelCase     ),
												    normalize(greyscale + factor     ),
												    normalize(greyscale + 2 * factor     ),
												]

				return img


if __name__ == "__main__":
						# read original image
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       {
						    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()