



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 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: torch.FloatTensor




class 	__snake_case (				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@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__()
											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															    F'''The configuration file of this model: {self.__class__} is outdated.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"""					,					a_					,					standard_warn=a_						)
															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=a_					,					num_channels=a_					,					eps=1E-6					,					affine=a_						)
															if use_linear_projection:
																			a__			 =						nn.Linear(a_					,					a_						)
															else:
																			a__			 =						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"

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

															a__			 =						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"

															a__			 =						sample_size
															a__			 =						sample_size

															a__			 =						patch_size
															a__			 =						PatchEmbed(
															    height=a_					,					width=a_					,					patch_size=a_					,					in_channels=a_					,					embed_dim=a_					,					)

											# 3. Define transformers blocks
											a__			 =						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
											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(a_					,					a_						)
															else:
																			a__			 =						nn.Convad(a_					,					a_					,					kernel_size=1					,					stride=1					,					padding=0						)
											elif self.is_input_vectorized:
															a__			 =						nn.LayerNorm(a_						)
															a__			 =						nn.Linear(a_					,					self.num_vector_embeds - 1						)
											elif self.is_input_patches:
															a__			 =						nn.LayerNorm(a_					,					elementwise_affine=a_					,					eps=1E-6						)
															a__			 =						nn.Linear(a_					,					2 * inner_dim						)
															a__			 =						nn.Linear(a_					,					patch_size * patch_size * self.out_channels						)







							def _a (							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)
															a__			 =						(1 - attention_mask.to(hidden_states.dtype						)) * -10_000.0
															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						)) * -10_000.0
															a__			 =						encoder_attention_mask.unsqueeze(1						)

											# 1. Input
											if self.is_input_continuous:
															a__ ,     a__ ,     a__ ,     a__			 =						hidden_states.shape
															a__			 =						hidden_states

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

											# 2. Blocks
											for block in self.transformer_blocks:
															a__			 =						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:
																			a__			 =						hidden_states.reshape(a_					,					a_					,					a_					,					a_						).permute(0					,					3					,					1					,					2						).contiguous()
																			a__			 =						self.proj_out(a_						)
															else:
																			a__			 =						self.proj_out(a_						)
																			a__			 =						hidden_states.reshape(a_					,					a_					,					a_					,					a_						).permute(0					,					3					,					1					,					2						).contiguous()

															a__			 =						hidden_states + residual
											elif self.is_input_vectorized:
															a__			 =						self.norm_out(a_						)
															a__			 =						self.out(a_						)
															# (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(
															    a_					,					a_					,					hidden_dtype=hidden_states.dtype						)
															a__ ,     a__			 =						self.proj_out_a(F.silu(a_						)						).chunk(2					,					dim=1						)
															a__			 =						self.norm_out(a_						) * (1 + scale[:, None]) + shift[:, None]
															a__			 =						self.proj_out_a(a_						)

															# unpatchify
															a__			 =						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"""					,					a_						)
															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=a_						)






from __future__ import annotations

def      A_ (		__a       :			list[int | float]						):

				"""simple docstring"""
				if len(__a						) == 0:
								raise ValueError("""find_max() arg is an empty sequence"""						)
				a__			 =						nums[0]
				for x in nums:
								if x > max_num:
												a__			 =						x
				return max_num


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)

