




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__							(_UpperCamelCase       ):
	"""simple docstring"""



	SCREAMING_SNAKE_CASE_	:							torch.FloatTensor




class    snake_case__							(_UpperCamelCase   ,	_UpperCamelCase       ):
	"""simple docstring"""



	@register_to_config
	def __init__(				self    :					List[Any] ,				__lowerCamelCase    :					int = 16 ,				__lowerCamelCase    :					int = 88 ,				__lowerCamelCase    :					Optional[int] = None ,				__lowerCamelCase    :					Optional[int] = None ,				__lowerCamelCase    :					int = 1 ,				__lowerCamelCase    :					float = 0.0 ,				__lowerCamelCase    :					int = 32 ,				__lowerCamelCase    :					Optional[int] = None ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					Optional[int] = None ,				__lowerCamelCase    :					Optional[int] = None ,				__lowerCamelCase    :					Optional[int] = None ,				__lowerCamelCase    :					str = "geglu" ,				__lowerCamelCase    :					Optional[int] = None ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					bool = False ,				__lowerCamelCase    :					str = "layer_norm" ,				__lowerCamelCase    :					bool = True ,				) ->					Optional[int]:
				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							    f"""The configuration file of this model: {self.__class__} is outdated.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" ,				__lowerCamelCase ,				standard_warn=__lowerCamelCase							)
							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=__lowerCamelCase ,				num_channels=__lowerCamelCase ,				eps=1e-6 ,				affine=__lowerCamelCase							)
							if use_linear_projection:
										a   =      nn.Linear(__lowerCamelCase ,				__lowerCamelCase							)
							else:
										a   =      nn.Convad(__lowerCamelCase ,				__lowerCamelCase ,				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=__lowerCamelCase ,				embed_dim=__lowerCamelCase ,				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=__lowerCamelCase ,				width=__lowerCamelCase ,				patch_size=__lowerCamelCase ,				in_channels=__lowerCamelCase ,				embed_dim=__lowerCamelCase ,				)

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

				# 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(__lowerCamelCase ,				__lowerCamelCase							)
							else:
										a   =      nn.Convad(__lowerCamelCase ,				__lowerCamelCase ,				kernel_size=1 ,				stride=1 ,				padding=0							)
				elif self.is_input_vectorized:
							a   =      nn.LayerNorm(__lowerCamelCase							)
							a   =      nn.Linear(__lowerCamelCase ,				self.num_vector_embeds - 1							)
				elif self.is_input_patches:
							a   =      nn.LayerNorm(__lowerCamelCase ,				elementwise_affine=__lowerCamelCase ,				eps=1e-6							)
							a   =      nn.Linear(__lowerCamelCase ,				2 * inner_dim							)
							a   =      nn.Linear(__lowerCamelCase ,				patch_size * patch_size * self.out_channels							)





	def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					torch.Tensor ,				__lowerCamelCase    :					Optional[torch.Tensor] = None ,				__lowerCamelCase    :					Optional[torch.LongTensor] = None ,				__lowerCamelCase    :					Optional[torch.LongTensor] = None ,				__lowerCamelCase    :					Dict[str, Any] = None ,				__lowerCamelCase    :					Optional[torch.Tensor] = None ,				__lowerCamelCase    :					Optional[torch.Tensor] = None ,				__lowerCamelCase    :					bool = True ,				) ->					int:
				# 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_00_00.0
							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_00_00.0
							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(__lowerCamelCase							)
							if not self.use_linear_projection:
										a   =      self.proj_in(__lowerCamelCase							)
										a   =      hidden_states.shape[1]
										a   =      hidden_states.permute(0 ,				2 ,				3 ,				1							).reshape(__lowerCamelCase ,				height * width ,				__lowerCamelCase							)
							else:
										a   =      hidden_states.shape[1]
										a   =      hidden_states.permute(0 ,				2 ,				3 ,				1							).reshape(__lowerCamelCase ,				height * width ,				__lowerCamelCase							)
										a   =      self.proj_in(__lowerCamelCase							)
				elif self.is_input_vectorized:
							a   =      self.latent_image_embedding(__lowerCamelCase							)
				elif self.is_input_patches:
							a   =      self.pos_embed(__lowerCamelCase							)

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

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

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

							# 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" ,				__lowerCamelCase							)
							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=__lowerCamelCase							)












from ..utils import DummyObject, requires_backends




class    snake_case__							(metaclass=_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= ["""torch""", """transformers""", """k_diffusion"""]





				def __init__(				self    :					Optional[int] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							requires_backends(self ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Tuple							) ->					List[Any]:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					Optional[Any] ,				*__lowerCamelCase    :					List[str] ,				**__lowerCamelCase    :					int							) ->					Dict:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)






