
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 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  42





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	@register_to_config
	def __init__( self      :				List[str]							,A      :				int = 16							,A      :				int = 88							,A      :				Optional[int] = None							,A      :				Optional[int] = None							,A      :				int = 1							,A      :				float = 0.0							,A      :				int = 32							,A      :				Optional[int] = None							,A      :				bool = False							,A      :				Optional[int] = None							,A      :				Optional[int] = None							,A      :				Optional[int] = None							,A      :				str = "geglu"							,A      :				Optional[int] = None							,A      :				bool = False							,A      :				bool = False							,A      :				bool = False							,A      :				str = "layer_norm"							,A      :				bool = 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									    f'''The configuration file of this model: {self.__class__} is outdated.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        UpperCamelCase_						( self      :				Dict							,A      :				torch.Tensor							,A      :				Optional[torch.Tensor] = None							,A      :				Optional[torch.LongTensor] = None							,A      :				Optional[torch.LongTensor] = None							,A      :				Dict[str, Any] = None							,A      :				Optional[torch.Tensor] = None							,A      :				Optional[torch.Tensor] = None							,A      :				bool = 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				)) * -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(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 typing import Any





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Union[str, Any]				):
					__A         =				[]
					__A         =				0






	def __str__( self      :				Tuple				):
					__A         =				"<" + str(self.stack				)[1:-1] + ">"
					return printed






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				Any				):
					self.stack.append(A				)
					__A         =				self.length + 1






	def        UpperCamelCase_						( self      :				Any				):
					self.rotate(1				)
					__A         =				self.stack[self.length - 1]
					__A         =				self.stack[:-1]
					self.rotate(self.length - 1				)
					__A         =				self.length - 1
					return dequeued






	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					for _ in range(A				):
									__A         =				self.stack[0]
									__A         =				self.stack[1:]
									self.put(A				)
									__A         =				self.length - 1






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.get()
					self.put(A				)
					self.rotate(self.length - 1				)
					return front






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return self.length
