





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_     (     a__       ):
					_a			:	torch.FloatTensor






class 	UpperCamelCase_     (     a__     ,    a__       ):



					@register_to_config
					def __init__(		self    :    List[str]					,			lowerCamelCase    :    List[Any] = 16					,			lowerCamelCase    :    Optional[Any] = 88					,			lowerCamelCase    :    Any = None					,			lowerCamelCase    :    Tuple = None					,			lowerCamelCase    :    str = 1					,			lowerCamelCase    :    Dict = 0.0					,			lowerCamelCase    :    Dict = 32					,			lowerCamelCase    :    List[str] = None					,			lowerCamelCase    :    Any = False					,			lowerCamelCase    :    List[Any] = None					,			lowerCamelCase    :    List[str] = None					,			lowerCamelCase    :    Tuple = None					,			lowerCamelCase    :    int = "geglu"					,			lowerCamelCase    :    Optional[Any] = None					,			lowerCamelCase    :    Dict = False					,			lowerCamelCase    :    str = False					,			lowerCamelCase    :    int = False					,			lowerCamelCase    :    str = "layer_norm"					,			lowerCamelCase    :    int = True					,			):
							super().__init__()
							lowerCamelCase_  :    Tuple   	=			use_linear_projection
							lowerCamelCase_  :    int   	=			num_attention_heads
							lowerCamelCase_  :    Dict   	=			attention_head_dim
							lowerCamelCase_  :    Optional[int]   	=			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
							lowerCamelCase_  :    Optional[Any]   	=			(in_channels is not None) and (patch_size is None)
							lowerCamelCase_  :    Tuple   	=			num_vector_embeds is not None
							lowerCamelCase_  :    Optional[int]   	=			in_channels is not None and patch_size is not None

							if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
									lowerCamelCase_  :    Any   	=			(
									    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_	)
									lowerCamelCase_  :    Any   	=			'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:
									lowerCamelCase_  :    Optional[int]   	=			in_channels

									lowerCamelCase_  :    str   	=			torch.nn.GroupNorm(num_groups=SCREAMING_SNAKE_CASE_					,			num_channels=SCREAMING_SNAKE_CASE_					,			eps=1E-6					,			affine=SCREAMING_SNAKE_CASE_	)
									if use_linear_projection:
											lowerCamelCase_  :    Dict   	=			nn.Linear(SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_	)
									else:
											lowerCamelCase_  :    Any   	=			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"

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

									lowerCamelCase_  :    Union[str, Any]   	=			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"

									lowerCamelCase_  :    int   	=			sample_size
									lowerCamelCase_  :    Optional[Any]   	=			sample_size

									lowerCamelCase_  :    List[str]   	=			patch_size
									lowerCamelCase_  :    Tuple   	=			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
							lowerCamelCase_  :    Any   	=			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
							lowerCamelCase_  :    Optional[int]   	=			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:
											lowerCamelCase_  :    Optional[Any]   	=			nn.Linear(SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_	)
									else:
											lowerCamelCase_  :    List[str]   	=			nn.Convad(SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			kernel_size=1					,			stride=1					,			padding=0	)
							elif self.is_input_vectorized:
									lowerCamelCase_  :    Optional[Any]   	=			nn.LayerNorm(SCREAMING_SNAKE_CASE_	)
									lowerCamelCase_  :    Any   	=			nn.Linear(SCREAMING_SNAKE_CASE_					,			self.num_vector_embeds - 1	)
							elif self.is_input_patches:
									lowerCamelCase_  :    List[str]   	=			nn.LayerNorm(SCREAMING_SNAKE_CASE_					,			elementwise_affine=SCREAMING_SNAKE_CASE_					,			eps=1E-6	)
									lowerCamelCase_  :    Tuple   	=			nn.Linear(SCREAMING_SNAKE_CASE_					,			2 * inner_dim	)
									lowerCamelCase_  :    Any   	=			nn.Linear(SCREAMING_SNAKE_CASE_					,			patch_size * patch_size * self.out_channels	)





					def      __a    (		self    :    Dict					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    Optional[int] = None					,			lowerCamelCase    :    str = None					,			lowerCamelCase    :    Optional[Any] = None					,			lowerCamelCase    :    Union[str, Any] = None					,			lowerCamelCase    :    Any = None					,			lowerCamelCase    :    Tuple = None					,			lowerCamelCase    :    Optional[Any] = 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)
									lowerCamelCase_  :    List[str]   	=			(1 - attention_mask.to(hidden_states.dtype	)) * -1_00_00.0
									lowerCamelCase_  :    Tuple   	=			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:
									lowerCamelCase_  :    int   	=			(1 - encoder_attention_mask.to(hidden_states.dtype	)) * -1_00_00.0
									lowerCamelCase_  :    Dict   	=			encoder_attention_mask.unsqueeze(1	)

							# 1. Input
							if self.is_input_continuous:
									lowerCamelCase_  :    Union[str, Any]   	=			hidden_states.shape
									lowerCamelCase_  :    str   	=			hidden_states

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

							# 2. Blocks
							for block in self.transformer_blocks:
									lowerCamelCase_  :    Optional[int]   	=			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:
											lowerCamelCase_  :    Tuple   	=			hidden_states.reshape(SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_	).permute(0					,			3					,			1					,			2	).contiguous()
											lowerCamelCase_  :    int   	=			self.proj_out(SCREAMING_SNAKE_CASE_	)
									else:
											lowerCamelCase_  :    Optional[int]   	=			self.proj_out(SCREAMING_SNAKE_CASE_	)
											lowerCamelCase_  :    Optional[int]   	=			hidden_states.reshape(SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_	).permute(0					,			3					,			1					,			2	).contiguous()

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

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

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







def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		return (input_a, input_a).count(0	) % 2


def 		_SCREAMING_SNAKE_CASE    (   ):
		assert xor_gate(0       ,0	) == 0
		assert xor_gate(0       ,1	) == 1
		assert xor_gate(1       ,0	) == 1
		assert xor_gate(1       ,1	) == 0


if __name__ == "__main__":
		print(xor_gate(0, 0))
		print(xor_gate(0, 1))
