
'''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 				lowerCAmelCase__     (						_a  ):
					lowerCAmelCase_       =  42







class 				lowerCAmelCase__     (						_a					,		_a  ):






					@register_to_config
					def __init__(     self       ,							__SCREAMING_SNAKE_CASE = 16       ,							__SCREAMING_SNAKE_CASE = 88       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = 1       ,							__SCREAMING_SNAKE_CASE = 0.0       ,							__SCREAMING_SNAKE_CASE = 32       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = False       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = "geglu"       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = False       ,							__SCREAMING_SNAKE_CASE = False       ,							__SCREAMING_SNAKE_CASE = False       ,							__SCREAMING_SNAKE_CASE = "layer_norm"       ,							__SCREAMING_SNAKE_CASE = True       ,							):


											"""simple docstring"""





											super().__init__()
											lowercase_			:  str									= use_linear_projection
											lowercase_			:  Optional[int]									= num_attention_heads
											lowercase_			:  List[Any]									= attention_head_dim
											lowercase_			:  List[Any]									= 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
											lowercase_			:  Tuple									= (in_channels is not None) and (patch_size is None)
											lowercase_			:  Union[str, Any]									= num_vector_embeds is not None
											lowercase_			:  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:
																	lowercase_			:  List[str]									= (
																	    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	)
																	lowercase_			:  str									= '''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:
																	lowercase_			:  List[Any]									= in_channels

																	lowercase_			:  Tuple									= torch.nn.GroupNorm(num_groups=__SCREAMING_SNAKE_CASE       ,							num_channels=__SCREAMING_SNAKE_CASE       ,							eps=1E-6       ,							affine=__SCREAMING_SNAKE_CASE	)
																	if use_linear_projection:
																							lowercase_			:  Tuple									= nn.Linear(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)
																	else:
																							lowercase_			:  Dict									= 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"

																	lowercase_			:  List[Any]									= sample_size
																	lowercase_			:  Union[str, Any]									= sample_size
																	lowercase_			:  List[str]									= num_vector_embeds
																	lowercase_			:  List[str]									= self.height * self.width

																	lowercase_			:  int									= 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"

																	lowercase_			:  str									= sample_size
																	lowercase_			:  Optional[int]									= sample_size

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






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = True       ,							):


											"""simple docstring"""





											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)
																	lowercase_			:  int									= (1 - attention_mask.to(hidden_states.dtype	)) * -1_00_00.0
																	lowercase_			:  List[str]									= 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:
																	lowercase_			:  int									= (1 - encoder_attention_mask.to(hidden_states.dtype	)) * -1_00_00.0
																	lowercase_			:  List[Any]									= encoder_attention_mask.unsqueeze(1	)

											# 1. Input
											if self.is_input_continuous:
																	lowercase_		,				lowercase_		,				lowercase_		,				lowercase_			:  int									= hidden_states.shape
																	lowercase_			:  str									= hidden_states

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

											# 2. Blocks
											for block in self.transformer_blocks:
																	lowercase_			:  Any									= 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:
																							lowercase_			:  Any									= hidden_states.reshape(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	).permute(0       ,							3       ,							1       ,							2	).contiguous()
																							lowercase_			:  Optional[int]									= self.proj_out(__SCREAMING_SNAKE_CASE	)
																	else:
																							lowercase_			:  List[Any]									= self.proj_out(__SCREAMING_SNAKE_CASE	)
																							lowercase_			:  Union[str, Any]									= hidden_states.reshape(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	).permute(0       ,							3       ,							1       ,							2	).contiguous()

																	lowercase_			:  str									= hidden_states + residual
											elif self.is_input_vectorized:
																	lowercase_			:  int									= self.norm_out(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  Any									= self.out(__SCREAMING_SNAKE_CASE	)
																	# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
																	lowercase_			:  int									= logits.permute(0       ,							2       ,							1	)

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

																	# unpatchify
																	lowercase_			:  Optional[int]									= int(hidden_states.shape[1] ** 0.5	)
																	lowercase_			:  str									= hidden_states.reshape(
																	    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)	)
																	lowercase_			:  List[Any]									= torch.einsum('''nhwpqc->nchpwq'''       ,							__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  str									= 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	)








'''simple docstring'''







def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 1000							):



						"""simple docstring"""





						lowercase_			:  List[Any]									= 0
						for i in range(__SCREAMING_SNAKE_CASE							):
												if i % 3 == 0 or i % 5 == 0:
																		result += i
						return result


if __name__ == "__main__":
							print(f"""{solution() = }""")



