



'''simple docstring'''





from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin






@dataclass
class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       42






class 						__a (     SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				):



	@register_to_config
	def __init__(						self							:    Union[str, Any]				,	snake_case_							:    int = 32				,	snake_case_							:    int = 64				,	snake_case_							:    int = 20				,	snake_case_							:    int = 7_68				,	snake_case_							:    str=77				,	snake_case_							:    List[Any]=4				,	snake_case_							:    float = 0.0				,	snake_case_							:    str = "silu"				,	snake_case_							:    Optional[str] = None				,	snake_case_							:    Optional[str] = None				,	snake_case_							:    Optional[str] = "linear"				,	snake_case_							:    Optional[str] = "prd"				,	snake_case_							:    Optional[int] = None				,	snake_case_							:    Optional[int] = None				,	snake_case_							:    Optional[int] = None				,	)->							List[str]:
						super().__init__()
						__lowerCAmelCase	   =num_attention_heads
						__lowerCAmelCase	   =attention_head_dim
						__lowerCAmelCase	   =num_attention_heads * attention_head_dim
						__lowerCAmelCase	   =additional_embeddings

						__lowerCAmelCase	   =time_embed_dim or inner_dim
						__lowerCAmelCase	   =embedding_proj_dim or embedding_dim
						__lowerCAmelCase	   =clip_embed_dim or embedding_dim

						__lowerCAmelCase	   =Timesteps(snake_case_				,	snake_case_				,	0)
						__lowerCAmelCase	   =TimestepEmbedding(snake_case_				,	snake_case_				,	out_dim=snake_case_				,	act_fn=snake_case_)

						__lowerCAmelCase	   =nn.Linear(snake_case_				,	snake_case_)

						if embedding_proj_norm_type is None:
											__lowerCAmelCase	   =None
						elif embedding_proj_norm_type == "layer":
											__lowerCAmelCase	   =nn.LayerNorm(snake_case_)
						else:
											raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""")

						__lowerCAmelCase	   =nn.Linear(snake_case_				,	snake_case_)

						if encoder_hid_proj_type is None:
											__lowerCAmelCase	   =None
						elif encoder_hid_proj_type == "linear":
											__lowerCAmelCase	   =nn.Linear(snake_case_				,	snake_case_)
						else:
											raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""")

						__lowerCAmelCase	   =nn.Parameter(torch.zeros(1				,	num_embeddings + additional_embeddings				,	snake_case_))

						if added_emb_type == "prd":
											__lowerCAmelCase	   =nn.Parameter(torch.zeros(1				,	1				,	snake_case_))
						elif added_emb_type is None:
											__lowerCAmelCase	   =None
						else:
											raise ValueError(
											    F"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""")

						__lowerCAmelCase	   =nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            snake_case_				,	snake_case_				,	snake_case_				,	dropout=snake_case_				,	activation_fn="""gelu"""				,	attention_bias=snake_case_				,	)
						        for d in range(snake_case_)
						    ])

						if norm_in_type == "layer":
											__lowerCAmelCase	   =nn.LayerNorm(snake_case_)
						elif norm_in_type is None:
											__lowerCAmelCase	   =None
						else:
											raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}.""")

						__lowerCAmelCase	   =nn.LayerNorm(snake_case_)

						__lowerCAmelCase	   =nn.Linear(snake_case_				,	snake_case_)

						__lowerCAmelCase	   =torch.full(
						    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]				,	-1_00_00.0)
						causal_attention_mask.triu_(1)
						__lowerCAmelCase	   =causal_attention_mask[None, ...]
						self.register_buffer("""causal_attention_mask"""				,	snake_case_				,	persistent=snake_case_)

						__lowerCAmelCase	   =nn.Parameter(torch.zeros(1				,	snake_case_))
						__lowerCAmelCase	   =nn.Parameter(torch.zeros(1				,	snake_case_))



	@property
	# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
	def 		UpperCamelCase							(						self							:    Optional[int])->							Dict[str, AttentionProcessor]:
						__lowerCAmelCase	   ={}

						def fn_recursive_add_processors(snake_case_							:    str				,	snake_case_							:    torch.nn.Module				,	snake_case_							:    Dict[str, AttentionProcessor]):
											if hasattr(snake_case_				,	"""set_processor"""):
																__lowerCAmelCase	   =module.processor

											for sub_name, child in module.named_children():
																fn_recursive_add_processors(F"""{name}.{sub_name}"""				,	snake_case_				,	snake_case_)

											return processors

						for name, module in self.named_children():
											fn_recursive_add_processors(snake_case_				,	snake_case_				,	snake_case_)

						return processors



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Union[AttentionProcessor, Dict[str, AttentionProcessor]])->							Optional[Any]:
						__lowerCAmelCase	   =len(self.attn_processors.keys())

						if isinstance(snake_case_				,	snake_case_) and len(snake_case_) != count:
											raise ValueError(
											    F"""A dict of processors was passed, but the number of processors {len(snake_case_)} does not match the"""
											    F""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""")

						def fn_recursive_attn_processor(snake_case_							:    str				,	snake_case_							:    torch.nn.Module				,	snake_case_							:    Optional[int]):
											if hasattr(snake_case_				,	"""set_processor"""):
																if not isinstance(snake_case_				,	snake_case_):
																					module.set_processor(snake_case_)
																else:
																					module.set_processor(processor.pop(F"""{name}.processor"""))

											for sub_name, child in module.named_children():
																fn_recursive_attn_processor(F"""{name}.{sub_name}"""				,	snake_case_				,	snake_case_)

						for name, module in self.named_children():
											fn_recursive_attn_processor(snake_case_				,	snake_case_				,	snake_case_)



	def 		UpperCamelCase							(						self							:    str)->							List[Any]:
						self.set_attn_processor(AttnProcessor())



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    str				,	snake_case_							:    Union[torch.Tensor, float, int]				,	snake_case_							:    torch.FloatTensor				,	snake_case_							:    Optional[torch.FloatTensor] = None				,	snake_case_							:    Optional[torch.BoolTensor] = None				,	snake_case_							:    bool = True				,	)->							Any:
						__lowerCAmelCase	   =hidden_states.shape[0]

						__lowerCAmelCase	   =timestep
						if not torch.is_tensor(snake_case_):
											__lowerCAmelCase	   =torch.tensor([timesteps]				,	dtype=torch.long				,	device=hidden_states.device)
						elif torch.is_tensor(snake_case_) and len(timesteps.shape) == 0:
											__lowerCAmelCase	   =timesteps[None].to(hidden_states.device)

						# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
						__lowerCAmelCase	   =timesteps * torch.ones(snake_case_				,	dtype=timesteps.dtype				,	device=timesteps.device)

						__lowerCAmelCase	   =self.time_proj(snake_case_)

						# timesteps does not contain any weights and will always return f32 tensors
						# but time_embedding might be fp16, so we need to cast here.
						__lowerCAmelCase	   =timesteps_projected.to(dtype=self.dtype)
						__lowerCAmelCase	   =self.time_embedding(snake_case_)

						if self.embedding_proj_norm is not None:
											__lowerCAmelCase	   =self.embedding_proj_norm(snake_case_)

						__lowerCAmelCase	   =self.embedding_proj(snake_case_)
						if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
											__lowerCAmelCase	   =self.encoder_hidden_states_proj(snake_case_)
						elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
											raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set""")

						__lowerCAmelCase	   =self.proj_in(snake_case_)

						__lowerCAmelCase	   =self.positional_embedding.to(hidden_states.dtype)

						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =0

						if encoder_hidden_states is not None:
											additional_embeds.append(snake_case_)
											additional_embeddings_len += encoder_hidden_states.shape[1]

						if len(proj_embeddings.shape) == 2:
											__lowerCAmelCase	   =proj_embeddings[:, None, :]

						if len(hidden_states.shape) == 2:
											__lowerCAmelCase	   =hidden_states[:, None, :]

						__lowerCAmelCase	   =additional_embeds + [
						    proj_embeddings,
						    time_embeddings[:, None, :],
						    hidden_states,
						]

						if self.prd_embedding is not None:
											__lowerCAmelCase	   =self.prd_embedding.to(hidden_states.dtype).expand(snake_case_				,	-1				,	-1)
											additional_embeds.append(snake_case_)

						__lowerCAmelCase	   =torch.cat(
						    snake_case_				,	dim=1				,	)

						# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
						__lowerCAmelCase	   =additional_embeddings_len + proj_embeddings.shape[1] + 1
						if positional_embeddings.shape[1] < hidden_states.shape[1]:
											__lowerCAmelCase	   =F.pad(
											    snake_case_				,	(
											        0,
											        0,
											        additional_embeddings_len,
											        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
											    )				,	value=0.0				,	)

						__lowerCAmelCase	   =hidden_states + positional_embeddings

						if attention_mask is not None:
											__lowerCAmelCase	   =(1 - attention_mask.to(hidden_states.dtype)) * -1_00_00.0
											__lowerCAmelCase	   =F.pad(snake_case_				,	(0, self.additional_embeddings)				,	value=0.0)
											__lowerCAmelCase	   =(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
											__lowerCAmelCase	   =attention_mask.repeat_interleave(self.config.num_attention_heads				,	dim=0)

						if self.norm_in is not None:
											__lowerCAmelCase	   =self.norm_in(snake_case_)

						for block in self.transformer_blocks:
											__lowerCAmelCase	   =block(snake_case_				,	attention_mask=snake_case_)

						__lowerCAmelCase	   =self.norm_out(snake_case_)

						if self.prd_embedding is not None:
											__lowerCAmelCase	   =hidden_states[:, -1]
						else:
											__lowerCAmelCase	   =hidden_states[:, additional_embeddings_len:]

						__lowerCAmelCase	   =self.proj_to_clip_embeddings(snake_case_)

						if not return_dict:
											return (predicted_image_embedding,)

						return PriorTransformerOutput(predicted_image_embedding=snake_case_)



	def 		UpperCamelCase							(						self							:    List[str]				,	snake_case_							:    List[Any])->							int:
						__lowerCAmelCase	   =(prior_latents * self.clip_std) + self.clip_mean
						return prior_latents









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int = 3				,   __lowerCamelCase	:    int = 7				,   __lowerCamelCase	:    int = 1000000	)  ->						int:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =1

					for current_denominator in range(1				,   limit + 1	):
										__lowerCAmelCase	   =current_denominator * numerator // denominator
										if current_denominator % denominator == 0:
															current_numerator -= 1
										if current_numerator * max_denominator > current_denominator * max_numerator:
															__lowerCAmelCase	   =current_numerator
															__lowerCAmelCase	   =current_denominator
					return max_numerator


if __name__ == "__main__":
				print(solution(numerator=3, denominator=7, limit=1_00_00_00))




