import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class   lowercase			(   a   ):
							lowercase__  :				torch.FloatTensor
							lowercase__  :				torch.FloatTensor





def 		__lowerCamelCase							(UpperCAmelCase__   :							Tuple					,       UpperCAmelCase__   :							Tuple=0.999					,       UpperCAmelCase__   :							Optional[Any]="cosine"					,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(UpperCAmelCase__   :							str     ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCAmelCase__   :							List[str]     ):
															return math.exp(t * -12.0     )

					else:
										raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"     )

					SCREAMING_SNAKE_CASE				=				[]
					for i in range(UpperCAmelCase__     ):
										SCREAMING_SNAKE_CASE				=				i / num_diffusion_timesteps
										SCREAMING_SNAKE_CASE				=				(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(UpperCAmelCase__     ) / alpha_bar_fn(UpperCAmelCase__     )					,       UpperCAmelCase__     )     )
					return torch.tensor(UpperCAmelCase__					,       dtype=torch.floataa     )


class   lowercase			(   a ,				a   ):
							lowercase__  :				List[Any]									=							1




							@register_to_config
							def __init__(  self      :    Dict		,							_UpperCamelCase      :    int = 1_000		,							_UpperCamelCase      :    float = 0.0_0_0_1		,							_UpperCamelCase      :    float = 0.0_2		,							_UpperCamelCase      :    str = "linear"		,							_UpperCamelCase      :    float = 0.0		,							_UpperCamelCase      :    Optional[np.ndarray] = None		,							_UpperCamelCase      :    bool = True		,							)    -> Tuple:



												'''simple docstring'''

												if trained_betas is not None:
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(_UpperCamelCase					)
												elif beta_schedule == "linear":
																	SCREAMING_SNAKE_CASE				=				torch.linspace(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.floataa					)
												elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	SCREAMING_SNAKE_CASE				=				(
																	    torch.linspace(beta_start**0.5		,							beta_end**0.5		,							_UpperCamelCase		,							dtype=torch.floataa					) ** 2
																	)
												elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	SCREAMING_SNAKE_CASE				=				betas_for_alpha_bar(_UpperCamelCase					)
												elif beta_schedule == "sigmoid":
																	# GeoDiff sigmoid schedule
																	SCREAMING_SNAKE_CASE				=				torch.linspace(-6		,							6		,							_UpperCamelCase					)
																	SCREAMING_SNAKE_CASE				=				torch.sigmoid(_UpperCamelCase					) * (beta_end - beta_start) + beta_start
												else:
																	raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"					)

												SCREAMING_SNAKE_CASE				=				1.0 - self.betas
												SCREAMING_SNAKE_CASE				=				torch.cumprod(self.alphas		,							dim=0					)
												SCREAMING_SNAKE_CASE				=				torch.tensor(1.0					)

												SCREAMING_SNAKE_CASE				=				torch.tensor(1.0					)

												# standard deviation of the initial noise distribution
												SCREAMING_SNAKE_CASE				=				1.0

												# setable values
												SCREAMING_SNAKE_CASE				=				None
												SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.arange(0		,							_UpperCamelCase					)[::-1].copy()					)

												SCREAMING_SNAKE_CASE				=				eta




							def 		__snake_case(  self      :    List[str]		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    Optional[int] = None					)    -> torch.FloatTensor:



												'''simple docstring'''

												return sample




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    int = 10		,							_UpperCamelCase      :    int = 10		,							_UpperCamelCase      :    Union[str, torch.device] = None		,							)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				min(self.config.num_train_timesteps		,							_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				num_inference_steps

												SCREAMING_SNAKE_CASE				=				[]

												SCREAMING_SNAKE_CASE				=				{}
												for j in range(0		,							num_inference_steps - jump_length		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				jump_n_sample - 1

												SCREAMING_SNAKE_CASE				=				num_inference_steps
												while t >= 1:
																	SCREAMING_SNAKE_CASE				=				t - 1
																	timesteps.append(_UpperCamelCase					)

																	if jumps.get(_UpperCamelCase		,							0					) > 0:
																						SCREAMING_SNAKE_CASE				=				jumps[t] - 1
																						for _ in range(_UpperCamelCase					):
																											SCREAMING_SNAKE_CASE				=				t + 1
																											timesteps.append(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				np.array(_UpperCamelCase					) * (self.config.num_train_timesteps // self.num_inference_steps)
												SCREAMING_SNAKE_CASE				=				torch.from_numpy(_UpperCamelCase					).to(_UpperCamelCase					)




							def 		__snake_case(  self      :    int		,							_UpperCamelCase      :    Union[str, Any]					)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				t - self.config.num_train_timesteps // self.num_inference_steps

												SCREAMING_SNAKE_CASE				=				self.alphas_cumprod[t]
												SCREAMING_SNAKE_CASE				=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												SCREAMING_SNAKE_CASE				=				1 - alpha_prod_t
												SCREAMING_SNAKE_CASE				=				1 - alpha_prod_t_prev

												# For t > 0, compute predicted variance βt (see formula (6) and (7) from
												# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
												# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
												# variance to pred_sample
												# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
												# without eta.
												# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
												SCREAMING_SNAKE_CASE				=				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance




							def 		__snake_case(  self      :    Union[str, Any]		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    Optional[torch.Generator] = None		,							_UpperCamelCase      :    bool = True		,							)    -> Union[RePaintSchedulerOutput, Tuple]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				timestep
												SCREAMING_SNAKE_CASE				=				timestep - self.config.num_train_timesteps // self.num_inference_steps

												# 1. compute alphas, betas
												SCREAMING_SNAKE_CASE				=				self.alphas_cumprod[t]
												SCREAMING_SNAKE_CASE				=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												SCREAMING_SNAKE_CASE				=				1 - alpha_prod_t

												# 2. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
												SCREAMING_SNAKE_CASE				=				(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

												# 3. Clip "predicted x_0"
												if self.config.clip_sample:
																	SCREAMING_SNAKE_CASE				=				torch.clamp(_UpperCamelCase		,							-1		,							1					)

												# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
												# substitute formula (7) in the algorithm coming from DDPM paper
												# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
												# DDIM schedule gives the same results as DDPM with eta = 1.0
												# Noise is being reused in 7. and 8., but no impact on quality has
												# been observed.

												# 5. Add noise
												SCREAMING_SNAKE_CASE				=				model_output.device
												SCREAMING_SNAKE_CASE				=				randn_tensor(model_output.shape		,							generator=_UpperCamelCase		,							device=_UpperCamelCase		,							dtype=model_output.dtype					)
												SCREAMING_SNAKE_CASE				=				self.eta * self._get_variance(_UpperCamelCase					) ** 0.5

												SCREAMING_SNAKE_CASE				=				0
												if t > 0 and self.eta > 0:
																	SCREAMING_SNAKE_CASE				=				std_dev_t * noise

												# 6. compute "direction pointing to x_t" of formula (12)
												# from https://arxiv.org/pdf/2010.02502.pdf
												SCREAMING_SNAKE_CASE				=				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

												# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												SCREAMING_SNAKE_CASE				=				alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

												# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
												SCREAMING_SNAKE_CASE				=				(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

												# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
												SCREAMING_SNAKE_CASE				=				mask * prev_known_part + (1.0 - mask) * prev_unknown_part

												if not return_dict:
																	return (
																	    pred_prev_sample,
																	    pred_original_sample,
																	)

												return RePaintSchedulerOutput(prev_sample=_UpperCamelCase		,							pred_original_sample=_UpperCamelCase					)




							def 		__snake_case(  self      :    List[Any]		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Union[str, Any]		,							_UpperCamelCase      :    int=None					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.config.num_train_timesteps // self.num_inference_steps

												for i in range(_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				self.betas[timestep + i]
																	if sample.device.type == "mps":
																						# randn does not work reproducibly on mps
																						SCREAMING_SNAKE_CASE				=				randn_tensor(sample.shape		,							dtype=sample.dtype		,							generator=_UpperCamelCase					)
																						SCREAMING_SNAKE_CASE				=				noise.to(sample.device					)
																	else:
																						SCREAMING_SNAKE_CASE				=				randn_tensor(sample.shape		,							generator=_UpperCamelCase		,							device=sample.device		,							dtype=sample.dtype					)

																	# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
																	SCREAMING_SNAKE_CASE				=				(1 - beta) ** 0.5 * sample + beta**0.5 * noise

												return sample




							def 		__snake_case(  self      :    List[str]		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    torch.FloatTensor		,							_UpperCamelCase      :    torch.IntTensor		,							)    -> torch.FloatTensor:



												'''simple docstring'''

												raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."					)





							def __len__(  self      :    List[Any]					)    -> List[str]:



												'''simple docstring'''

												return self.config.num_train_timesteps





from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
							import pyximport

							pyximport.install(setup_args={'''include_dirs''': np.get_include()})
							from . import algos_graphormer  # noqa E402





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							int = 5_1_2     ):
					SCREAMING_SNAKE_CASE				=				x.shape[1] if len(x.shape     ) > 1 else 1
					SCREAMING_SNAKE_CASE				=				1 + np.arange(0					,       feature_num * offset					,       UpperCAmelCase__					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				x + feature_offset
					return x





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[Any]=True     ):
					requires_backends(UpperCAmelCase__					,       ["cython"]     )

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_attr"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((len(item["edge_index"][0]     ), 1)					,       dtype=np.intaa     )  # same embedding for all

					if keep_features and "node_feat" in item.keys():  # input_nodes
										SCREAMING_SNAKE_CASE				=				np.asarray(item["node_feat"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((item["num_nodes"], 1)					,       dtype=np.intaa     )  # same embedding for all

					SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_index"]					,       dtype=np.intaa     )

					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1
					SCREAMING_SNAKE_CASE				=				item["num_nodes"]

					if len(edge_attr.shape     ) == 1:
										SCREAMING_SNAKE_CASE				=				edge_attr[:, None]
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes]					,       dtype=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				True

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				algos_graphormer.floyd_warshall(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.amax(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				algos_graphormer.gen_edge_input(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes + 1, num_nodes + 1]					,       dtype=np.single     )  # with graph token

					# combine
					SCREAMING_SNAKE_CASE				=				input_nodes + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				attn_bias
					SCREAMING_SNAKE_CASE				=				attn_edge_type
					SCREAMING_SNAKE_CASE				=				shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				np.sum(UpperCAmelCase__					,       axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				item["in_degree"]  # for undirected graph
					SCREAMING_SNAKE_CASE				=				input_edges + 1  # we shift all indices by one for padding
					if "labels" not in item:
										SCREAMING_SNAKE_CASE				=				item["y"]

					return item


class   lowercase			:




							def __init__(  self      :    int		,							_UpperCamelCase      :    Any=20		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												if not is_cython_available():
																	raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

												SCREAMING_SNAKE_CASE				=				spatial_pos_max
												SCREAMING_SNAKE_CASE				=				on_the_fly_processing





							def __call__(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[dict]					)    -> Dict[str, Any]:



												'''simple docstring'''

												if self.on_the_fly_processing:
																	SCREAMING_SNAKE_CASE				=				[preprocess_item(_UpperCamelCase					) for i in features]

												if not isinstance(features[0]		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				[vars(_UpperCamelCase					) for f in features]
												SCREAMING_SNAKE_CASE				=				{}

												SCREAMING_SNAKE_CASE				=				max(len(i["input_nodes"]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_nodes"][0]					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["attn_edge_type"][0][0]					)
												SCREAMING_SNAKE_CASE				=				max(len(i["input_edges"][0][0]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_edges"][0][0][0]					)
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							max_node_num + 1		,							max_node_num + 1		,							dtype=torch.float					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(
												    _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)

												for ix, f in enumerate(_UpperCamelCase					):
																	for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																						SCREAMING_SNAKE_CASE				=				torch.tensor(f[k]					)

																	if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
																						SCREAMING_SNAKE_CASE				=				float("-inf"					)

																	SCREAMING_SNAKE_CASE				=				f["attn_bias"]
																	SCREAMING_SNAKE_CASE				=				f[
																	    "attn_edge_type"
																	]
																	SCREAMING_SNAKE_CASE				=				f["spatial_pos"]
																	SCREAMING_SNAKE_CASE				=				f["in_degree"]
																	SCREAMING_SNAKE_CASE				=				f["input_nodes"]
																	SCREAMING_SNAKE_CASE				=				f["input_edges"]

												SCREAMING_SNAKE_CASE				=				batch["in_degree"]

												SCREAMING_SNAKE_CASE				=				features[0]["labels"]
												if len(_UpperCamelCase					) == 1:  # one task
																	if isinstance(sample[0]		,							_UpperCamelCase					):  # regression
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
																	else:  # binary classification
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
												else:  # multi task classification, left to float to keep the NaNs
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.stack([i["labels"] for i in features]		,							axis=0					)					)

												return batch




