





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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



@dataclass
class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						42
							snake_case_							 =						42







class    _a     (				lowercase__					,							lowercase__							):






							"""simple docstring"""

							snake_case_							 =						1

							@register_to_config
							def __init__(      self				:     Dict							,	a				:     int = 20_00							,	a				:     float = 0.15							,	a				:     float = 0.01							,	a				:     float = 1348.0							,	a				:     float = 1E-5							,	a				:     int = 1							,	)	->int:
													# standard deviation of the initial noise distribution
													SCREAMING_SNAKE_CASE__     :				Any   =					sigma_max

													# setable values
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					None

													self.set_sigmas(a							,	a							,	a							,	a    )

							def      A_  (      self				:     Any							,	a				:     torch.FloatTensor							,	a				:     Optional[int] = None    )	->torch.FloatTensor:
													return sample

							def      A_  (      self				:     Tuple							,	a				:     int							,	a				:     float = None							,	a				:     Union[str, torch.device] = None    )	->str:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

													SCREAMING_SNAKE_CASE__     :				str   =					torch.linspace(1							,	a							,	a							,	device=a    )

							def      A_  (      self				:     Optional[Any]							,	a				:     int							,	a				:     float = None							,	a				:     float = None							,	a				:     float = None    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					sigma_min if sigma_min is not None else self.config.sigma_min
													SCREAMING_SNAKE_CASE__     :				List[str]   =					sigma_max if sigma_max is not None else self.config.sigma_max
													SCREAMING_SNAKE_CASE__     :				Tuple   =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
													if self.timesteps is None:
																			self.set_timesteps(a							,	a    )

													SCREAMING_SNAKE_CASE__     :				Tuple   =					sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.exp(torch.linspace(math.log(a    )							,	math.log(a    )							,	a    )    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )

							def      A_  (      self				:     Optional[Any]							,	a				:     str							,	a				:     Tuple    )	->Dict:
													return torch.where(
													    timesteps == 0							,	torch.zeros_like(t.to(timesteps.device    )    )							,	self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,	)

							def      A_  (      self				:     Optional[int]							,	a				:     torch.FloatTensor							,	a				:     int							,	a				:     torch.FloatTensor							,	a				:     Optional[torch.Generator] = None							,	a				:     bool = True							,	)	->Union[SdeVeOutput, Tuple]:
													if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

													SCREAMING_SNAKE_CASE__     :				Tuple   =					timestep * torch.ones(
													    sample.shape[0]							,	device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
													SCREAMING_SNAKE_CASE__     :				str   =					(timestep * (len(self.timesteps    ) - 1)).long()

													# mps requires indices to be in the same device, so we use cpu as is the default with cuda
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					timesteps.to(self.discrete_sigmas.device    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.discrete_sigmas[timesteps].to(sample.device    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.get_adjacent_sigma(a							,	a    ).to(sample.device    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.zeros_like(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					(sigma**2 - adjacent_sigma**2) ** 0.5

													# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
													# also equation 47 shows the analog from SDE models to ancestral sampling methods
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					diffusion.flatten()
													while len(diffusion.shape    ) < len(sample.shape    ):
																			SCREAMING_SNAKE_CASE__     :				Any   =					diffusion.unsqueeze(-1    )
													SCREAMING_SNAKE_CASE__     :				Any   =					drift - diffusion**2 * model_output

													#  equation 6: sample noise for the diffusion term of
													SCREAMING_SNAKE_CASE__     :				Tuple   =					randn_tensor(
													    sample.shape							,	layout=sample.layout							,	generator=a							,	device=sample.device							,	dtype=sample.dtype    )
													SCREAMING_SNAKE_CASE__     :				str   =					sample - drift  # subtract because `dt` is a small negative timestep
													# TODO is the variable diffusion the correct scaling term for the noise?
													SCREAMING_SNAKE_CASE__     :				str   =					prev_sample_mean + diffusion * noise  # add impact of diffusion field g

													if not return_dict:
																			return (prev_sample, prev_sample_mean)

													return SdeVeOutput(prev_sample=a							,	prev_sample_mean=a    )

							def      A_  (      self				:     Optional[Any]							,	a				:     torch.FloatTensor							,	a				:     torch.FloatTensor							,	a				:     Optional[torch.Generator] = None							,	a				:     bool = True							,	)	->Union[SchedulerOutput, Tuple]:
													if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

													# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
													# sample noise for correction
													SCREAMING_SNAKE_CASE__     :				str   =					randn_tensor(sample.shape							,	layout=sample.layout							,	generator=a    ).to(sample.device    )

													# compute step size from the model_output, the noise, and the snr
													SCREAMING_SNAKE_CASE__     :				int   =					torch.norm(model_output.reshape(model_output.shape[0]							,	-1    )							,	dim=-1    ).mean()
													SCREAMING_SNAKE_CASE__     :				Any   =					torch.norm(noise.reshape(noise.shape[0]							,	-1    )							,	dim=-1    ).mean()
													SCREAMING_SNAKE_CASE__     :				Tuple   =					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
													# self.repeat_scalar(step_size, sample.shape[0])

													# compute corrected sample: model_output term and noise term
													SCREAMING_SNAKE_CASE__     :				Tuple   =					step_size.flatten()
													while len(step_size.shape    ) < len(sample.shape    ):
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					step_size.unsqueeze(-1    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					sample + step_size * model_output
													SCREAMING_SNAKE_CASE__     :				int   =					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

													if not return_dict:
																			return (prev_sample,)

													return SchedulerOutput(prev_sample=a    )

							def      A_  (      self				:     Tuple							,	a				:     torch.FloatTensor							,	a				:     torch.FloatTensor							,	a				:     torch.FloatTensor							,	)	->torch.FloatTensor:
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					timesteps.to(original_samples.device    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.discrete_sigmas.to(original_samples.device    )[timesteps]
													SCREAMING_SNAKE_CASE__     :				Any   =					(
													    noise * sigmas[:, None, None, None]
													    if noise is not None
													    else torch.randn_like(a    ) * sigmas[:, None, None, None]
													)
													SCREAMING_SNAKE_CASE__     :				int   =					noise + original_samples
													return noisy_samples






							def __len__(      self				:     Dict    )	->Any:
													return self.config.num_train_timesteps






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
				from ...onnx.config import PatchingSpec
				from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowercase       :List[Any]       =  logging.get_logger(__name__)

__lowercase       :Optional[int]       =  {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"longformer"

							def __init__(      self				:     List[str]							,	a				:     Union[List[int], int] = 5_12							,	a				:     int = 2							,	a				:     int = 1							,	a				:     int = 0							,	a				:     int = 2							,	a				:     int = 3_05_22							,	a				:     int = 7_68							,	a				:     int = 12							,	a				:     int = 12							,	a				:     int = 30_72							,	a				:     str = "gelu"							,	a				:     float = 0.1							,	a				:     float = 0.1							,	a				:     int = 5_12							,	a				:     int = 2							,	a				:     float = 0.02							,	a				:     float = 1E-12							,	a				:     bool = False							,	**a				:     Dict							,	)	->Tuple:
													super().__init__(pad_token_id=a							,	**a    )

													SCREAMING_SNAKE_CASE__     :				int   =					attention_window
													SCREAMING_SNAKE_CASE__     :				Any   =					sep_token_id
													SCREAMING_SNAKE_CASE__     :				str   =					bos_token_id
													SCREAMING_SNAKE_CASE__     :				List[str]   =					eos_token_id
													SCREAMING_SNAKE_CASE__     :				List[str]   =					vocab_size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					hidden_size
													SCREAMING_SNAKE_CASE__     :				List[str]   =					num_hidden_layers
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					num_attention_heads
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					hidden_act
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					intermediate_size
													SCREAMING_SNAKE_CASE__     :				List[str]   =					hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :				Dict   =					attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					max_position_embeddings
													SCREAMING_SNAKE_CASE__     :				str   =					type_vocab_size
													SCREAMING_SNAKE_CASE__     :				Any   =					initializer_range
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					layer_norm_eps
													SCREAMING_SNAKE_CASE__     :				Any   =					onnx_export







class    _a     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     int							,	a				:     "PretrainedConfig"							,	a				:     str = "default"							,	a				:     "List[PatchingSpec]" = None    )	->str:
													super().__init__(a							,	a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					True

							@property
							def      A_  (      self				:     int    )	->Mapping[str, Mapping[int, str]]:
													if self.task == "multiple-choice":
																			SCREAMING_SNAKE_CASE__     :				int   =					{0: "batch", 1: "choice", 2: "sequence"}
													else:
																			SCREAMING_SNAKE_CASE__     :				str   =					{0: "batch", 1: "sequence"}
													return OrderedDict(
													    [
													        ("input_ids", dynamic_axis),
													        ("attention_mask", dynamic_axis),
													        ("global_attention_mask", dynamic_axis),
													    ]    )

							@property
							def      A_  (      self				:     Optional[Any]    )	->Mapping[str, Mapping[int, str]]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					super().outputs
													if self.task == "default":
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					{0: "batch"}
													return outputs

							@property
							def      A_  (      self				:     str    )	->float:
													return 1E-4

							@property
							def      A_  (      self				:     Any    )	->int:
													# needs to be >= 14 to support tril operator
													return max(super().default_onnx_opset							,	14    )






							def      A_  (      self				:     str							,	a				:     "PreTrainedTokenizerBase"							,	a				:     int = -1							,	a				:     int = -1							,	a				:     bool = False							,	a				:     Optional[TensorType] = None							,	)	->Mapping[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					super().generate_dummy_inputs(
													    preprocessor=a							,	batch_size=a							,	seq_length=a							,	is_pair=a							,	framework=a    )
													import torch

													# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
													# makes the export fail randomly
													SCREAMING_SNAKE_CASE__     :				Any   =					torch.zeros_like(inputs["input_ids"]    )
													# make every second token global
													SCREAMING_SNAKE_CASE__     :				str   =					1

													return inputs