





# 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 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	torch.FloatTensor
						_UpperCamelCase  :	torch.FloatTensor



class 			lowercase				(							a_		,			a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Union[str, Any]								=	1







						@register_to_config
						def __init__(    self			:		Union[str, Any] ,				lowerCamelCase_			:		int = 20_00 ,				lowerCamelCase_			:		float = 0.15 ,				lowerCamelCase_			:		float = 0.01 ,				lowerCamelCase_			:		float = 1348.0 ,				lowerCamelCase_			:		float = 1e-5 ,				lowerCamelCase_			:		int = 1 ,				):





										'''simple docstring'''







										_snake_case :			Dict  						=   sigma_max

										# setable values
										_snake_case :			Union[str, Any]  						=   None

										self.set_sigmas(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		Optional[int] = None					):





										'''simple docstring'''







										return sample







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float = None ,				lowerCamelCase_			:		Union[str, torch.device] = None					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

										_snake_case :			Optional[int]  						=   torch.linspace(1 ,				lowerCamelCase_ ,				lowerCamelCase_ ,				device=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float = None ,				lowerCamelCase_			:		float = None ,				lowerCamelCase_			:		float = None					):





										'''simple docstring'''







										_snake_case :			Dict  						=   sigma_min if sigma_min is not None else self.config.sigma_min
										_snake_case :			Tuple  						=   sigma_max if sigma_max is not None else self.config.sigma_max
										_snake_case :			Optional[Any]  						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
										if self.timesteps is None:
														self.set_timesteps(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			List[Any]  						=   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
										_snake_case :			Tuple  						=   torch.exp(torch.linspace(math.log(lowerCamelCase_					) ,				math.log(lowerCamelCase_					) ,				lowerCamelCase_					)					)
										_snake_case :			List[Any]  						=   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]					)







						def 		__UpperCAmelCase       (    self			:		Tuple ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Optional[int]					):





										'''simple docstring'''







										return torch.where(
										    timesteps == 0 ,				torch.zeros_like(t.to(timesteps.device					)					) ,				self.discrete_sigmas[timesteps - 1].to(timesteps.device					) ,				)







						def 		__UpperCAmelCase       (    self			:		Tuple ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		Optional[torch.Generator] = None ,				lowerCamelCase_			:		bool = True ,				):





										'''simple docstring'''







										if self.timesteps is None:
														raise ValueError(
														    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

										_snake_case :			Tuple  						=   timestep * torch.ones(
										    sample.shape[0] ,				device=sample.device					)  # torch.repeat_interleave(timestep, sample.shape[0])
										_snake_case :			Dict  						=   (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
										_snake_case :			Optional[int]  						=   timesteps.to(self.discrete_sigmas.device					)

										_snake_case :			Tuple  						=   self.discrete_sigmas[timesteps].to(sample.device					)
										_snake_case :			Optional[Any]  						=   self.get_adjacent_sigma(lowerCamelCase_ ,				lowerCamelCase_					).to(sample.device					)
										_snake_case :			List[str]  						=   torch.zeros_like(lowerCamelCase_					)
										_snake_case :			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
										_snake_case :			Optional[int]  						=   diffusion.flatten()
										while len(diffusion.shape					) < len(sample.shape					):
														_snake_case :			List[Any]  						=   diffusion.unsqueeze(-1					)
										_snake_case :			Tuple  						=   drift - diffusion**2 * model_output

										#  equation 6: sample noise for the diffusion term of
										_snake_case :			Optional[int]  						=   randn_tensor(
										    sample.shape ,				layout=sample.layout ,				generator=lowerCamelCase_ ,				device=sample.device ,				dtype=sample.dtype					)
										_snake_case :			Tuple  						=   sample - drift  # subtract because `dt` is a small negative timestep
										# TODO is the variable diffusion the correct scaling term for the noise?
										_snake_case :			Union[str, Any]  						=   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=lowerCamelCase_ ,				prev_sample_mean=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		Optional[torch.Generator] = None ,				lowerCamelCase_			:		bool = True ,				):





										'''simple docstring'''







										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
										_snake_case :			Optional[Any]  						=   randn_tensor(sample.shape ,				layout=sample.layout ,				generator=lowerCamelCase_					).to(sample.device					)

										# compute step size from the model_output, the noise, and the snr
										_snake_case :			Optional[int]  						=   torch.norm(model_output.reshape(model_output.shape[0] ,				-1					) ,				dim=-1					).mean()
										_snake_case :			Any  						=   torch.norm(noise.reshape(noise.shape[0] ,				-1					) ,				dim=-1					).mean()
										_snake_case :			str  						=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
										_snake_case :			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
										_snake_case :			Tuple  						=   step_size.flatten()
										while len(step_size.shape					) < len(sample.shape					):
														_snake_case :			Dict  						=   step_size.unsqueeze(-1					)
										_snake_case :			Dict  						=   sample + step_size * model_output
										_snake_case :			Optional[Any]  						=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

										if not return_dict:
														return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		torch.FloatTensor ,				lowerCamelCase_			:		torch.FloatTensor ,				):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   timesteps.to(original_samples.device					)
										_snake_case :			Optional[Any]  						=   self.discrete_sigmas.to(original_samples.device					)[timesteps]
										_snake_case :			Dict  						=   (
										    noise * sigmas[:, None, None, None]
										    if noise is not None
										    else torch.randn_like(lowerCamelCase_					) * sigmas[:, None, None, None]
										)
										_snake_case :			int  						=   noise + original_samples
										return noisy_samples







						def __len__(    self			:		Dict					):





										'''simple docstring'''







										return self.config.num_train_timesteps













import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase_		:				Tuple				=						logging.getLogger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		Dict=-1					):





										'''simple docstring'''







										_snake_case :			str  						=   label_idx







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			Union[str, Any]  						=   mode.value
										_snake_case :			Optional[int]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Dict  						=   1
										_snake_case :			List[str]  						=   []
										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														_snake_case :			List[Any]  						=   []
														_snake_case :			int  						=   []
														for line in f:
																		if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																						if words:
																										examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																										guid_index += 1
																										_snake_case :			Optional[Any]  						=   []
																										_snake_case :			Union[str, Any]  						=   []
																		else:
																						_snake_case :			Tuple  						=   line.split(' '					)
																						words.append(splits[0]					)
																						if len(lowerCamelCase_					) > 1:
																										labels.append(splits[self.label_idx].replace('\n' ,				''					)					)
																						else:
																										# Examples could have no label for mode = "test"
																										labels.append('O'					)
														if words:
																		examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
										return examples







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			str  						=   0
										for line in test_input_reader:
														if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																		writer.write(lowerCamelCase_					)
																		if not preds_list[example_id]:
																						example_id += 1
														elif preds_list[example_id]:
																		_snake_case :			List[str]  						=   line.split()[0] + ' ' + preds_list[example_id].pop(0					) + '\n'
																		writer.write(lowerCamelCase_					)
														else:
																		logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.' ,				line.split()[0]					)







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			Optional[int]  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Optional[int]  						=   ['O'] + labels
														return labels
										else:
														return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Optional[int]					):





										'''simple docstring'''







										super().__init__(label_idx=-2					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			str  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Union[str, Any]  						=   ['O'] + labels
														return labels
										else:
														return [
														    "O",
														    "B-ADVP",
														    "B-INTJ",
														    "B-LST",
														    "B-PRT",
														    "B-NP",
														    "B-SBAR",
														    "B-VP",
														    "B-ADJP",
														    "B-CONJP",
														    "B-PP",
														    "I-ADVP",
														    "I-INTJ",
														    "I-LST",
														    "I-PRT",
														    "I-NP",
														    "I-SBAR",
														    "I-VP",
														    "I-ADJP",
														    "I-CONJP",
														    "I-PP",
														]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			str  						=   mode.value
										_snake_case :			List[str]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Tuple  						=   1
										_snake_case :			List[str]  						=   []

										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														for sentence in parse_incr(lowerCamelCase_					):
																		_snake_case :			List[str]  						=   []
																		_snake_case :			str  						=   []
																		for token in sentence:
																						words.append(token['form']					)
																						labels.append(token['upos']					)
																		assert len(lowerCamelCase_					) == len(lowerCamelCase_					)
																		if words:
																						examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																						guid_index += 1
										return examples







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			Dict  						=   0
										for sentence in parse_incr(lowerCamelCase_					):
														_snake_case :			Optional[int]  						=   preds_list[example_id]
														_snake_case :			List[Any]  						=   ''
														for token in sentence:
																		out += f'''{token['form']} ({token['upos']}|{s_p.pop(0					)}) '''
														out += "\n"
														writer.write(lowerCamelCase_					)
														example_id += 1







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		return f.read().splitlines()
										else:
														return [
														    "ADJ",
														    "ADP",
														    "ADV",
														    "AUX",
														    "CCONJ",
														    "DET",
														    "INTJ",
														    "NOUN",
														    "NUM",
														    "PART",
														    "PRON",
														    "PROPN",
														    "PUNCT",
														    "SCONJ",
														    "SYM",
														    "VERB",
														    "X",
														]






