
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=0.999 ,       SCREAMING_SNAKE_CASE__="cosine" ,       )  ->							Optional[int]:


		'''simple docstring'''


		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(SCREAMING_SNAKE_CASE__			):
						return math.exp(t * -12.0			)

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

		snake_case  :       Tuple     			=						[]
		for i in range(SCREAMING_SNAKE_CASE__			):
				snake_case  :       str     			=						i / num_diffusion_timesteps
				snake_case  :       Optional[Any]     			=						(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__			) / alpha_bar_fn(SCREAMING_SNAKE_CASE__			) ,       SCREAMING_SNAKE_CASE__			)			)
		return torch.tensor(SCREAMING_SNAKE_CASE__ ,       dtype=torch.floataa			)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						[e.name for e in KarrasDiffusionSchedulers]
		lowerCamelCase     							=						2



		@register_to_config
		def __init__(      self				:			Optional[int]  ,	UpperCamelCase__				:			int = 1000  ,	UpperCamelCase__				:			float = 0.00_085  ,	UpperCamelCase__				:			float = 0.012  ,	UpperCamelCase__				:			str = "linear"  ,	UpperCamelCase__				:			Optional[Union[np.ndarray, List[float]]] = None  ,	UpperCamelCase__				:			str = "epsilon"  ,	UpperCamelCase__				:			str = "linspace"  ,	UpperCamelCase__				:			int = 0  ,	)						->			str:






				"""simple docstring"""







				if trained_betas is not None:
						snake_case  :       List[Any]     			=						torch.tensor(UpperCamelCase__  ,	dtype=torch.floataa      )
				elif beta_schedule == "linear":
						snake_case  :       Dict     			=						torch.linspace(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	dtype=torch.floataa      )
				elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						snake_case  :       str     			=						(
						    torch.linspace(beta_start**0.5  ,	beta_end**0.5  ,	UpperCamelCase__  ,	dtype=torch.floataa      ) ** 2
						)
				elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						snake_case  :       Any     			=						betas_for_alpha_bar(UpperCamelCase__      )
				else:
						raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'      )

				snake_case  :       Optional[int]     			=						1.0 - self.betas
				snake_case  :       Tuple     			=						torch.cumprod(self.alphas  ,	dim=0      )

				#  set all values
				self.set_timesteps(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			Any=None      )						->			Any:






				"""simple docstring"""







				if schedule_timesteps is None:
						snake_case  :       List[str]     			=						self.timesteps

				snake_case  :       Dict     			=						(schedule_timesteps == timestep).nonzero()

				# The sigma index that is taken for the **very** first `step`
				# is always the second index (or the last index if there is only 1)
				# This way we can ensure we don't accidentally skip a sigma in
				# case we start in the middle of the denoising schedule (e.g. for image-to-image)
				if len(self._index_counter      ) == 0:
						snake_case  :       List[Any]     			=						1 if len(UpperCamelCase__      ) > 1 else 0
				else:
						snake_case  :       str     			=						timestep.cpu().item() if torch.is_tensor(UpperCamelCase__      ) else timestep
						snake_case  :       int     			=						self._index_counter[timestep_int]

				return indices[pos].item()



		@property
		def       lowerCAmelCase				(      self				:			Dict      )						->			Optional[int]:






				"""simple docstring"""







				if self.config.timestep_spacing in ["linspace", "trailing"]:
						return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			Union[float, torch.FloatTensor]  ,	)						->			int:






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.index_for_timestep(UpperCamelCase__      )

				if self.state_in_first_order:
						snake_case  :       Optional[Any]     			=						self.sigmas[step_index]
				else:
						snake_case  :       Optional[int]     			=						self.sigmas_interpol[step_index]

				snake_case  :       List[Any]     			=						sample / ((sigma**2 + 1) ** 0.5)
				return sample



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Union[str, torch.device] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	)						->			str:






				"""simple docstring"""







				snake_case  :       Tuple     			=						num_inference_steps

				snake_case  :       Dict     			=						num_train_timesteps or self.config.num_train_timesteps

				# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
				if self.config.timestep_spacing == "linspace":
						snake_case  :       List[Any]     			=						np.linspace(0  ,	num_train_timesteps - 1  ,	UpperCamelCase__  ,	dtype=UpperCamelCase__      )[::-1].copy()
				elif self.config.timestep_spacing == "leading":
						snake_case  :       Union[str, Any]     			=						num_train_timesteps // self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						snake_case  :       Optional[Any]     			=						(np.arange(0  ,	UpperCamelCase__      ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase__      )
						timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
						snake_case  :       Union[str, Any]     			=						num_train_timesteps / self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						snake_case  :       str     			=						(np.arange(UpperCamelCase__  ,	0  ,	-step_ratio      )).round().copy().astype(UpperCamelCase__      )
						timesteps -= 1
				else:
						raise ValueError(
						    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'      )

				snake_case  :       Optional[int]     			=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
				snake_case  :       List[str]     			=						torch.from_numpy(np.log(UpperCamelCase__      )      ).to(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						np.interp(UpperCamelCase__  ,	np.arange(0  ,	len(UpperCamelCase__      )      )  ,	UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
				snake_case  :       int     			=						torch.from_numpy(UpperCamelCase__      ).to(device=UpperCamelCase__      )

				# interpolate sigmas
				snake_case  :       Any     			=						sigmas.log().lerp(sigmas.roll(1      ).log()  ,	0.5      ).exp()

				snake_case  :       str     			=						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
				snake_case  :       Tuple     			=						torch.cat(
				    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )

				if str(UpperCamelCase__      ).startswith('''mps'''      ):
						# mps does not support float64
						snake_case  :       str     			=						torch.from_numpy(UpperCamelCase__      ).to(UpperCamelCase__  ,	dtype=torch.floataa      )
				else:
						snake_case  :       Tuple     			=						torch.from_numpy(UpperCamelCase__      ).to(UpperCamelCase__      )

				# interpolate timesteps
				snake_case  :       Tuple     			=						self.sigma_to_t(UpperCamelCase__      ).to(UpperCamelCase__  ,	dtype=timesteps.dtype      )
				snake_case  :       Tuple     			=						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,	dim=-1      ).flatten()

				snake_case  :       Dict     			=						torch.cat([timesteps[:1], interleaved_timesteps]      )

				snake_case  :       List[str]     			=						None

				# for exp beta schedules, such as the one for `pipeline_shap_e.py`
				# we need an index counter
				snake_case  :       int     			=						defaultdict(UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			Optional[int]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						sigma.log()

				# get distribution
				snake_case  :       Optional[int]     			=						log_sigma - self.log_sigmas[:, None]

				# get sigmas range
				snake_case  :       List[Any]     			=						dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
				snake_case  :       Dict     			=						low_idx + 1

				snake_case  :       Union[str, Any]     			=						self.log_sigmas[low_idx]
				snake_case  :       Optional[int]     			=						self.log_sigmas[high_idx]

				# interpolate sigmas
				snake_case  :       Optional[Any]     			=						(low - log_sigma) / (low - high)
				snake_case  :       Dict     			=						w.clamp(0  ,	1      )

				# transform interpolation to time range
				snake_case  :       Any     			=						(1 - w) * low_idx + w * high_idx
				snake_case  :       Optional[Any]     			=						t.view(sigma.shape      )
				return t



		@property
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			List[Any]:






				"""simple docstring"""







				return self.sample is None



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			Union[torch.FloatTensor, np.ndarray]  ,	UpperCamelCase__				:			Union[float, torch.FloatTensor]  ,	UpperCamelCase__				:			Union[torch.FloatTensor, np.ndarray]  ,	UpperCamelCase__				:			bool = True  ,	)						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.index_for_timestep(UpperCamelCase__      )

				# advance index counter by 1
				snake_case  :       Dict     			=						timestep.cpu().item() if torch.is_tensor(UpperCamelCase__      ) else timestep
				self._index_counter[timestep_int] += 1

				if self.state_in_first_order:
						snake_case  :       Tuple     			=						self.sigmas[step_index]
						snake_case  :       Dict     			=						self.sigmas_interpol[step_index + 1]
						snake_case  :       List[Any]     			=						self.sigmas[step_index + 1]
				else:
						# 2nd order / KDPM2's method
						snake_case  :       str     			=						self.sigmas[step_index - 1]
						snake_case  :       Tuple     			=						self.sigmas_interpol[step_index]
						snake_case  :       Optional[Any]     			=						self.sigmas[step_index]

				# currently only gamma=0 is supported. This usually works best anyways.
				# We can support gamma in the future but then need to scale the timestep before
				# passing it to the model which requires a change in API
				snake_case  :       int     			=						0
				snake_case  :       Any     			=						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
						snake_case  :       List[Any]     			=						sigma_hat if self.state_in_first_order else sigma_interpol
						snake_case  :       List[str]     			=						sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
						snake_case  :       List[Any]     			=						sigma_hat if self.state_in_first_order else sigma_interpol
						snake_case  :       List[Any]     			=						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
						    sample / (sigma_input**2 + 1)
						)
				elif self.config.prediction_type == "sample":
						raise NotImplementedError('''prediction_type not implemented yet: sample'''      )
				else:
						raise ValueError(
						    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

				if self.state_in_first_order:
						# 2. Convert to an ODE derivative for 1st order
						snake_case  :       Tuple     			=						(sample - pred_original_sample) / sigma_hat
						# 3. delta timestep
						snake_case  :       str     			=						sigma_interpol - sigma_hat

						# store for 2nd order step
						snake_case  :       Optional[int]     			=						sample
				else:
						# DPM-Solver-2
						# 2. Convert to an ODE derivative for 2nd order
						snake_case  :       Optional[Any]     			=						(sample - pred_original_sample) / sigma_interpol

						# 3. delta timestep
						snake_case  :       List[str]     			=						sigma_next - sigma_hat

						snake_case  :       List[str]     			=						self.sample
						snake_case  :       Optional[int]     			=						None

				snake_case  :       List[str]     			=						sample + derivative * dt

				if not return_dict:
						return (prev_sample,)

				return SchedulerOutput(prev_sample=UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			torch.FloatTensor  ,	UpperCamelCase__				:			torch.FloatTensor  ,	)						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						self.sigmas.to(device=original_samples.device  ,	dtype=original_samples.dtype      )
				if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase__      ):
						# mps does not support float64
						snake_case  :       Optional[Any]     			=						self.timesteps.to(original_samples.device  ,	dtype=torch.floataa      )
						snake_case  :       Optional[int]     			=						timesteps.to(original_samples.device  ,	dtype=torch.floataa      )
				else:
						snake_case  :       int     			=						self.timesteps.to(original_samples.device      )
						snake_case  :       List[Any]     			=						timesteps.to(original_samples.device      )

				snake_case  :       Dict     			=						[self.index_for_timestep(UpperCamelCase__  ,	UpperCamelCase__      ) for t in timesteps]

				snake_case  :       Optional[Any]     			=						sigmas[step_indices].flatten()
				while len(sigma.shape      ) < len(original_samples.shape      ):
						snake_case  :       List[Any]     			=						sigma.unsqueeze(-1      )

				snake_case  :       Union[str, Any]     			=						original_samples + noise * sigma
				return noisy_samples



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






				"""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


SCREAMING_SNAKE_CASE__     					=			logging.getLogger(__name__)



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Optional[int]   ,  UpperCAmelCase				:     Any=-1):
				# in NER datasets, the last column is usually reserved for NER label
				SCREAMING_SNAKE_CASE_				:Tuple				     =						label_idx






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						1
				SCREAMING_SNAKE_CASE_				:str				     =						[]
				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
						SCREAMING_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=UpperCAmelCase   ,  labels=UpperCAmelCase))
												guid_index += 1
												SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
												SCREAMING_SNAKE_CASE_				:Any				     =						[]
								else:
										SCREAMING_SNAKE_CASE_				:int				     =						line.split(" ")
										words.append(splits[0])
										if len(UpperCAmelCase) > 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=UpperCAmelCase   ,  labels=UpperCAmelCase))
				return examples






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







		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Any				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						["O"] + labels
						return labels
				else:
						return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Dict):
				# in CONLL2003 dataset chunk column is second-to-last
				super().__init__(label_idx=-2)







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Dict				     =						["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 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:List[str]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Dict				     =						1
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[]

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






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						0
				for sentence in parse_incr(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						preds_list[example_id]
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						""
						for token in sentence:
								out += F"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
						out += "\n"
						writer.write(UpperCAmelCase)
						example_id += 1







		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "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",
						]






