



# 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		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42



class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__	):
   _UpperCAmelCase     	=			1

   @register_to_config
   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       int = 2000							,   __lowerCAmelCase			:       float = 0.1_5							,   __lowerCAmelCase			:       float = 0.0_1							,   __lowerCAmelCase			:       float = 1348.0							,   __lowerCAmelCase			:       float = 1E-5							,   __lowerCAmelCase			:       int = 1							,   )       ->				Optional[Any]:


    """simple docstring"""






    a						=		sigma_max

    # setable values
    a						=		None

    self.set_sigmas(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							)

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       Optional[int] = None							)       ->				torch.FloatTensor:


    """simple docstring"""






    return sample

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       float = None							,   __lowerCAmelCase			:       Union[str, torch.device] = None							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		sampling_eps if sampling_eps is not None else self.config.sampling_eps

    a						=		torch.linspace(1							,   __lowerCAmelCase							,   __lowerCAmelCase							,   device=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       float = None							,   __lowerCAmelCase			:       float = None							,   __lowerCAmelCase			:       float = None							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		sigma_min if sigma_min is not None else self.config.sigma_min
    a						=		sigma_max if sigma_max is not None else self.config.sigma_max
    a						=		sampling_eps if sampling_eps is not None else self.config.sampling_eps
    if self.timesteps is None:
     self.set_timesteps(__lowerCAmelCase							,   __lowerCAmelCase							)

    a						=		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
    a						=		torch.exp(torch.linspace(math.log(__lowerCAmelCase							)							,   math.log(__lowerCAmelCase							)							,   __lowerCAmelCase							)							)
    a						=		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Optional[Any]							)       ->				str:


    """simple docstring"""






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

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       Optional[torch.Generator] = None							,   __lowerCAmelCase			:       bool = True							,   )       ->				Union[SdeVeOutput, Tuple]:


    """simple docstring"""






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

    a						=		timestep * torch.ones(
        sample.shape[0]							,   device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
    a						=		(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
    a						=		timesteps.to(self.discrete_sigmas.device							)

    a						=		self.discrete_sigmas[timesteps].to(sample.device							)
    a						=		self.get_adjacent_sigma(__lowerCAmelCase							,   __lowerCAmelCase							).to(sample.device							)
    a						=		torch.zeros_like(__lowerCAmelCase							)
    a						=		(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
    a						=		diffusion.flatten()
    while len(diffusion.shape							) < len(sample.shape							):
     a						=		diffusion.unsqueeze(-1							)
    a						=		drift - diffusion**2 * model_output

    #  equation 6: sample noise for the diffusion term of
    a						=		randn_tensor(
        sample.shape							,   layout=sample.layout							,   generator=__lowerCAmelCase							,   device=sample.device							,   dtype=sample.dtype							)
    a						=		sample - drift  # subtract because `dt` is a small negative timestep
    # TODO is the variable diffusion the correct scaling term for the noise?
    a						=		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 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       Optional[torch.Generator] = None							,   __lowerCAmelCase			:       bool = True							,   )       ->				Union[SchedulerOutput, Tuple]:


    """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
    a						=		randn_tensor(sample.shape							,   layout=sample.layout							,   generator=__lowerCAmelCase							).to(sample.device							)

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

    if not return_dict:
     return (prev_sample,)

    return SchedulerOutput(prev_sample=__lowerCAmelCase							)

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       torch.FloatTensor							,   __lowerCAmelCase			:       torch.FloatTensor							,   )       ->				torch.FloatTensor:


    """simple docstring"""






    a						=		timesteps.to(original_samples.device							)
    a						=		self.discrete_sigmas.to(original_samples.device							)[timesteps]
    a						=		(
        noise * sigmas[:, None, None, None]
        if noise is not None
        else torch.randn_like(__lowerCAmelCase							) * sigmas[:, None, None, None]
    )
    a						=		noise + original_samples
    return noisy_samples




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


    """simple docstring"""






    return self.config.num_train_timesteps










import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A_		:     int        =     logging.getLogger(__name__)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
   _UpperCAmelCase     	=			field(
       default='''NER''',   metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
   _UpperCAmelCase     	=			field(default=UpperCAmelCase__,   metadata={'''help''': '''Set this flag to use fast tokenization.'''}	)
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},   )



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''},   )
   _UpperCAmelCase     	=			field(
       default=128,   metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       },   )
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
 if len(sys.argv					) == 2 and sys.argv[1].endswith(".json"					):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  a		,  a		,  a						=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  a		,  a		,  a						=		parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir					)
     and os.listdir(training_args.output_dir					)
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      " --overwrite_output_dir to overcome."					)

 a						=		import_module("tasks"					)
 try:
  a						=		getattr(UpperCAmelCase__ ,					model_args.task_type					)
  a						=		token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
      F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""					)

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,					datefmt="%m/%d/%Y %H:%M:%S" ,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,					)
 logger.warning(
     "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" ,					training_args.local_rank ,					training_args.device ,					training_args.n_gpu ,					bool(training_args.local_rank != -1					) ,					training_args.fpaa ,					)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank					):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("Training/evaluation parameters %s" ,					UpperCAmelCase__					)

 # Set seed
 set_seed(training_args.seed					)

 # Prepare CONLL-2003 task
 a						=		token_classification_task.get_labels(data_args.labels					)
 a						=		dict(enumerate(UpperCAmelCase__					)					)
 a						=		len(UpperCAmelCase__					)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 a						=		AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path ,					num_labels=UpperCAmelCase__ ,					idalabel=UpperCAmelCase__ ,					labelaid={label: i for i, label in enumerate(UpperCAmelCase__					)} ,					cache_dir=model_args.cache_dir ,					)
 a						=		AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,					cache_dir=model_args.cache_dir ,					use_fast=model_args.use_fast ,					)
 a						=		AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path ,					from_tf=bool(".ckpt" in model_args.model_name_or_path					) ,					config=UpperCAmelCase__ ,					cache_dir=model_args.cache_dir ,					)

 # Get datasets
 a						=		(
     TokenClassificationDataset(
         token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					model_type=config.model_type ,					max_seq_length=data_args.max_seq_length ,					overwrite_cache=data_args.overwrite_cache ,					mode=Split.train ,					)
     if training_args.do_train
     else None
 )
 a						=		(
     TokenClassificationDataset(
         token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					model_type=config.model_type ,					max_seq_length=data_args.max_seq_length ,					overwrite_cache=data_args.overwrite_cache ,					mode=Split.dev ,					)
     if training_args.do_eval
     else None
 )

 def align_predictions(UpperCAmelCase__ :np.ndarray ,					UpperCAmelCase__ :np.ndarray					) -> Tuple[List[int], List[int]]:
  a						=		np.argmax(UpperCAmelCase__ ,					axis=2					)

  a		,  a						=		preds.shape

  a						=		[[] for _ in range(UpperCAmelCase__					)]
  a						=		[[] for _ in range(UpperCAmelCase__					)]

  for i in range(UpperCAmelCase__					):
   for j in range(UpperCAmelCase__					):
    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
     out_label_list[i].append(label_map[label_ids[i][j]]					)
     preds_list[i].append(label_map[preds[i][j]]					)

  return preds_list, out_label_list

 def compute_metrics(UpperCAmelCase__ :EvalPrediction					) -> Dict:
  a		,  a						=		align_predictions(p.predictions ,					p.label_ids					)
  return {
      "accuracy_score": accuracy_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
      "precision": precision_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
      "recall": recall_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
      "f1": fa_score(UpperCAmelCase__ ,					UpperCAmelCase__					),
  }

 # Data collator
 a						=		DataCollatorWithPadding(UpperCAmelCase__ ,					pad_to_multiple_of=8					) if training_args.fpaa else None

 # Initialize our Trainer
 a						=		Trainer(
     model=UpperCAmelCase__ ,					args=UpperCAmelCase__ ,					train_dataset=UpperCAmelCase__ ,					eval_dataset=UpperCAmelCase__ ,					compute_metrics=UpperCAmelCase__ ,					data_collator=UpperCAmelCase__ ,					)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_process_zero():
   tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
 a						=		{}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"					)

  a						=		trainer.evaluate()

  a						=		os.path.join(training_args.output_dir ,					"eval_results.txt"					)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase__ ,					"w"					) as writer:
    logger.info("***** Eval results *****"					)
    for key, value in result.items():
     logger.info("  %s = %s" ,					UpperCAmelCase__ ,					UpperCAmelCase__					)
     writer.write("%s = %s\n" % (key, value)					)

   results.update(UpperCAmelCase__					)

    # Predict
 if training_args.do_predict:
  a						=		TokenClassificationDataset(
      token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					model_type=config.model_type ,					max_seq_length=data_args.max_seq_length ,					overwrite_cache=data_args.overwrite_cache ,					mode=Split.test ,					)

  a		,  a		,  a						=		trainer.predict(UpperCAmelCase__					)
  a		,  a						=		align_predictions(UpperCAmelCase__ ,					UpperCAmelCase__					)

  a						=		os.path.join(training_args.output_dir ,					"test_results.txt"					)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase__ ,					"w"					) as writer:
    for key, value in metrics.items():
     logger.info("  %s = %s" ,					UpperCAmelCase__ ,					UpperCAmelCase__					)
     writer.write("%s = %s\n" % (key, value)					)

        # Save predictions
  a						=		os.path.join(training_args.output_dir ,					"test_predictions.txt"					)
  if trainer.is_world_process_zero():
   with open(UpperCAmelCase__ ,					"w"					) as writer:
    with open(os.path.join(data_args.data_dir ,					"test.txt"					) ,					"r"					) as f:
     token_classification_task.write_predictions_to_file(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 return results


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple					):






 '''simple docstring'''

 main()


if __name__ == "__main__":
     main()

