





"""simple docstring"""






import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


lowerCAmelCase_      =				logging.getLogger(__name__)



@dataclass
class 						lowerCAmelCase       :
       lowerCAmelCase__    			=	field(
           metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
       lowerCAmelCase__    			=	field(
           default=snake_case      ,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
       lowerCAmelCase__    			=	field(
           default="""NER"""      ,						metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}				)
       lowerCAmelCase__    			=	field(
           default=snake_case      ,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
       lowerCAmelCase__    			=	field(default=snake_case      ,						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.
       lowerCAmelCase__    			=	field(
           default=snake_case      ,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,						)



@dataclass
class 						lowerCAmelCase       :
       lowerCAmelCase__    			=	field(
           metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}				)
       lowerCAmelCase__    			=	field(
           metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}				)
       lowerCAmelCase__    			=	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."""
               )
           }      ,						)
       lowerCAmelCase__    			=	field(
           default=snake_case      ,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)


def    __lowerCamelCase     (							)		->							int:






       """simple docstring"""




       _UpperCAmelCase			     =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
       _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase			     =  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.'				)

       _UpperCAmelCase			     =  import_module('tasks'				)

       try:
              _UpperCAmelCase			     =  getattr(SCREAMING_SNAKE_CASE,model_args.task_type				)
              _UpperCAmelCase			     =  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,)
       logger.info(
           'n_replicas: %s, distributed training: %s, 16-bits training: %s',training_args.n_replicas,bool(training_args.n_replicas > 1				),training_args.fpaa,)
       logger.info('Training/evaluation parameters %s',SCREAMING_SNAKE_CASE				)

       # Prepare Token Classification task
       _UpperCAmelCase			     =  token_classification_task.get_labels(data_args.labels				)
       _UpperCAmelCase			     =  dict(enumerate(SCREAMING_SNAKE_CASE				)				)
       _UpperCAmelCase			     =  len(SCREAMING_SNAKE_CASE				)

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

       _UpperCAmelCase			     =  AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path,num_labels=SCREAMING_SNAKE_CASE,idalabel=SCREAMING_SNAKE_CASE,labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE				)},cache_dir=model_args.cache_dir,)
       _UpperCAmelCase			     =  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,)

       with training_args.strategy.scope():
              _UpperCAmelCase			     =  TFAutoModelForTokenClassification.from_pretrained(
                  model_args.model_name_or_path,from_pt=bool('.bin' in model_args.model_name_or_path				),config=SCREAMING_SNAKE_CASE,cache_dir=model_args.cache_dir,)

       # Get datasets
       _UpperCAmelCase			     =  (
           TFTokenClassificationDataset(
               token_classification_task=SCREAMING_SNAKE_CASE,data_dir=data_args.data_dir,tokenizer=SCREAMING_SNAKE_CASE,labels=SCREAMING_SNAKE_CASE,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
       )
       _UpperCAmelCase			     =  (
           TFTokenClassificationDataset(
               token_classification_task=SCREAMING_SNAKE_CASE,data_dir=data_args.data_dir,tokenizer=SCREAMING_SNAKE_CASE,labels=SCREAMING_SNAKE_CASE,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(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				) -> Tuple[List[int], List[int]]:
              _UpperCAmelCase			     =  np.argmax(SCREAMING_SNAKE_CASE,axis=2				)
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  preds.shape
              _UpperCAmelCase			     =  [[] for _ in range(SCREAMING_SNAKE_CASE				)]
              _UpperCAmelCase			     =  [[] for _ in range(SCREAMING_SNAKE_CASE				)]

              for i in range(SCREAMING_SNAKE_CASE				):
                     for j in range(SCREAMING_SNAKE_CASE				):
                            if label_ids[i, j] != -100:
                                   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(SCREAMING_SNAKE_CASE				) -> Dict:
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  align_predictions(p.predictions,p.label_ids				)

              return {
                  "precision": precision_score(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				),
                  "recall": recall_score(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				),
                  "f1": fa_score(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				),
              }

       # Initialize our Trainer
       _UpperCAmelCase			     =  TFTrainer(
           model=SCREAMING_SNAKE_CASE,args=SCREAMING_SNAKE_CASE,train_dataset=train_dataset.get_dataset() if train_dataset else None,eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,compute_metrics=SCREAMING_SNAKE_CASE,)

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              tokenizer.save_pretrained(training_args.output_dir				)

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

              _UpperCAmelCase			     =  trainer.evaluate()
              _UpperCAmelCase			     =  os.path.join(training_args.output_dir,'eval_results.txt'				)

              with open(SCREAMING_SNAKE_CASE,'w'				) as writer:
                     logger.info('***** Eval results *****'				)

                     for key, value in result.items():
                            logger.info('  %s = %s',SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)
                            writer.write('%s = %s\n' % (key, value)				)

                     results.update(SCREAMING_SNAKE_CASE				)

    # Predict
       if training_args.do_predict:
              _UpperCAmelCase			     =  TFTokenClassificationDataset(
                  token_classification_task=SCREAMING_SNAKE_CASE,data_dir=data_args.data_dir,tokenizer=SCREAMING_SNAKE_CASE,labels=SCREAMING_SNAKE_CASE,model_type=config.model_type,max_seq_length=data_args.max_seq_length,overwrite_cache=data_args.overwrite_cache,mode=Split.test,)

              _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase			     =  trainer.predict(test_dataset.get_dataset()				)
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  align_predictions(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)
              _UpperCAmelCase			     =  classification_report(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)

              logger.info('\n%s',SCREAMING_SNAKE_CASE				)

              _UpperCAmelCase			     =  os.path.join(training_args.output_dir,'test_results.txt'				)

              with open(SCREAMING_SNAKE_CASE,'w'				) as writer:
                     writer.write('%s\n' % report				)

              # Save predictions
              _UpperCAmelCase			     =  os.path.join(training_args.output_dir,'test_predictions.txt'				)

              with open(SCREAMING_SNAKE_CASE,'w'				) as writer:
                     with open(os.path.join(data_args.data_dir,'test.txt'				),'r'				) as f:
                            _UpperCAmelCase			     =  0

                            for line in f:
                                   if line.startswith('-DOCSTART-'				) or line == "" or line == "\n":
                                          writer.write(SCREAMING_SNAKE_CASE				)

                                          if not preds_list[example_id]:
                                                 example_id += 1
                                   elif preds_list[example_id]:
                                          _UpperCAmelCase			     =  line.split()[0] + ' ' + preds_list[example_id].pop(0				) + '\n'

                                          writer.write(SCREAMING_SNAKE_CASE				)
                                   else:
                                          logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.',line.split()[0]				)

       return results


if __name__ == "__main__":
    main()













"""simple docstring"""






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	(LMSDiscreteScheduler,)
       lowerCAmelCase__    			=	10






       def 				__A			( self     ,	**a__  ):
              _UpperCAmelCase			     =  {
                  'num_train_timesteps': 11_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
              }

              config.update(**a__  )
              return config






       def 				__A			( self  ):
              for timesteps in [10, 50, 1_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=a__  )






       def 				__A			( self  ):
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,	[0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=a__     ,	beta_end=a__  )






       def 				__A			( self  ):
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=a__  )






       def 				__A			( self  ):
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=a__  )






       def 				__A			( self  ):
              for t in [0, 5_00, 8_00]:
                     self.check_over_forward(time_step=a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config(prediction_type='v_prediction'  )
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1E-2
              assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              _UpperCAmelCase			     =  sample.to(a__  )

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__     ,	use_karras_sigmas=a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
              _UpperCAmelCase			     =  sample.to(a__  )

              for t in scheduler.timesteps:
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2E-2
              assert abs(result_mean.item() - 4.9_648  ) < 1E-3






