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        a_				:






 '''simple docstring'''






 UpperCAmelCase_							=      field(
     metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}		)
 UpperCAmelCase_							=      field(
     default=SCREAMING_SNAKE_CASE ,  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=SCREAMING_SNAKE_CASE ,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}		)
 UpperCAmelCase_							=      field(default=SCREAMING_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.
 UpperCAmelCase_							=      field(
     default=SCREAMING_SNAKE_CASE ,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} ,  )






@dataclass
class        a_				:






 '''simple docstring'''






 UpperCAmelCase_							=      field(
     metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}		)
 UpperCAmelCase_							=      field(
     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=SCREAMING_SNAKE_CASE ,  metadata={'help': 'Overwrite the cached training and evaluation sets'}		)






def    __lowerCamelCase	(		):
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       lowerCAmelCase__  			=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
       lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__  			=      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.'			)

       lowerCAmelCase__  			=      import_module('tasks'			)

       try:
              lowerCAmelCase__  			=      getattr(lowerCAmelCase__			,						model_args.task_type			)
              lowerCAmelCase__  			=      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'			,						lowerCAmelCase__			)

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

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

       lowerCAmelCase__  			=      AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path			,						num_labels=lowerCAmelCase__			,						idalabel=lowerCAmelCase__			,						labelaid={label: i for i, label in enumerate(lowerCAmelCase__			)}			,						cache_dir=model_args.cache_dir			,						)
       lowerCAmelCase__  			=      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():
              lowerCAmelCase__  			=      TFAutoModelForTokenClassification.from_pretrained(
                  model_args.model_name_or_path			,						from_pt=bool('.bin' in model_args.model_name_or_path			)			,						config=lowerCAmelCase__			,						cache_dir=model_args.cache_dir			,						)

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

              for i in range(lowerCAmelCase__			):
                     for j in range(lowerCAmelCase__			):
                            if label_ids[i, j] != -1_0_0:
                                   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(lowerCAmelCase__			) -> Dict:
              lowerCAmelCase__			,      lowerCAmelCase__  			=      align_predictions(p.predictions			,						p.label_ids			)

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

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

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

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

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

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

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

                     results.update(lowerCAmelCase__			)

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

              lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__  			=      trainer.predict(test_dataset.get_dataset()			)
              lowerCAmelCase__			,      lowerCAmelCase__  			=      align_predictions(lowerCAmelCase__			,						lowerCAmelCase__			)
              lowerCAmelCase__  			=      classification_report(lowerCAmelCase__			,						lowerCAmelCase__			)

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

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

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

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

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

                            for line in f:
                                   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]:
                                          lowerCAmelCase__  			=      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]			)

       return results


if __name__ == "__main__":
    main()

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class        a_				(		unittest.TestCase		):






 '''simple docstring'''






 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      1
        lowerCAmelCase__  			=      3
        lowerCAmelCase__  			=      8
        lowerCAmelCase__  			=      8

        lowerCAmelCase__  			=      torch.rand((batch_size, num_channels, height, width))

        return sample




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Any):







        '''simple docstring'''



        if isinstance(image[0]    ,					PIL.Image.Image):
               return np.stack([np.array(lowercase__) for i in image]    ,					axis=0)
        elif isinstance(lowercase__    ,					torch.Tensor):
               return image.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)
        return image




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      self.to_np(lowercase__)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(
                   image_processor.preprocess(lowercase__)    ,					output_type=lowercase__    ,					)
               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (input_np * 255).round() if output_type == 'pil' else input_np
               assert (
                   np.abs(in_np - out_np).max() < 1e-6
               ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)
        lowerCAmelCase__  			=      self.dummy_sample.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(image_processor.preprocess(lowercase__)    ,					output_type=lowercase__)

               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (input_np * 255).round() if output_type == 'pil' else input_np
               assert (
                   np.abs(in_np - out_np).max() < 1e-6
               ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)
        lowerCAmelCase__  			=      image_processor.numpy_to_pil(lowercase__)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(image_processor.preprocess(lowercase__)    ,					output_type=lowercase__)
               for i, o in zip(lowercase__    ,					lowercase__):
                      lowerCAmelCase__  			=      np.array(lowercase__)
                      lowerCAmelCase__  			=      self.to_np(lowercase__) if output_type == 'pil' else (self.to_np(lowercase__) * 255).round()
                      assert (
                          np.abs(in_np - out_np).max() < 1e-6
                      ), F"""decoded output does not match input for output_type {output_type}"""




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      input_pt_ad.squeeze(0)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      self.to_np(self.dummy_sample)
        lowerCAmelCase__  			=      input_np_ad.squeeze(0)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        assert np.abs(out_pt_ad - out_pt_ad).max() < 1e-6
        assert np.abs(out_np_ad - out_np_ad).max() < 1e-6




 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      self.to_np(self.dummy_sample)
        list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        assert np.abs(out_pt_ad - out_pt_list).max() < 1e-6
        assert np.abs(out_np_ad - out_np_list).max() < 1e-6
