






'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


lowerCamelCase_							      =      -1 if torch_device == '''cpu''' else 0

@is_pipeline_test
class      _UpperCAmelCase    (  unittest.TestCase		):




   """simple docstring"""

   snake_case					       =		MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   snake_case					       =		TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

   def 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			str	):





         '''simple docstring'''





         _A      =			SummarizationPipeline(model=__UpperCAmelCase   ,   tokenizer=__UpperCAmelCase	)
         return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			List[Any]   ,   __UpperCAmelCase				:			List[Any]	):





         '''simple docstring'''





         _A      =			summarizer.model

         _A      =			summarizer("(CNN)The Palestinian Authority officially became"	)
         self.assertEqual(__UpperCAmelCase   ,   [{"summary_text": ANY(__UpperCAmelCase	)}]	)

         _A      =			summarizer(
             "(CNN)The Palestinian Authority officially became "   ,   num_beams=2   ,   min_length=2   ,   max_length=5   ,   )
         self.assertEqual(__UpperCAmelCase   ,   [{"summary_text": ANY(__UpperCAmelCase	)}]	)

         # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
         _A      =			[
             "SwitchTransformersConfig",
             "T5Config",
             "LongT5Config",
             "LEDConfig",
             "PegasusXConfig",
             "FSMTConfig",
             "M2M100Config",
             "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
         ]
         if model.config.__class__.__name__ not in model_can_handle_longer_seq:
               # Too long and exception is expected.
               # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
               # the embedding layer.
               if not (
                   isinstance(__UpperCAmelCase   ,   __UpperCAmelCase	)
                   and get_gpu_count() > 0
                   and len(summarizer.model.trainable_weights	) > 0
               ):
                     with self.assertRaises(__UpperCAmelCase	):
                           _A      =			summarizer("This " * 1000	)
         _A      =			summarizer("This " * 1000   ,   truncation=TruncationStrategy.ONLY_FIRST	)

   @require_torch
   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         _A      =			pipeline(task="summarization"   ,   model="sshleifer/tiny-mbart"   ,   framework="pt"	)
         _A      =			summarizer("This is a small test"	)
         self.assertEqual(
             __UpperCAmelCase   ,   [
                 {
                     "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
                 }
             ]   ,   )

   @require_tf
   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         _A      =			pipeline(task="summarization"   ,   model="sshleifer/tiny-mbart"   ,   framework="tf"	)
         _A      =			summarizer("This is a small test"	)
         self.assertEqual(
             __UpperCAmelCase   ,   [
                 {
                     "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
                 }
             ]   ,   )

   @require_torch
   @slow
   def 		lowerCAmelCase				(	self				:			Any	):





         '''simple docstring'''





         _A      =			pipeline(task="summarization"   ,   device=__UpperCAmelCase	)
         _A      =			(
             " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
             " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
             " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
             " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
             " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
             " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
             " situation in Palestinian territories, paving the way for possible war crimes investigations against"
             " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
             " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
             " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
             " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
             " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
             " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
             " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
             " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
             " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
             " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
             " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
             " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
             " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
             " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
             " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
             " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
             " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
             " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
             " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
             " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
             " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
             " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
             " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
             " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
             " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
             " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
             " will include alleged war crimes committed since June. The International Criminal Court was set up in"
             " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
             " and Faith Karimi contributed to this report."
         )
         _A      =			(
             " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
             " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
             " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
             " says governments seeking to penalize Palestine should end pressure ."
         )
         _A      =			summarizer(__UpperCAmelCase	)
         self.assertEqual(result[0]["summary_text"]   ,   __UpperCAmelCase	)








'''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      _UpperCAmelCase    :




   """simple docstring"""

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





@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}		)
   snake_case					       =		field(
       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		)
   snake_case					       =		field(
       default=1_28							,					metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }							,					)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def       __lowercase		(    ) ->		Any:

      '''simple docstring'''


      _A      =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
      _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(__lowercase     ,						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     ,						)
      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"     ,						__lowercase )

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

      # 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=__lowercase     ,						idalabel=__lowercase     ,						labelaid={label: i for i, label in enumerate(__lowercase )}     ,						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     ,						)

      with training_args.strategy.scope():
            _A      =			TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path     ,						from_pt=bool(".bin" in model_args.model_name_or_path )     ,						config=__lowercase     ,						cache_dir=model_args.cache_dir     ,						)

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

            for i in range(__lowercase ):
                  for j in range(__lowercase ):
                        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(__lowercase ) -> Dict:
            _A  ,			_A      =			align_predictions(p.predictions     ,						p.label_ids )

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

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

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            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" )

            with open(__lowercase     ,						"w" ) as writer:
                  logger.info("***** Eval results *****" )

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

                  results.update(__lowercase )

    # Predict
      if training_args.do_predict:
            _A      =			TFTokenClassificationDataset(
                token_classification_task=__lowercase     ,						data_dir=data_args.data_dir     ,						tokenizer=__lowercase     ,						labels=__lowercase     ,						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(test_dataset.get_dataset() )
            _A  ,			_A      =			align_predictions(__lowercase     ,						__lowercase )
            _A      =			classification_report(__lowercase     ,						__lowercase )

            logger.info("\n%s"     ,						__lowercase )

            _A      =			os.path.join(training_args.output_dir     ,						"test_results.txt" )

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

            # Save predictions
            _A      =			os.path.join(training_args.output_dir     ,						"test_predictions.txt" )

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

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

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

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

      return results


if __name__ == "__main__":
    main()





