"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


a				={
    """Acehnese Arabic""": """ace_Arab""",
    """Acehnese Latin""": """ace_Latn""",
    """Mesopotamian Arabic""": """acm_Arab""",
    """Ta'izzi-Adeni Arabic""": """acq_Arab""",
    """Tunisian Arabic""": """aeb_Arab""",
    """Afrikaans""": """afr_Latn""",
    """South Levantine Arabic""": """ajp_Arab""",
    """Akan""": """aka_Latn""",
    """Amharic""": """amh_Ethi""",
    """North Levantine Arabic""": """apc_Arab""",
    """Modern Standard Arabic""": """arb_Arab""",
    """Modern Standard Arabic Romanized""": """arb_Latn""",
    """Najdi Arabic""": """ars_Arab""",
    """Moroccan Arabic""": """ary_Arab""",
    """Egyptian Arabic""": """arz_Arab""",
    """Assamese""": """asm_Beng""",
    """Asturian""": """ast_Latn""",
    """Awadhi""": """awa_Deva""",
    """Central Aymara""": """ayr_Latn""",
    """South Azerbaijani""": """azb_Arab""",
    """North Azerbaijani""": """azj_Latn""",
    """Bashkir""": """bak_Cyrl""",
    """Bambara""": """bam_Latn""",
    """Balinese""": """ban_Latn""",
    """Belarusian""": """bel_Cyrl""",
    """Bemba""": """bem_Latn""",
    """Bengali""": """ben_Beng""",
    """Bhojpuri""": """bho_Deva""",
    """Banjar Arabic""": """bjn_Arab""",
    """Banjar Latin""": """bjn_Latn""",
    """Standard Tibetan""": """bod_Tibt""",
    """Bosnian""": """bos_Latn""",
    """Buginese""": """bug_Latn""",
    """Bulgarian""": """bul_Cyrl""",
    """Catalan""": """cat_Latn""",
    """Cebuano""": """ceb_Latn""",
    """Czech""": """ces_Latn""",
    """Chokwe""": """cjk_Latn""",
    """Central Kurdish""": """ckb_Arab""",
    """Crimean Tatar""": """crh_Latn""",
    """Welsh""": """cym_Latn""",
    """Danish""": """dan_Latn""",
    """German""": """deu_Latn""",
    """Southwestern Dinka""": """dik_Latn""",
    """Dyula""": """dyu_Latn""",
    """Dzongkha""": """dzo_Tibt""",
    """Greek""": """ell_Grek""",
    """English""": """eng_Latn""",
    """Esperanto""": """epo_Latn""",
    """Estonian""": """est_Latn""",
    """Basque""": """eus_Latn""",
    """Ewe""": """ewe_Latn""",
    """Faroese""": """fao_Latn""",
    """Fijian""": """fij_Latn""",
    """Finnish""": """fin_Latn""",
    """Fon""": """fon_Latn""",
    """French""": """fra_Latn""",
    """Friulian""": """fur_Latn""",
    """Nigerian Fulfulde""": """fuv_Latn""",
    """Scottish Gaelic""": """gla_Latn""",
    """Irish""": """gle_Latn""",
    """Galician""": """glg_Latn""",
    """Guarani""": """grn_Latn""",
    """Gujarati""": """guj_Gujr""",
    """Haitian Creole""": """hat_Latn""",
    """Hausa""": """hau_Latn""",
    """Hebrew""": """heb_Hebr""",
    """Hindi""": """hin_Deva""",
    """Chhattisgarhi""": """hne_Deva""",
    """Croatian""": """hrv_Latn""",
    """Hungarian""": """hun_Latn""",
    """Armenian""": """hye_Armn""",
    """Igbo""": """ibo_Latn""",
    """Ilocano""": """ilo_Latn""",
    """Indonesian""": """ind_Latn""",
    """Icelandic""": """isl_Latn""",
    """Italian""": """ita_Latn""",
    """Javanese""": """jav_Latn""",
    """Japanese""": """jpn_Jpan""",
    """Kabyle""": """kab_Latn""",
    """Jingpho""": """kac_Latn""",
    """Kamba""": """kam_Latn""",
    """Kannada""": """kan_Knda""",
    """Kashmiri Arabic""": """kas_Arab""",
    """Kashmiri Devanagari""": """kas_Deva""",
    """Georgian""": """kat_Geor""",
    """Central Kanuri Arabic""": """knc_Arab""",
    """Central Kanuri Latin""": """knc_Latn""",
    """Kazakh""": """kaz_Cyrl""",
    """Kabiyè""": """kbp_Latn""",
    """Kabuverdianu""": """kea_Latn""",
    """Khmer""": """khm_Khmr""",
    """Kikuyu""": """kik_Latn""",
    """Kinyarwanda""": """kin_Latn""",
    """Kyrgyz""": """kir_Cyrl""",
    """Kimbundu""": """kmb_Latn""",
    """Northern Kurdish""": """kmr_Latn""",
    """Kikongo""": """kon_Latn""",
    """Korean""": """kor_Hang""",
    """Lao""": """lao_Laoo""",
    """Ligurian""": """lij_Latn""",
    """Limburgish""": """lim_Latn""",
    """Lingala""": """lin_Latn""",
    """Lithuanian""": """lit_Latn""",
    """Lombard""": """lmo_Latn""",
    """Latgalian""": """ltg_Latn""",
    """Luxembourgish""": """ltz_Latn""",
    """Luba-Kasai""": """lua_Latn""",
    """Ganda""": """lug_Latn""",
    """Luo""": """luo_Latn""",
    """Mizo""": """lus_Latn""",
    """Standard Latvian""": """lvs_Latn""",
    """Magahi""": """mag_Deva""",
    """Maithili""": """mai_Deva""",
    """Malayalam""": """mal_Mlym""",
    """Marathi""": """mar_Deva""",
    """Minangkabau Arabic """: """min_Arab""",
    """Minangkabau Latin""": """min_Latn""",
    """Macedonian""": """mkd_Cyrl""",
    """Plateau Malagasy""": """plt_Latn""",
    """Maltese""": """mlt_Latn""",
    """Meitei Bengali""": """mni_Beng""",
    """Halh Mongolian""": """khk_Cyrl""",
    """Mossi""": """mos_Latn""",
    """Maori""": """mri_Latn""",
    """Burmese""": """mya_Mymr""",
    """Dutch""": """nld_Latn""",
    """Norwegian Nynorsk""": """nno_Latn""",
    """Norwegian Bokmål""": """nob_Latn""",
    """Nepali""": """npi_Deva""",
    """Northern Sotho""": """nso_Latn""",
    """Nuer""": """nus_Latn""",
    """Nyanja""": """nya_Latn""",
    """Occitan""": """oci_Latn""",
    """West Central Oromo""": """gaz_Latn""",
    """Odia""": """ory_Orya""",
    """Pangasinan""": """pag_Latn""",
    """Eastern Panjabi""": """pan_Guru""",
    """Papiamento""": """pap_Latn""",
    """Western Persian""": """pes_Arab""",
    """Polish""": """pol_Latn""",
    """Portuguese""": """por_Latn""",
    """Dari""": """prs_Arab""",
    """Southern Pashto""": """pbt_Arab""",
    """Ayacucho Quechua""": """quy_Latn""",
    """Romanian""": """ron_Latn""",
    """Rundi""": """run_Latn""",
    """Russian""": """rus_Cyrl""",
    """Sango""": """sag_Latn""",
    """Sanskrit""": """san_Deva""",
    """Santali""": """sat_Olck""",
    """Sicilian""": """scn_Latn""",
    """Shan""": """shn_Mymr""",
    """Sinhala""": """sin_Sinh""",
    """Slovak""": """slk_Latn""",
    """Slovenian""": """slv_Latn""",
    """Samoan""": """smo_Latn""",
    """Shona""": """sna_Latn""",
    """Sindhi""": """snd_Arab""",
    """Somali""": """som_Latn""",
    """Southern Sotho""": """sot_Latn""",
    """Spanish""": """spa_Latn""",
    """Tosk Albanian""": """als_Latn""",
    """Sardinian""": """srd_Latn""",
    """Serbian""": """srp_Cyrl""",
    """Swati""": """ssw_Latn""",
    """Sundanese""": """sun_Latn""",
    """Swedish""": """swe_Latn""",
    """Swahili""": """swh_Latn""",
    """Silesian""": """szl_Latn""",
    """Tamil""": """tam_Taml""",
    """Tatar""": """tat_Cyrl""",
    """Telugu""": """tel_Telu""",
    """Tajik""": """tgk_Cyrl""",
    """Tagalog""": """tgl_Latn""",
    """Thai""": """tha_Thai""",
    """Tigrinya""": """tir_Ethi""",
    """Tamasheq Latin""": """taq_Latn""",
    """Tamasheq Tifinagh""": """taq_Tfng""",
    """Tok Pisin""": """tpi_Latn""",
    """Tswana""": """tsn_Latn""",
    """Tsonga""": """tso_Latn""",
    """Turkmen""": """tuk_Latn""",
    """Tumbuka""": """tum_Latn""",
    """Turkish""": """tur_Latn""",
    """Twi""": """twi_Latn""",
    """Central Atlas Tamazight""": """tzm_Tfng""",
    """Uyghur""": """uig_Arab""",
    """Ukrainian""": """ukr_Cyrl""",
    """Umbundu""": """umb_Latn""",
    """Urdu""": """urd_Arab""",
    """Northern Uzbek""": """uzn_Latn""",
    """Venetian""": """vec_Latn""",
    """Vietnamese""": """vie_Latn""",
    """Waray""": """war_Latn""",
    """Wolof""": """wol_Latn""",
    """Xhosa""": """xho_Latn""",
    """Eastern Yiddish""": """ydd_Hebr""",
    """Yoruba""": """yor_Latn""",
    """Yue Chinese""": """yue_Hant""",
    """Chinese Simplified""": """zho_Hans""",
    """Chinese Traditional""": """zho_Hant""",
    """Standard Malay""": """zsm_Latn""",
    """Zulu""": """zul_Latn""",
}






class   __UpperCAmelCase		(      __SCREAMING_SNAKE_CASE      ):
				A__ :  str    				=			"""facebook/nllb-200-distilled-600M"""
				A__ :  Optional[Any]    				=			(
				    """This is a tool that translates text from a language to another. It takes three inputs: `text`, which should """
				    """be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, """
				    """which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in """
				    """plain English, such as 'Romanian', or 'Albanian'. It returns the text translated in `tgt_lang`."""
				)
				A__ :  Union[str, Any]    				=			"""translator"""
				A__ :  List[str]    				=			AutoTokenizer
				A__ :  Any    				=			AutoModelForSeqaSeqLM
				A__ :  Tuple    				=			LANGUAGE_CODES

				A__ :  str    				=			["""text""", """text""", """text"""]
				A__ :  Tuple    				=			["""text"""]

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):

										if src_lang not in self.lang_to_code:
																raise ValueError(F'''{src_lang} is not a supported language.''' )
										if tgt_lang not in self.lang_to_code:
																raise ValueError(F'''{tgt_lang} is not a supported language.''' )
										lowerCamelCase__   =self.lang_to_code[src_lang]
										lowerCamelCase__   =self.lang_to_code[tgt_lang]
										return self.pre_processor._build_translation_inputs(
										    A_      ,					return_tensors="pt"      ,					src_lang=A_      ,					tgt_lang=A_ )

				def 						_a      ( self      ,					_lowerCamelCase ):
										return self.model.generate(**A_ )






				def 						_a      ( self      ,					_lowerCamelCase ):
										return self.post_processor.decode(outputs[0].tolist()      ,					skip_special_tokens=A_ )









import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


_A					:				List[str]				=			logging.getLogger(__name__)



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	"""token-classification"""



    def __init__(       self   ,				A_     ):





        '''simple docstring'''

        if type(A_     ) == dict:
            SCREAMING_SNAKE_CASE__											=			Namespace(**A_     )
        SCREAMING_SNAKE_CASE__											=			import_module('''tasks'''     )
        try:
            SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				hparams.task_type     )
            SCREAMING_SNAKE_CASE__											=			token_classification_task_clazz()
        except AttributeError:
            raise ValueError(
                f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
                f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''     )
        SCREAMING_SNAKE_CASE__											=			self.token_classification_task.get_labels(hparams.labels     )
        SCREAMING_SNAKE_CASE__											=			CrossEntropyLoss().ignore_index
        super().__init__(A_   ,				len(self.labels     )   ,				self.mode     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        return self.model(**A_     )



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            SCREAMING_SNAKE_CASE__											=			(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids

        SCREAMING_SNAKE_CASE__											=			self(**A_     )
        SCREAMING_SNAKE_CASE__											=			outputs[0]
        # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss}



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.hparams
        for mode in ["train", "dev", "test"]:
            SCREAMING_SNAKE_CASE__											=			self._feature_file(A_     )
            if os.path.exists(A_     ) and not args.overwrite_cache:
                logger.info('''Loading features from cached file %s'''   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			torch.load(A_     )
            else:
                logger.info('''Creating features from dataset file at %s'''   ,				args.data_dir     )
                SCREAMING_SNAKE_CASE__											=			self.token_classification_task.read_examples_from_file(args.data_dir   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			self.token_classification_task.convert_examples_to_features(
                    A_   ,				self.labels   ,				args.max_seq_length   ,				self.tokenizer   ,				cls_token_at_end=bool(self.config.model_type in ['''xlnet''']     )   ,				cls_token=self.tokenizer.cls_token   ,				cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0   ,				sep_token=self.tokenizer.sep_token   ,				sep_token_extra=A_   ,				pad_on_left=bool(self.config.model_type in ['''xlnet''']     )   ,				pad_token=self.tokenizer.pad_token_id   ,				pad_token_segment_id=self.tokenizer.pad_token_type_id   ,				pad_token_label_id=self.pad_token_label_id   ,				)
                logger.info('''Saving features into cached file %s'''   ,				A_     )
                torch.save(A_   ,				A_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = False     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self._feature_file(A_     )
        logger.info('''Loading features from cached file %s'''   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			torch.load(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor([f.input_ids for f in features]   ,				dtype=torch.long     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor([f.attention_mask for f in features]   ,				dtype=torch.long     )
        if features[0].token_type_ids is not None:
            SCREAMING_SNAKE_CASE__											=			torch.tensor([f.token_type_ids for f in features]   ,				dtype=torch.long     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.tensor([0 for f in features]   ,				dtype=torch.long     )
            # HACK(we will not use this anymore soon)
        SCREAMING_SNAKE_CASE__											=			torch.tensor([f.label_ids for f in features]   ,				dtype=torch.long     )
        return DataLoader(
            TensorDataset(A_   ,				A_   ,				A_   ,				A_     )   ,				batch_size=A_     )



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        """Compute validation""" ""
        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            SCREAMING_SNAKE_CASE__											=			(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids
        SCREAMING_SNAKE_CASE__											=			self(**A_     )
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			outputs[:2]
        SCREAMING_SNAKE_CASE__											=			logits.detach().cpu().numpy()
        SCREAMING_SNAKE_CASE__											=			inputs['''labels'''].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			torch.stack([x['''val_loss'''] for x in outputs]     ).mean()
        SCREAMING_SNAKE_CASE__											=			np.concatenate([x['''pred'''] for x in outputs]   ,				axis=0     )
        SCREAMING_SNAKE_CASE__											=			np.argmax(A_   ,				axis=2     )
        SCREAMING_SNAKE_CASE__											=			np.concatenate([x['''target'''] for x in outputs]   ,				axis=0     )

        SCREAMING_SNAKE_CASE__											=			dict(enumerate(self.labels     )     )
        SCREAMING_SNAKE_CASE__											=			[[] for _ in range(out_label_ids.shape[0]     )]
        SCREAMING_SNAKE_CASE__											=			[[] for _ in range(out_label_ids.shape[0]     )]

        for i in range(out_label_ids.shape[0]     ):
            for j in range(out_label_ids.shape[1]     ):
                if out_label_ids[i, j] != self.pad_token_label_id:
                    out_label_list[i].append(label_map[out_label_ids[i][j]]     )
                    preds_list[i].append(label_map[preds[i][j]]     )

        SCREAMING_SNAKE_CASE__											=			{
            '''val_loss''': val_loss_mean,
            '''accuracy_score''': accuracy_score(A_   ,				A_     ),
            '''precision''': precision_score(A_   ,				A_     ),
            '''recall''': recall_score(A_   ,				A_     ),
            '''f1''': fa_score(A_   ,				A_     ),
        }

        SCREAMING_SNAKE_CASE__											=			dict(results.items()     )
        SCREAMING_SNAKE_CASE__											=			results
        return ret, preds_list, out_label_list



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._eval_end(A_     )
        SCREAMING_SNAKE_CASE__											=			ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._eval_end(A_     )

        # Converting to the dict required by pl
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
        # pytorch_lightning/trainer/logging.py#L139
        SCREAMING_SNAKE_CASE__											=			ret['''log''']
        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




    @staticmethod
    def  lowercase_					(       A_   ,				A_     ):





        '''simple docstring'''

        BaseTransformer.add_model_specific_args(A_   ,				A_     )
        parser.add_argument(
            '''--task_type'''   ,				default='''NER'''   ,				type=A_   ,				help='''Task type to fine tune in training (e.g. NER, POS, etc)'''     )
        parser.add_argument(
            '''--max_seq_length'''   ,				default=1_28   ,				type=A_   ,				help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )   ,				)

        parser.add_argument(
            '''--labels'''   ,				default=''''''   ,				type=A_   ,				help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''   ,				)
        parser.add_argument(
            '''--gpus'''   ,				default=0   ,				type=A_   ,				help='''The number of GPUs allocated for this, it is by default 0 meaning none'''   ,				)

        parser.add_argument(
            '''--overwrite_cache'''   ,				action='''store_true'''   ,				help='''Overwrite the cached training and evaluation sets'''     )

        return parser


if __name__ == "__main__":
  _A					:				List[str]				=			argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  _A					:				List[Any]				=			NERTransformer.add_model_specific_args(parser, os.getcwd())
  _A					:				Optional[Any]				=			parser.parse_args()
  _A					:				Any				=			NERTransformer(args)
  _A					:				Any				=			generic_train(model, args)

  if args.do_predict:
    # See https://github.com/huggingface/transformers/issues/3159
    # pl use this default format to create a checkpoint:
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
    # /pytorch_lightning/callbacks/model_checkpoint.py#L322
    _A					:				Optional[int]				=			sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    _A					:				Optional[int]				=			model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)
