'''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


__lowerCAmelCase	:       Any						       =	{
    "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 						A   (   UpperCAmelCase   ):
							a_        =      '''facebook/nllb-200-distilled-600M'''
							a_        =      (
							    '''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_        =      '''translator'''
							a_        =      AutoTokenizer
							a_        =      AutoModelForSeqaSeqLM
							a_        =      LANGUAGE_CODES

							a_        =      ['''text''', '''text''', '''text''']
							a_        =      ['''text''']





							def       snake_case__						(      self				:							Any     ,		__a				:							int     ,		__a				:							List[str]     ,		__a				:							Optional[Any]	)							->	str:
								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."""	)
								__UpperCAmelCase							=			self.lang_to_code[src_lang]
								__UpperCAmelCase							=			self.lang_to_code[tgt_lang]
								return self.pre_processor._build_translation_inputs(
								    __a     ,		return_tensors='''pt'''     ,		src_lang=__a     ,		tgt_lang=__a	)





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							str	)							->	Any:
								return self.model.generate(**__a	)






							def       snake_case__						(      self				:							Any     ,		__a				:							Any	)							->	Any:
								return self.post_processor.decode(outputs[0].tolist()     ,		skip_special_tokens=__a	)

'''simple docstring'''


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    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()





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   PreTrainedTokenizer    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   Optional[int] = None    ,					):


	"""simple docstring"""
	__UpperCAmelCase							=			{}

	if train_file is not None:
		__UpperCAmelCase							=			[train_file]
	if eval_file is not None:
		__UpperCAmelCase							=			[eval_file]
	if test_file is not None:
		__UpperCAmelCase							=			[test_file]

	__UpperCAmelCase							=			datasets.load_dataset('''csv'''    ,					data_files=UpperCamelCase__       )
	__UpperCAmelCase							=			list(ds[list(files.keys()       )[0]].features.keys()       )
	__UpperCAmelCase							=			features_name.pop(UpperCamelCase__       )
	__UpperCAmelCase							=			list(set(ds[list(files.keys()       )[0]][label_name]       )       )
	__UpperCAmelCase							=			{label: i for i, label in enumerate(UpperCamelCase__       )}
	__UpperCAmelCase							=			tokenizer.model_input_names
	__UpperCAmelCase							=			{}

	if len(UpperCamelCase__       ) == 1:
		for k in files.keys():
			__UpperCAmelCase							=			ds[k].map(
			    lambda UpperCamelCase__       : tokenizer.batch_encode_plus(
			        example[features_name[0]]    ,					truncation=UpperCamelCase__    ,					max_length=UpperCamelCase__    ,					padding='''max_length'''       )    ,					batched=UpperCamelCase__    ,					)
	elif len(UpperCamelCase__       ) == 2:
		for k in files.keys():
			__UpperCAmelCase							=			ds[k].map(
			    lambda UpperCamelCase__       : tokenizer.batch_encode_plus(
			        (example[features_name[0]], example[features_name[1]])    ,					truncation=UpperCamelCase__    ,					max_length=UpperCamelCase__    ,					padding='''max_length'''    ,					)    ,					batched=UpperCamelCase__    ,					)

	def gen_train():
		for ex in transformed_ds[datasets.Split.TRAIN]:
			__UpperCAmelCase							=			{k: v for k, v in ex.items() if k in input_names}
			__UpperCAmelCase							=			labelaid[ex[label_name]]
			yield (d, label)

	def gen_val():
		for ex in transformed_ds[datasets.Split.VALIDATION]:
			__UpperCAmelCase							=			{k: v for k, v in ex.items() if k in input_names}
			__UpperCAmelCase							=			labelaid[ex[label_name]]
			yield (d, label)

	def gen_test():
		for ex in transformed_ds[datasets.Split.TEST]:
			__UpperCAmelCase							=			{k: v for k, v in ex.items() if k in input_names}
			__UpperCAmelCase							=			labelaid[ex[label_name]]
			yield (d, label)

	__UpperCAmelCase							=			(
	    tf.data.Dataset.from_generator(
	        UpperCamelCase__    ,					({k: tf.intaa for k in input_names}, tf.intaa)    ,					({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))    ,					)
	    if datasets.Split.TRAIN in transformed_ds
	    else None
	)

	if train_ds is not None:
		__UpperCAmelCase							=			train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

	__UpperCAmelCase							=			(
	    tf.data.Dataset.from_generator(
	        UpperCamelCase__    ,					({k: tf.intaa for k in input_names}, tf.intaa)    ,					({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))    ,					)
	    if datasets.Split.VALIDATION in transformed_ds
	    else None
	)

	if val_ds is not None:
		__UpperCAmelCase							=			val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

	__UpperCAmelCase							=			(
	    tf.data.Dataset.from_generator(
	        UpperCamelCase__    ,					({k: tf.intaa for k in input_names}, tf.intaa)    ,					({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))    ,					)
	    if datasets.Split.TEST in transformed_ds
	    else None
	)

	if test_ds is not None:
		__UpperCAmelCase							=			test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

	return train_ds, val_ds, test_ds, labelaid


__lowerCAmelCase	:       List[Any]						       =	logging.getLogger(__name__)






@dataclass
class 						A   :
							a_        =      field(metadata={'''help''': '''Which column contains the label'''}   )
							a_        =      field(default=UpperCAmelCase       ,	metadata={'''help''': '''The path of the training file'''}   )
							a_        =      field(default=UpperCAmelCase       ,	metadata={'''help''': '''The path of the development file'''}   )
							a_        =      field(default=UpperCAmelCase       ,	metadata={'''help''': '''The path of the test file'''}   )
							a_        =      field(
							    default=1_2_8       ,	metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }       ,	)
							a_        =      field(
							    default=UpperCAmelCase       ,	metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )






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





def       lowerCAmelCase					(		):


	"""simple docstring"""
	# 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.
	__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.'''       )

	# 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(
	    f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1       )}, """
	    f"""16-bits training: {training_args.fpaa}"""       )
	logger.info(f"""Training/evaluation parameters {training_args}"""       )

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

	__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    ,					)

	__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			get_tfds(
	    train_file=data_args.train_file    ,					eval_file=data_args.dev_file    ,					test_file=data_args.test_file    ,					tokenizer=UpperCamelCase__    ,					label_column_id=data_args.label_column_id    ,					max_seq_length=data_args.max_seq_length    ,					)

	__UpperCAmelCase							=			AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,					num_labels=len(UpperCamelCase__       )    ,					labelaid=UpperCamelCase__    ,					idalabel={id: label for label, id in labelaid.items()}    ,					finetuning_task='''text-classification'''    ,					cache_dir=model_args.cache_dir    ,					)

	with training_args.strategy.scope():
		__UpperCAmelCase							=			TFAutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path    ,					from_pt=bool('''.bin''' in model_args.model_name_or_path       )    ,					config=UpperCamelCase__    ,					cache_dir=model_args.cache_dir    ,					)

	def compute_metrics(UpperCamelCase__				:   EvalPrediction       ) -> Dict:
		__UpperCAmelCase							=			np.argmax(p.predictions    ,					axis=1       )

		return {"acc": (preds == p.label_ids).mean()}

	# Initialize our Trainer
	__UpperCAmelCase							=			TFTrainer(
	    model=UpperCamelCase__    ,					args=UpperCamelCase__    ,					train_dataset=UpperCamelCase__    ,					eval_dataset=UpperCamelCase__    ,					compute_metrics=UpperCamelCase__    ,					)

	# 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(UpperCamelCase__    ,					'''w'''       ) as writer:
			logger.info('''***** Eval results *****'''       )

			for key, value in result.items():
				logger.info(f"""  {key} = {value}"""       )
				writer.write(f"""{key} = {value}\n"""       )

			results.update(UpperCamelCase__       )

	return results


if __name__ == "__main__":
					main()
