


"""simple docstring"""


from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


UpperCamelCase						:				List[str]       =      {
    "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 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """facebook/nllb-200-distilled-600M"""
						lowerCAmelCase       =     (
						    """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`."""
						)
						lowerCAmelCase       =     """translator"""
						lowerCAmelCase       =     AutoTokenizer
						lowerCAmelCase       =     AutoModelForSeqaSeqLM
						lowerCAmelCase       =     LANGUAGE_CODES

						lowerCAmelCase       =     ["""text""", """text""", """text"""]
						lowerCAmelCase       =     ["""text"""]







						def __a						(							self   :    Union[str, Any]			,			_lowercase   :    Optional[int]			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Union[str, Any]		):
								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.'		)
								A					       = self.lang_to_code[src_lang]
								A					       = self.lang_to_code[tgt_lang]
								return self.pre_processor._build_translation_inputs(
								    _lowercase			,			return_tensors='pt'			,			src_lang=_lowercase			,			tgt_lang=_lowercase		)







						def __a						(							self   :    int			,			_lowercase   :    Dict		):
								return self.model.generate(**_lowercase		)







						def __a						(							self   :    List[Any]			,			_lowercase   :    Any		):
								return self.post_processor.decode(outputs[0].tolist()			,			skip_special_tokens=_lowercase		)









"""simple docstring"""


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCamelCase						:				int             =      16
UpperCamelCase						:				Optional[int]             =      32







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 16 ,			UpperCamelCase__ = "bert-base-cased"	)      ->		Optional[int]:







		"""simple docstring"""





		A					       = AutoTokenizer.from_pretrained(UpperCamelCase__	)
		A					       = load_dataset('glue' ,			'mrpc'	)

		def tokenize_function(UpperCamelCase__	):
				# max_length=None => use the model max length (it's actually the default)
				A					       = tokenizer(examples['sentence1'] ,			examples['sentence2'] ,			truncation=UpperCamelCase__ ,			max_length=UpperCamelCase__	)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		A					       = datasets.map(
		    UpperCamelCase__ ,			batched=UpperCamelCase__ ,			remove_columns=['idx', 'sentence1', 'sentence2'] ,			load_from_cache_file=UpperCamelCase__	)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		A					       = tokenized_datasets.rename_column('label' ,			'labels'	)

		def collate_fn(UpperCamelCase__	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(UpperCamelCase__ ,			padding='max_length' ,			max_length=128 ,			return_tensors='pt'	)
				return tokenizer.pad(UpperCamelCase__ ,			padding='longest' ,			return_tensors='pt'	)

		# Instantiate dataloaders.
		A					       = DataLoader(
		    tokenized_datasets['train'] ,			shuffle=UpperCamelCase__ ,			collate_fn=UpperCamelCase__ ,			batch_size=UpperCamelCase__	)
		A					       = DataLoader(
		    tokenized_datasets['validation'] ,			shuffle=UpperCamelCase__ ,			collate_fn=UpperCamelCase__ ,			batch_size=UpperCamelCase__	)

		return train_dataloader, eval_dataloader







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Optional[Any]:







		"""simple docstring"""





		A					       = Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		A					       = config['lr']
		A					       = int(config['num_epochs']	)
		A					       = int(config['seed']	)
		A					       = int(config['batch_size']	)
		A					       = args.model_name_or_path

		set_seed(UpperCamelCase__	)
		A       , A					       = get_dataloaders(UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A					       = AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__ ,			return_dict=UpperCamelCase__	)

		# Instantiate optimizer
		A					       = (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		A					       = optimizer_cls(params=model.parameters() ,			lr=UpperCamelCase__	)

		if accelerator.state.deepspeed_plugin is not None:
				A					       = accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				A					       = 1
		A					       = (len(UpperCamelCase__	) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				A					       = get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase__ ,			num_warmup_steps=0 ,			num_training_steps=UpperCamelCase__ ,			)
		else:
				A					       = DummyScheduler(UpperCamelCase__ ,			total_num_steps=UpperCamelCase__ ,			warmup_num_steps=0	)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		A       , A       , A       , A       , A					       = accelerator.prepare(
		    UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)

		# We need to keep track of how many total steps we have iterated over
		A					       = 0
		# We also need to keep track of the stating epoch so files are named properly
		A					       = 0

		# Now we train the model
		A					       = evaluate.load('glue' ,			'mrpc'	)
		A					       = 0
		A					       = {}
		for epoch in range(UpperCamelCase__ ,			UpperCamelCase__	):
				model.train()
				for step, batch in enumerate(UpperCamelCase__	):
						A					       = model(**UpperCamelCase__	)
						A					       = outputs.loss
						A					       = loss / gradient_accumulation_steps
						accelerator.backward(UpperCamelCase__	)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

				model.eval()
				A					       = 0
				for step, batch in enumerate(UpperCamelCase__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						with torch.no_grad():
								A					       = model(**UpperCamelCase__	)
						A					       = outputs.logits.argmax(dim=-1	)
						# It is slightly faster to call this once, than multiple times
						A       , A					       = accelerator.gather(
						    (predictions, batch['labels'])	)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
								if step == len(UpperCamelCase__	) - 1:
										A					       = predictions[: len(eval_dataloader.dataset	) - samples_seen]
										A					       = references[: len(eval_dataloader.dataset	) - samples_seen]
								else:
										samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=UpperCamelCase__ ,			references=UpperCamelCase__ ,			)

				A					       = metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'epoch {epoch}:' ,			UpperCamelCase__	)
				A					       = eval_metric['accuracy']

				if best_performance < eval_metric["accuracy"]:
						A					       = eval_metric['accuracy']

		if args.performance_lower_bound is not None:
				assert (
				    args.performance_lower_bound <= best_performance
				), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir ,			'all_results.json'	) ,			'w'	) as f:
						json.dump(UpperCamelCase__ ,			UpperCamelCase__	)







def  __snake_case       (   )      ->		Optional[int]:







		"""simple docstring"""





		A					       = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
		parser.add_argument(
		    '--model_name_or_path' ,			type=UpperCamelCase__ ,			default='bert-base-cased' ,			help='Path to pretrained model or model identifier from huggingface.co/models.' ,			required=UpperCamelCase__ ,			)
		parser.add_argument(
		    '--output_dir' ,			type=UpperCamelCase__ ,			default='.' ,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,			)
		parser.add_argument(
		    '--performance_lower_bound' ,			type=UpperCamelCase__ ,			default=UpperCamelCase__ ,			help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' ,			)
		parser.add_argument(
		    '--num_epochs' ,			type=UpperCamelCase__ ,			default=3 ,			help='Number of train epochs.' ,			)
		A					       = parser.parse_args()
		A					       = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
		training_function(UpperCamelCase__ ,			UpperCamelCase__	)


if __name__ == "__main__":
	main()





