






"""simple docstring"""
import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
      print("#################### AVAILABLE DEVICES ####################")
      print(jax.devices())
      print("###########################################################")

      # setup for wandb sweep
      A_			: Tuple				=			Args()
      A_			: Any				=			wandb.init(project="bigbird-natural-questions", config=args.__dict__)
      A_			: Any				=			dict(logger.config)
      del wandb_args["batch_size"]
      A_			: List[str]				=			replace(args, **wandb_args)
      A_			: Optional[Any]				=			args.base_dir + "-" + wandb.run.id
      A_			: Any				=			replace(args, base_dir=base_dir)
      print(args)

      A_			: List[Any]				=			load_dataset("json", data_files=args.tr_data_path)["train"]
      A_			: Any				=			load_dataset("json", data_files=args.val_data_path)["train"]

      # drop extra batch for now
      A_			: int				=			range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
      A_			: Tuple				=			tr_dataset.shuffle().select(indices)
      A_			: int				=			range(len(val_dataset) - len(val_dataset) % args.batch_size)
      A_			: Union[str, Any]				=			val_dataset.shuffle().select(indices)

      if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
            A_			: Optional[Any]				=			tr_dataset.shuffle().select(range(8_00_00))
            A_			: Tuple				=			val_dataset.shuffle().select(range(80_00))

      print(tr_dataset)
      print(val_dataset)

      A_			: List[str]				=			FlaxBigBirdForNaturalQuestions.from_pretrained(
          args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
      )
      A_			: int				=			BigBirdTokenizerFast.from_pretrained(args.model_id)
      A_			: Any				=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

      A_			: str				=			{
          "lr": args.lr,
          "init_lr": args.init_lr,
          "warmup_steps": args.warmup_steps,
          "num_train_steps": args.max_epochs * (len(tr_dataset) // args.batch_size),
          "weight_decay": args.weight_decay,
      }
      A_,       A_			: Tuple				=			build_tx(**tx_args)

      A_			: Union[str, Any]				=			Trainer(
          args=args,
          data_collator=data_collator,
          model_save_fn=model.save_pretrained,
          train_step_fn=train_step,
          val_step_fn=val_step,
          logger=logger,
          scheduler_fn=lr,
      )

      A_			: str				=			None
      A_			: Union[str, Any]				=			trainer.create_state(model, tx, num_train_steps=tx_args["num_train_steps"], ckpt_dir=ckpt_dir)
      try:
            trainer.train(state, tr_dataset, val_dataset)
      except KeyboardInterrupt:
            print("Oooops; TRAINING STOPPED UNFORTUNATELY")

      print("SAVING WEIGHTS IN `final-weights`")
      A_			: List[str]				=			jax_utils.unreplicate(state.params)
      model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params)


"""simple docstring"""


import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY






@is_pipeline_test
class    __UpperCAmelCase						(	unittest.TestCase	):
				A__      :   Optional[Any]  							=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				A__      :   int  							=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										if isinstance(model.config	, _lowerCamelCase      ):
																lowerCamelCase__					,      lowerCamelCase__					=list(tokenizer.lang_code_to_id.keys()      )[:2]
																lowerCamelCase__					=TranslationPipeline(model=_lowerCamelCase	, tokenizer=_lowerCamelCase	, src_lang=_lowerCamelCase	, tgt_lang=_lowerCamelCase      )
										else:
																lowerCamelCase__					=TranslationPipeline(model=_lowerCamelCase	, tokenizer=_lowerCamelCase      )
										return translator, ["Some string", "Some other text"]

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=translator("Some string"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string", "other string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}, {"translation_text": ANY(_lowerCamelCase      )}]      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
										                " Beide Beide"
										            )
										        }
										    ]	, )

				@require_tf
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
										                " Beide Beide"
										            )
										        }
										    ]	, )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
										                " urine urine urine urine urine urine urine"
										            )
										        }
										    ]	, )






				@require_tf
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
										                " urine urine urine urine urine urine urine"
										            )
										        }
										    ]	, )






class    __UpperCAmelCase						(	unittest.TestCase	):

				@require_torch
				@slow
				def 		_a	(      self      ):
										# We don't provide a default for this pair
										with self.assertRaises(_lowerCamelCase      ):
																pipeline(task="translation_cn_to_ar"      )

										# but we do for this one
										lowerCamelCase__					=pipeline(task="translation_en_to_de"      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "en"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "de"      )

				@require_torch
				@slow
				def 		_a	(      self      ):
										lowerCamelCase__					=MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )
										lowerCamelCase__					=MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )

										lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase	, tokenizer=_lowerCamelCase      )
										# Missing src_lang, tgt_lang
										with self.assertRaises(_lowerCamelCase      ):
																translator("This is a test"      )

										lowerCamelCase__					=translator("This is a test"	, src_lang="en_XX"	, tgt_lang="ar_AR"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "هذا إختبار"}]      )

										lowerCamelCase__					=translator("This is a test"	, src_lang="en_XX"	, tgt_lang="hi_IN"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "यह एक परीक्षण है"}]      )

										# src_lang, tgt_lang can be defined at pipeline call time
										lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase	, tokenizer=_lowerCamelCase	, src_lang="en_XX"	, tgt_lang="ar_AR"      )
										lowerCamelCase__					=translator("This is a test"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "هذا إختبار"}]      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										lowerCamelCase__					=pipeline(task="translation_cn_to_ar"	, model=_lowerCamelCase      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "cn"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "ar"      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										with pytest.warns(_lowerCamelCase	, match=r".*translation_en_to_de.*"      ):
																lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase      )
										self.assertEqual(translator.task	, "translation_en_to_de"      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "en"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "de"      )






				@require_torch
				def 		_a	(      self      ):
										with self.assertRaises(_lowerCamelCase      ):
																pipeline(task="translation"      )


