



"""simple docstring"""







import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
     from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowercase__						:							str        =      get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_torch
@require_torchaudio
@require_sentencepiece
class  UpperCamelCase__  (      unittest.TestCase    ):




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        lowerCAmelCase_  :							Optional[int]								=					tempfile.mkdtemp()

        lowerCAmelCase_  :							int								=					['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
        lowerCAmelCase_  :							int								=					dict(zip(SCREAMING_SNAKE_CASE_       ,     range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
        lowerCAmelCase_  :							Optional[int]								=					Path(self.tmpdirname			)
        save_json(SCREAMING_SNAKE_CASE_       ,     save_dir / VOCAB_FILES_NAMES['vocab_file']			)
        if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
          copyfile(SCREAMING_SNAKE_CASE_       ,     save_dir / VOCAB_FILES_NAMES['spm_file']			)

        lowerCAmelCase_  :							Optional[Any]								=					SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
        tokenizer.save_pretrained(self.tmpdirname			)

        lowerCAmelCase_  :							Optional[Any]								=					{
            'feature_size': 2_4,
            'num_mel_bins': 2_4,
            'padding_value': 0.0,
            'sampling_rate': 1_6_0_0_0,
            'return_attention_mask': False,
            'do_normalize': True,
        }
        save_json(SCREAMING_SNAKE_CASE_       ,     save_dir / FEATURE_EXTRACTOR_NAME			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]       ,     **SCREAMING_SNAKE_CASE_							: Dict			):
        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     **SCREAMING_SNAKE_CASE_							: int			):
        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Any			):
        shutil.rmtree(self.tmpdirname			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        lowerCAmelCase_  :							Union[str, Any]								=					self.get_tokenizer()
        lowerCAmelCase_  :							List[str]								=					self.get_feature_extractor()

        lowerCAmelCase_  :							Optional[int]								=					SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,     feature_extractor=SCREAMING_SNAKE_CASE_			)

        processor.save_pretrained(self.tmpdirname			)
        lowerCAmelCase_  :							Tuple								=					SpeechaTextProcessor.from_pretrained(self.tmpdirname			)

        self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer.get_vocab()			)
        self.assertIsInstance(processor.tokenizer       ,     SCREAMING_SNAKE_CASE_			)

        self.assertEqual(processor.feature_extractor.to_json_string()       ,     feature_extractor.to_json_string()			)
        self.assertIsInstance(processor.feature_extractor       ,     SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        lowerCAmelCase_  :							Any								=					SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()       ,     feature_extractor=self.get_feature_extractor()			)
        processor.save_pretrained(self.tmpdirname			)

        lowerCAmelCase_  :							Any								=					self.get_tokenizer(bos_token='(BOS)'       ,     eos_token='(EOS)'			)
        lowerCAmelCase_  :							Optional[Any]								=					self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_       ,     padding_value=1.0			)

        lowerCAmelCase_  :							Union[str, Any]								=					SpeechaTextProcessor.from_pretrained(
            self.tmpdirname       ,     bos_token='(BOS)'       ,     eos_token='(EOS)'       ,     do_normalize=SCREAMING_SNAKE_CASE_       ,     padding_value=1.0			)

        self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()			)
        self.assertIsInstance(processor.tokenizer       ,     SCREAMING_SNAKE_CASE_			)

        self.assertEqual(processor.feature_extractor.to_json_string()       ,     feature_extractor_add_kwargs.to_json_string()			)
        self.assertIsInstance(processor.feature_extractor       ,     SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        lowerCAmelCase_  :							Tuple								=					self.get_feature_extractor()
        lowerCAmelCase_  :							Any								=					self.get_tokenizer()

        lowerCAmelCase_  :							List[str]								=					SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,     feature_extractor=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Any								=					floats_list((3, 1_0_0_0)			)

        lowerCAmelCase_  :							int								=					feature_extractor(SCREAMING_SNAKE_CASE_       ,     return_tensors='np'			)
        lowerCAmelCase_  :							List[Any]								=					processor(SCREAMING_SNAKE_CASE_       ,     return_tensors='np'			)

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()       ,     input_processor[key].sum()       ,     delta=1E-2			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        lowerCAmelCase_  :							List[Any]								=					self.get_feature_extractor()
        lowerCAmelCase_  :							Optional[int]								=					self.get_tokenizer()

        lowerCAmelCase_  :							List[Any]								=					SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,     feature_extractor=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							List[Any]								=					'This is a test string'

        lowerCAmelCase_  :							int								=					processor(text=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							List[str]								=					tokenizer(SCREAMING_SNAKE_CASE_			)

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]			)



      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        lowerCAmelCase_  :							Tuple								=					self.get_feature_extractor()
        lowerCAmelCase_  :							Optional[Any]								=					self.get_tokenizer()

        lowerCAmelCase_  :							Optional[int]								=					SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,     feature_extractor=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Any								=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowerCAmelCase_  :							Union[str, Any]								=					processor.batch_decode(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

        self.assertListEqual(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)





      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]			):
        lowerCAmelCase_  :							List[str]								=					self.get_feature_extractor()
        lowerCAmelCase_  :							Any								=					self.get_tokenizer()

        lowerCAmelCase_  :							Optional[int]								=					SpeechaTextProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,     feature_extractor=SCREAMING_SNAKE_CASE_			)

        self.assertListEqual(
            processor.model_input_names       ,     feature_extractor.model_input_names       ,     msg='`processor` and `feature_extractor` model input names do not match'       ,     )










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

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[Any]        =      {
         """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,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							List[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,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      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`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





