import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
      pass

if is_speech_available():
      from transformers import EncodecFeatureExtractor, MusicgenProcessor


UpperCAmelCase_    		=				random.Random()

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_=1.0							,       lowerCAmelCase_=None							,       lowerCAmelCase_=None )		->    Optional[Any]:




       '''simple docstring'''






       if rng is None:
              _A=						global_rng

       _A=						[]
       for batch_idx in range(shape[0] ):
              values.append([] )
              for _ in range(shape[1] ):
                     values[-1].append(rng.random() * scale )

       return values





@require_torch
@require_sentencepiece
class   lowerCAmelCase    (       unittest.TestCase      ):



     def       a__	(       self							):
            _A=						'facebook/musicgen-small'
            _A=						tempfile.mkdtemp()



     def       a__	(       self					,   **lowerCAmelCase__							):
            return TaTokenizer.from_pretrained(self.checkpoint					,   **lowerCAmelCase__							)



     def       a__	(       self					,   **lowerCAmelCase__							):
            return EncodecFeatureExtractor.from_pretrained(self.checkpoint					,   **lowerCAmelCase__							)



     def       a__	(       self							):
            shutil.rmtree(self.tmpdirname							)



     def       a__	(       self							):
            _A=						self.get_tokenizer()
            _A=						self.get_feature_extractor()

            _A=						MusicgenProcessor(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__							)

            processor.save_pretrained(self.tmpdirname							)
            _A=						MusicgenProcessor.from_pretrained(self.tmpdirname							)

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

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



     def       a__	(       self							):
            _A=						MusicgenProcessor(tokenizer=self.get_tokenizer()					,   feature_extractor=self.get_feature_extractor()							)
            processor.save_pretrained(self.tmpdirname							)

            _A=						self.get_tokenizer(bos_token='(BOS)'					,   eos_token='(EOS)'							)
            _A=						self.get_feature_extractor(do_normalize=lowerCAmelCase__					,   padding_value=1.0							)

            _A=						MusicgenProcessor.from_pretrained(
                self.tmpdirname					,   bos_token='(BOS)'					,   eos_token='(EOS)'					,   do_normalize=lowerCAmelCase__					,   padding_value=1.0							)

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

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



     def       a__	(       self							):
            _A=						self.get_feature_extractor()
            _A=						self.get_tokenizer()

            _A=						MusicgenProcessor(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__							)

            _A=						floats_list((3, 1000)							)

            _A=						feature_extractor(lowerCAmelCase__					,   return_tensors='np'							)
            _A=						processor(lowerCAmelCase__					,   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       a__	(       self							):
            _A=						self.get_feature_extractor()
            _A=						self.get_tokenizer()

            _A=						MusicgenProcessor(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__							)

            _A=						'This is a test string'

            _A=						processor(text=lowerCAmelCase__							)

            _A=						tokenizer(lowerCAmelCase__							)

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



     def       a__	(       self							):
            _A=						self.get_feature_extractor()
            _A=						self.get_tokenizer()

            _A=						MusicgenProcessor(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__							)

            _A=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _A=						processor.batch_decode(sequences=lowerCAmelCase__							)
            _A=						tokenizer.batch_decode(lowerCAmelCase__							)

            self.assertListEqual(lowerCAmelCase__					,   lowerCAmelCase__							)



     def       a__	(       self							):
            _A=						self.get_feature_extractor()
            _A=						self.get_tokenizer()

            _A=						MusicgenProcessor(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__							)

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





     def       a__	(       self							):
            _A=						self.get_feature_extractor(padding_side='left'							)
            _A=						self.get_tokenizer()

            _A=						MusicgenProcessor(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__							)

            _A=						[floats_list((1, x)							)[0] for x in range(5					,   20					,   5							)]
            _A=						processor(lowerCAmelCase__							).padding_mask

            _A=						np.asarray(floats_list((3, 20)							)							)[:, None, :]
            _A=						processor.batch_decode(lowerCAmelCase__					,   padding_mask=lowerCAmelCase__							)

            self.assertIsInstance(lowerCAmelCase__					,   lowerCAmelCase__							)

            for audio in decoded_audios:
                   self.assertIsInstance(lowerCAmelCase__					,   np.ndarray							)

            self.assertTrue(decoded_audios[0].shape == (1, 10)							)
            self.assertTrue(decoded_audios[1].shape == (1, 15)							)
            self.assertTrue(decoded_audios[2].shape == (1, 20)							)
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
      UpperCAmelCase_    		=				Args()
      UpperCAmelCase_    		=				wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
      UpperCAmelCase_    		=				dict(logger.config)
      del wandb_args["batch_size"]
      UpperCAmelCase_    		=				replace(args, **wandb_args)
      UpperCAmelCase_    		=				args.base_dir + '''-''' + wandb.run.id
      UpperCAmelCase_    		=				replace(args, base_dir=base_dir)
      print(args)

      UpperCAmelCase_    		=				load_dataset('''json''', data_files=args.tr_data_path)['''train''']
      UpperCAmelCase_    		=				load_dataset('''json''', data_files=args.val_data_path)['''train''']

      # drop extra batch for now
      UpperCAmelCase_    		=				range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
      UpperCAmelCase_    		=				tr_dataset.shuffle().select(indices)
      UpperCAmelCase_    		=				range(len(val_dataset) - len(val_dataset) % args.batch_size)
      UpperCAmelCase_    		=				val_dataset.shuffle().select(indices)

      if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
            UpperCAmelCase_    		=				tr_dataset.shuffle().select(range(80_000))
            UpperCAmelCase_    		=				val_dataset.shuffle().select(range(8_000))

      print(tr_dataset)
      print(val_dataset)

      UpperCAmelCase_    		=				FlaxBigBirdForNaturalQuestions.from_pretrained(
          args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
      )
      UpperCAmelCase_    		=				BigBirdTokenizerFast.from_pretrained(args.model_id)
      UpperCAmelCase_    		=				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

      UpperCAmelCase_    		=				{
          '''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,
      }
      UpperCAmelCase_		,     UpperCAmelCase_    		=				build_tx(**tx_args)

      UpperCAmelCase_    		=				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,
      )

      UpperCAmelCase_    		=				None
      UpperCAmelCase_    		=				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`''')
      UpperCAmelCase_    		=				jax_utils.unreplicate(state.params)
      model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)