
import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[int]=2		,				__SCREAMING_SNAKE_CASE					:  Dict=True		,				__SCREAMING_SNAKE_CASE					:  int=False    ):






  """simple docstring"""




  _lowerCAmelCase						=		Accelerator(step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE		,				split_batches=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		torch.nn.Linear(2		,				4    )
  _lowerCAmelCase						=		torch.optim.AdamW(model.parameters()		,				lr=1.0    )
  _lowerCAmelCase						=		torch.optim.lr_scheduler.OneCycleLR(__SCREAMING_SNAKE_CASE		,				max_lr=0.0_1		,				steps_per_epoch=2		,				epochs=1    )
  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		accelerator.prepare(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  # Optimizer has stepped
  scheduler.step()
  if step_scheduler_with_optimizer or (num_processes == 1):
    assert (
        scheduler.scheduler.last_epoch == num_processes
    ), f'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
  else:
    assert (
        scheduler.scheduler.last_epoch != num_processes
    ), f'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]=2		,				__SCREAMING_SNAKE_CASE					:  Any=True		,				__SCREAMING_SNAKE_CASE					:  List[Any]=False    ):






  """simple docstring"""




  _lowerCAmelCase						=		Accelerator(step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE		,				split_batches=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		torch.nn.Linear(2		,				4    )
  _lowerCAmelCase						=		torch.optim.AdamW(model.parameters()		,				lr=1.0    )
  _lowerCAmelCase						=		torch.optim.lr_scheduler.LambdaLR(__SCREAMING_SNAKE_CASE		,				lr_lambda=lambda __SCREAMING_SNAKE_CASE    : 1 - n / 10    )
  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		accelerator.prepare(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  # Optimizer has stepped
  _lowerCAmelCase						=		False
  scheduler.step()
  _lowerCAmelCase						=		1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

  # Optimizer has not stepped
  _lowerCAmelCase						=		True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    _lowerCAmelCase						=		1 - 2 / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''



def 	_a  (					__SCREAMING_SNAKE_CASE					:  int = 2    ):






  """simple docstring"""




  from transformers import get_linear_schedule_with_warmup

  _lowerCAmelCase						=		[1, 2, 4]
  for num_steps in steps:
    _lowerCAmelCase						=		GradientAccumulationPlugin(num_steps=__SCREAMING_SNAKE_CASE		,				adjust_scheduler=num_steps > 1    )
    _lowerCAmelCase						=		Accelerator(gradient_accumulation_plugin=__SCREAMING_SNAKE_CASE    )
    _lowerCAmelCase						=		torch.nn.Linear(2		,				4    )
    _lowerCAmelCase						=		torch.optim.AdamW(model.parameters()		,				lr=1_0.0    )
    _lowerCAmelCase						=		get_linear_schedule_with_warmup(optimizer=__SCREAMING_SNAKE_CASE		,				num_warmup_steps=0		,				num_training_steps=20    )

    _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		accelerator.prepare(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

    for i in range(10 * num_steps    ):
      with accelerator.accumulate(__SCREAMING_SNAKE_CASE    ):
        optimizer.step()
        scheduler.step()

      if i == (10 * num_steps - 2):
        assert (
            scheduler.get_last_lr()[0] != 0
        ), f'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
    assert (
        scheduler.get_last_lr()[0] == 0
    ), f'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
    GradientState._reset_state()





@require_cpu
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Union[str, Any]:
         debug_launcher(partial(_lowerCAmelCase  ,      num_processes=1       )  ,      num_processes=1       )
         debug_launcher(partial(_lowerCAmelCase  ,      num_processes=1  ,      split_batches=_lowerCAmelCase       )  ,      num_processes=1       )



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Optional[int]:
         debug_launcher(partial(_lowerCAmelCase  ,      num_processes=1       )  ,      num_processes=1       )
         debug_launcher(partial(_lowerCAmelCase  ,      num_processes=1  ,      split_batches=_lowerCAmelCase       )  ,      num_processes=1       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   List[Any]:
         debug_launcher(partial(_lowerCAmelCase  ,      num_processes=1  ,      step_scheduler_with_optimizer=_lowerCAmelCase       )  ,      num_processes=1       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Any:
         debug_launcher(partial(_lowerCAmelCase  ,      num_processes=1  ,      step_scheduler_with_optimizer=_lowerCAmelCase       )  ,      num_processes=1       )



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   List[str]:
         AcceleratorState._reset_state(_lowerCAmelCase       )
         debug_launcher(_lowerCAmelCase       )
         debug_launcher(partial(_lowerCAmelCase  ,      num_processes=1  ,      split_batches=_lowerCAmelCase       )  ,      num_processes=1       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   List[Any]:
         AcceleratorState._reset_state(_lowerCAmelCase       )
         debug_launcher(_lowerCAmelCase       )
         debug_launcher(partial(_lowerCAmelCase  ,      num_processes=1  ,      split_batches=_lowerCAmelCase       )  ,      num_processes=1       )



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Dict:
         AcceleratorState._reset_state(_lowerCAmelCase       )
         debug_launcher(partial(_lowerCAmelCase  ,      step_scheduler_with_optimizer=_lowerCAmelCase       )       )



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   List[Any]:
         AcceleratorState._reset_state(_lowerCAmelCase       )
         debug_launcher(partial(_lowerCAmelCase  ,      step_scheduler_with_optimizer=_lowerCAmelCase       )       )






       @require_huggingface_suite
       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   List[Any]:
         AcceleratorState._reset_state(_lowerCAmelCase       )
         debug_launcher(partial(_lowerCAmelCase  ,      num_processes=1       )       )
         debug_launcher(_lowerCAmelCase       )


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


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





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


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

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

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

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

         _lowerCAmelCase						=		SpeechaTextProcessor.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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

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

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(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'  ,      )
