




import torch

from accelerate import Accelerator, DistributedType

class 			_lowerCamelCase    :

      """simple docstring"""

      def __enter__(  self       :			Optional[int]					):
          if self.enabled:
              __UpperCamelCase			=    self.model.no_sync()
              self.model_sync_obj.__enter__()

          return self







      def __exit__(  self       :			Union[str, Any]			,							snake_case       :			Dict			,							snake_case       :			Any			,							snake_case       :			Dict					):
          if self.enabled:
              # Average all models on exit
              self._sync_and_avg_model_params()
              self.model_sync_obj.__exit__(snake_case			,							snake_case			,							snake_case					)







      def __init__(  self       :			Tuple			,							snake_case       :			Accelerator			,							snake_case       :			torch.nn.Module			,							snake_case       :			int			,							snake_case       :			bool = True					):
          if accelerator.distributed_type not in [
              DistributedType.NO,
              DistributedType.MULTI_CPU,
              DistributedType.MULTI_GPU,
          ]:
              raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''					)
          __UpperCamelCase			=    enabled and accelerator.distributed_type != DistributedType.NO
          __UpperCamelCase			=    0
          if self.enabled:
              __UpperCamelCase			=    accelerator
              __UpperCamelCase			=    model
              __UpperCamelCase			=    local_sgd_steps







      def    snake_case						(  self       :			int					):
          self.num_steps += 1
          if not self.enabled:
              return

          if self.num_steps % self.local_sgd_steps == 0:
              self._sync_and_avg_model_params()







      def    snake_case						(  self       :			str					):
          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  __UpperCamelCase			=    self.accelerator.reduce(param.data			,							reduction='''mean'''					)












import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
 from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

 from .test_feature_extraction_speechta import floats_list


a_ 		=  get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    tempfile.mkdtemp()

          __UpperCamelCase			=    SpeechTaTokenizer(snake_case					)
          tokenizer.save_pretrained(self.tmpdirname					)

          __UpperCamelCase			=    {
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 16000,
              '''do_normalize''': False,
              '''num_mel_bins''': 80,
              '''hop_length''': 16,
              '''win_length''': 64,
              '''win_function''': '''hann_window''',
              '''fmin''': 80,
              '''fmax''': 7600,
              '''mel_floor''': 1E-10,
              '''reduction_factor''': 2,
              '''return_attention_mask''': True,
          }

          __UpperCamelCase			=    os.path.join(self.tmpdirname			,							snake_case					)
          with open(self.feature_extraction_file			,							'''w'''			,							encoding='''utf-8'''					) as fp:
              fp.write(json.dumps(snake_case					) + '''\n'''					)







      def    snake_case						(  self       :			Union[str, Any]			,							**snake_case       :			List[Any]					):
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,							**snake_case					)







      def    snake_case						(  self       :			List[Any]			,							**snake_case       :			List[str]					):
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,							**snake_case					)







      def    snake_case						(  self       :			Union[str, Any]					):
          shutil.rmtree(self.tmpdirname					)







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.get_tokenizer()
          __UpperCamelCase			=    self.get_feature_extractor()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          processor.save_pretrained(self.tmpdirname					)
          __UpperCamelCase			=    SpeechTaProcessor.from_pretrained(self.tmpdirname					)

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

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







      def    snake_case						(  self       :			Tuple					):
          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=self.get_tokenizer()			,							feature_extractor=self.get_feature_extractor()					)
          processor.save_pretrained(self.tmpdirname					)

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

          __UpperCamelCase			=    SpeechTaProcessor.from_pretrained(
              self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=snake_case			,							padding_value=1.0					)

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

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







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    floats_list((3, 1000)					)

          __UpperCamelCase			=    feature_extractor(audio=snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(audio=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    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    floats_list((3, 1000)					)

          __UpperCamelCase			=    feature_extractor(audio_target=snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(audio_target=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    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    '''This is a test string'''

          __UpperCamelCase			=    processor(text=snake_case					)
          __UpperCamelCase			=    tokenizer(snake_case					)

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







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    '''This is a test string'''

          __UpperCamelCase			=    processor(text_target=snake_case					)
          __UpperCamelCase			=    tokenizer(snake_case					)

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







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

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

          __UpperCamelCase			=    processor.batch_decode(snake_case					)
          __UpperCamelCase			=    tokenizer.batch_decode(snake_case					)

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

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






