


"""simple docstring"""






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__		(					_UpperCAmelCase    :      Any=2				,    _UpperCAmelCase    :      List[Any]=True				,    _UpperCAmelCase    :      List[str]=False	)							->	Dict:

	'''simple docstring'''


	snake_case__  :						List[str]		=      Accelerator(step_scheduler_with_optimizer=_UpperCAmelCase				,    split_batches=_UpperCAmelCase	)
	snake_case__  :						str		=      torch.nn.Linear(2				,    4	)
	snake_case__  :						Tuple		=      torch.optim.AdamW(model.parameters()				,    lr=1.0	)
	snake_case__  :						List[str]		=      torch.optim.lr_scheduler.OneCycleLR(_UpperCAmelCase				,    max_lr=0.0_1				,    steps_per_epoch=2				,    epochs=1	)
	snake_case__,					snake_case__,					snake_case__  :						str		=      accelerator.prepare(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)

	# 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__		(					_UpperCAmelCase    :      Optional[int]=2				,    _UpperCAmelCase    :      Tuple=True				,    _UpperCAmelCase    :      Union[str, Any]=False	)							->	Union[str, Any]:

	'''simple docstring'''


	snake_case__  :						Any		=      Accelerator(step_scheduler_with_optimizer=_UpperCAmelCase				,    split_batches=_UpperCAmelCase	)
	snake_case__  :						Union[str, Any]		=      torch.nn.Linear(2				,    4	)
	snake_case__  :						int		=      torch.optim.AdamW(model.parameters()				,    lr=1.0	)
	snake_case__  :						str		=      torch.optim.lr_scheduler.LambdaLR(_UpperCAmelCase				,    lr_lambda=lambda _UpperCAmelCase	: 1 - n / 10	)
	snake_case__,					snake_case__,					snake_case__  :						Any		=      accelerator.prepare(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)

	# Optimizer has stepped
	snake_case__  :						str		=      False
	scheduler.step()
	snake_case__  :						List[Any]		=      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
	snake_case__  :						Optional[Any]		=      True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		snake_case__  :						Optional[Any]		=      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__		(					_UpperCAmelCase    :      int = 2	)							->	int:

	'''simple docstring'''


	from transformers import get_linear_schedule_with_warmup

	snake_case__  :						Union[str, Any]		=      [1, 2, 4]
	for num_steps in steps:
		snake_case__  :						Union[str, Any]		=      GradientAccumulationPlugin(num_steps=_UpperCAmelCase				,    adjust_scheduler=num_steps > 1	)
		snake_case__  :						Tuple		=      Accelerator(gradient_accumulation_plugin=_UpperCAmelCase	)
		snake_case__  :						str		=      torch.nn.Linear(2				,    4	)
		snake_case__  :						Optional[int]		=      torch.optim.AdamW(model.parameters()				,    lr=1_0.0	)
		snake_case__  :						str		=      get_linear_schedule_with_warmup(optimizer=_UpperCAmelCase				,    num_warmup_steps=0				,    num_training_steps=20	)

		snake_case__,					snake_case__,					snake_case__  :						List[Any]		=      accelerator.prepare(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)

		for i in range(10 * num_steps	):
			with accelerator.accumulate(_UpperCAmelCase	):
				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 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								debug_launcher(partial(lowerCamelCase__	,					num_processes=1)	,					num_processes=1)
								debug_launcher(partial(lowerCamelCase__	,					num_processes=1	,					split_batches=lowerCamelCase__)	,					num_processes=1)




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								debug_launcher(partial(lowerCamelCase__	,					num_processes=1)	,					num_processes=1)
								debug_launcher(partial(lowerCamelCase__	,					num_processes=1	,					split_batches=lowerCamelCase__)	,					num_processes=1)




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								debug_launcher(partial(lowerCamelCase__	,					num_processes=1	,					step_scheduler_with_optimizer=lowerCamelCase__)	,					num_processes=1)




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								debug_launcher(partial(lowerCamelCase__	,					num_processes=1	,					step_scheduler_with_optimizer=lowerCamelCase__)	,					num_processes=1)




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								AcceleratorState._reset_state(lowerCamelCase__)
								debug_launcher(lowerCamelCase__)
								debug_launcher(partial(lowerCamelCase__	,					num_processes=1	,					split_batches=lowerCamelCase__)	,					num_processes=1)




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								AcceleratorState._reset_state(lowerCamelCase__)
								debug_launcher(lowerCamelCase__)
								debug_launcher(partial(lowerCamelCase__	,					num_processes=1	,					split_batches=lowerCamelCase__)	,					num_processes=1)




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								AcceleratorState._reset_state(lowerCamelCase__)
								debug_launcher(partial(lowerCamelCase__	,					step_scheduler_with_optimizer=lowerCamelCase__))




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								AcceleratorState._reset_state(lowerCamelCase__)
								debug_launcher(partial(lowerCamelCase__	,					step_scheduler_with_optimizer=lowerCamelCase__))







							@require_huggingface_suite
							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								AcceleratorState._reset_state(lowerCamelCase__)
								debug_launcher(partial(lowerCamelCase__	,					num_processes=1))
								debug_launcher(lowerCamelCase__)












import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list



class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       __magic_name__				=       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''						)
       __magic_name__				=       dict(zip(A	,  range(len(A						)						)						)						)

       __magic_name__				=       {
           '''pad_token''': '''<pad>''',
           '''unk_token''': '''<unk>''',
           '''bos_token''': '''<s>''',
           '''eos_token''': '''</s>''',
       }
       __magic_name__				=       {
           '''feature_size''': 1,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_60_00,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }

       __magic_name__				=       tempfile.mkdtemp()
       __magic_name__				=       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES['''vocab_file''']						)
       __magic_name__				=       os.path.join(self.tmpdirname	,  A						)
       with open(self.vocab_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)

       with open(self.feature_extraction_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)





  def 			__A      (  self	,  **A						)		->   str:





       '''simple docstring'''







       __magic_name__				=       self.add_kwargs_tokens_map.copy()
       kwargs.update(A						)
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self	,  **A						)		->   List[Any]:





       '''simple docstring'''







       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       shutil.rmtree(self.tmpdirname						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       self.get_tokenizer()
       __magic_name__				=       self.get_feature_extractor()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       processor.save_pretrained(self.tmpdirname						)
       __magic_name__				=       WavaVecaProcessor.from_pretrained(self.tmpdirname						)

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

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





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       WavaVecaProcessor(tokenizer=self.get_tokenizer()	,  feature_extractor=self.get_feature_extractor()						)
       processor.save_pretrained(self.tmpdirname						)

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

       __magic_name__				=       WavaVecaProcessor.from_pretrained(
           self.tmpdirname	,  bos_token='''(BOS)'''	,  eos_token='''(EOS)'''	,  do_normalize=A	,  padding_value=1.0						)

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

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





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       floats_list((3, 10_00)						)

       __magic_name__				=       feature_extractor(A	,  return_tensors='''np'''						)
       __magic_name__				=       processor(A	,  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						)		->   Optional[Any]:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       '''This is a test string'''

       __magic_name__				=       processor(text=A						)

       __magic_name__				=       tokenizer(A						)

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





  def 			__A      (  self						)		->   Tuple:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

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

       __magic_name__				=       processor.batch_decode(A						)
       __magic_name__				=       tokenizer.batch_decode(A						)

       self.assertListEqual(A	,  A						)







  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

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