"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	:		Optional[int]   =   get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')






@require_torch
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					tempfile.mkdtemp()

      __lowerCamelCase    		=					SpeechTaTokenizer(_SCREAMING_SNAKE_CASE	)
      tokenizer.save_pretrained(self.tmpdirname	)

      __lowerCamelCase    		=					{
          '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-1_0,
          'reduction_factor': 2,
          'return_attention_mask': True,
      }

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







   def    _A				(	self ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return SpeechTaTokenizer.from_pretrained(self.tmpdirname ,				**_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname ,				**_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      shutil.rmtree(self.tmpdirname	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.get_tokenizer()
      __lowerCamelCase    		=					self.get_feature_extractor()

      __lowerCamelCase    		=					SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,				feature_extractor=_SCREAMING_SNAKE_CASE	)

      processor.save_pretrained(self.tmpdirname	)
      __lowerCamelCase    		=					SpeechTaProcessor.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    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					SpeechTaProcessor(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=_SCREAMING_SNAKE_CASE ,				padding_value=1.0	)

      __lowerCamelCase    		=					SpeechTaProcessor.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    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.get_feature_extractor()
      __lowerCamelCase    		=					self.get_tokenizer()

      __lowerCamelCase    		=					SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,				feature_extractor=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					floats_list((3, 1000)	)

      __lowerCamelCase    		=					feature_extractor(audio=_SCREAMING_SNAKE_CASE ,				return_tensors='np'	)
      __lowerCamelCase    		=					processor(audio=_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    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.get_feature_extractor()
      __lowerCamelCase    		=					self.get_tokenizer()

      __lowerCamelCase    		=					SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,				feature_extractor=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					floats_list((3, 1000)	)

      __lowerCamelCase    		=					feature_extractor(audio_target=_SCREAMING_SNAKE_CASE ,				return_tensors='np'	)
      __lowerCamelCase    		=					processor(audio_target=_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    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.get_feature_extractor()
      __lowerCamelCase    		=					self.get_tokenizer()

      __lowerCamelCase    		=					SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,				feature_extractor=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					'This is a test string'

      __lowerCamelCase    		=					processor(text=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					tokenizer(_SCREAMING_SNAKE_CASE	)

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







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.get_feature_extractor()
      __lowerCamelCase    		=					self.get_tokenizer()

      __lowerCamelCase    		=					SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,				feature_extractor=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					'This is a test string'

      __lowerCamelCase    		=					processor(text_target=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					tokenizer(_SCREAMING_SNAKE_CASE	)

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







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.get_feature_extractor()
      __lowerCamelCase    		=					self.get_tokenizer()

      __lowerCamelCase    		=					SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,				feature_extractor=_SCREAMING_SNAKE_CASE	)

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

      __lowerCamelCase    		=					processor.batch_decode(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					tokenizer.batch_decode(_SCREAMING_SNAKE_CASE	)

      self.assertListEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.get_feature_extractor()
      __lowerCamelCase    		=					self.get_tokenizer()

      __lowerCamelCase    		=					SpeechTaProcessor(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' ,				)








import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[Any] 						=     (inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					List[str] 						=     1
								return mask
