





"""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


a__					:     List[str]							=				get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")



@require_torch
class      lowercase_				(     unittest.TestCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				tempfile.mkdtemp()

        snake_case__    							=				SpeechTaTokenizer(__SCREAMING_SNAKE_CASE     )
        tokenizer.save_pretrained(self.tmpdirname     )

        snake_case__    							=				{
            "feature_size": 1,
            "padding_value": 0.0,
            "sampling_rate": 1_6000,
            "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,
        }

        snake_case__    							=				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 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        shutil.rmtree(self.tmpdirname     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_tokenizer()
        snake_case__    							=				self.get_feature_extractor()

        snake_case__    							=				SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					feature_extractor=__SCREAMING_SNAKE_CASE     )

        processor.save_pretrained(self.tmpdirname     )
        snake_case__    							=				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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,					feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        snake_case__    							=				self.get_tokenizer(bos_token="(BOS)"   ,					eos_token="(EOS)"     )
        snake_case__    							=				self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE   ,					padding_value=1.0     )

        snake_case__    							=				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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_feature_extractor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					feature_extractor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				floats_list((3, 1000)     )

        snake_case__    							=				feature_extractor(audio=__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )
        snake_case__    							=				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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_feature_extractor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					feature_extractor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				floats_list((3, 1000)     )

        snake_case__    							=				feature_extractor(audio_target=__SCREAMING_SNAKE_CASE   ,					return_tensors="np"     )
        snake_case__    							=				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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_feature_extractor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					feature_extractor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				"This is a test string"

        snake_case__    							=				processor(text=__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				tokenizer(__SCREAMING_SNAKE_CASE     )

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_feature_extractor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					feature_extractor=__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				"This is a test string"

        snake_case__    							=				processor(text_target=__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				tokenizer(__SCREAMING_SNAKE_CASE     )

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_feature_extractor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,					feature_extractor=__SCREAMING_SNAKE_CASE     )

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

        snake_case__    							=				processor.batch_decode(__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     )

        self.assertListEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.get_feature_extractor()
        snake_case__    							=				self.get_tokenizer()

        snake_case__    							=				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"   ,					)








"""simple docstring"""


class      lowercase_				:

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				data
        snake_case__    							=				42
        snake_case__    							=				42





def 							a__			(  _UpperCAmelCase	)    ->  None:

 '''simple docstring'''






 snake_case__    							=				0
 snake_case__    							=				x





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  None:

 '''simple docstring'''






 snake_case__							,   snake_case__    							=				find_set(_UpperCAmelCase	), find_set(_UpperCAmelCase	)
 if x == y:
  return

 elif x.rank > y.rank:
  snake_case__    							=				x
 else:
  snake_case__    							=				y
  if x.rank == y.rank:
   y.rank += 1





def 							a__			(  _UpperCAmelCase	)    ->  Node:

 '''simple docstring'''






 if x != x.parent:
  snake_case__    							=				find_set(x.parent	)
 return x.parent





def 							a__			(  _UpperCAmelCase	)    ->  set:

 '''simple docstring'''






 snake_case__    							=				({0, 1, 2}, {3, 4, 5})
 for s in sets:
  if node.data in s:
   return s
 snake_case__    							=				f"{node.data} is not in {sets}"
 raise ValueError(_UpperCAmelCase	)





def 							a__			(  )    ->  None:

 '''simple docstring'''






 snake_case__    							=				[Node(_UpperCAmelCase	) for i in range(6	)]
 for v in vertex:
  make_set(_UpperCAmelCase	)

 union_set(vertex[0]	,     vertex[1]	)
 union_set(vertex[1]	,     vertex[2]	)
 union_set(vertex[3]	,     vertex[4]	)
 union_set(vertex[3]	,     vertex[5]	)

 for nodea in vertex:
  for nodea in vertex:
   if find_python_set(_UpperCAmelCase	).isdisjoint(find_python_set(_UpperCAmelCase	)	):
    assert find_set(_UpperCAmelCase	) != find_set(_UpperCAmelCase	)
   else:
    assert find_set(_UpperCAmelCase	) == find_set(_UpperCAmelCase	)


if __name__ == "__main__":
   test_disjoint_set()

