






"""simple docstring"""





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


_SCREAMING_SNAKE_CASE :    List[Any]    =						get_tests_dir('''fixtures/test_sentencepiece.model''')






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






				def        UpperCamelCase	(	self					:							int )					->		Tuple:
						lowerCamelCase_   					=						tempfile.mkdtemp()

						lowerCamelCase_   					=						['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
						lowerCamelCase_   					=						dict(zip(__SCREAMING_SNAKE_CASE	,						range(len(__SCREAMING_SNAKE_CASE ) ) ) )
						lowerCamelCase_   					=						Path(self.tmpdirname )
						save_json(__SCREAMING_SNAKE_CASE	,						save_dir / VOCAB_FILES_NAMES['vocab_file'] )
						if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
								copyfile(__SCREAMING_SNAKE_CASE	,						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': 16000,
						    'return_attention_mask': False,
						    'do_normalize': True,
						}
						save_json(__SCREAMING_SNAKE_CASE	,						save_dir / FEATURE_EXTRACTOR_NAME )






				def        UpperCamelCase	(	self					:							Optional[int]	,						**__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		Optional[int]:
						return SpeechaTextTokenizer.from_pretrained(self.tmpdirname	,						**__SCREAMING_SNAKE_CASE )






				def        UpperCamelCase	(	self					:							List[Any]	,						**__SCREAMING_SNAKE_CASE					:							Any )					->		Tuple:
						return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname	,						**__SCREAMING_SNAKE_CASE )






				def        UpperCamelCase	(	self					:							str )					->		str:
						shutil.rmtree(self.tmpdirname )






				def        UpperCamelCase	(	self					:							Optional[Any] )					->		Tuple:
						lowerCamelCase_   					=						self.get_tokenizer()
						lowerCamelCase_   					=						self.get_feature_extractor()

						lowerCamelCase_   					=						SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,						feature_extractor=__SCREAMING_SNAKE_CASE )

						processor.save_pretrained(self.tmpdirname )
						lowerCamelCase_   					=						SpeechaTextProcessor.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        UpperCamelCase	(	self					:							Dict )					->		Optional[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=__SCREAMING_SNAKE_CASE	,						padding_value=1.0 )

						lowerCamelCase_   					=						SpeechaTextProcessor.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        UpperCamelCase	(	self					:							Optional[int] )					->		List[Any]:
						lowerCamelCase_   					=						self.get_feature_extractor()
						lowerCamelCase_   					=						self.get_tokenizer()

						lowerCamelCase_   					=						SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,						feature_extractor=__SCREAMING_SNAKE_CASE )

						lowerCamelCase_   					=						floats_list((3, 1000) )

						lowerCamelCase_   					=						feature_extractor(__SCREAMING_SNAKE_CASE	,						return_tensors='np' )
						lowerCamelCase_   					=						processor(__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        UpperCamelCase	(	self					:							int )					->		Optional[Any]:
						lowerCamelCase_   					=						self.get_feature_extractor()
						lowerCamelCase_   					=						self.get_tokenizer()

						lowerCamelCase_   					=						SpeechaTextProcessor(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        UpperCamelCase	(	self					:							Union[str, Any] )					->		str:
						lowerCamelCase_   					=						self.get_feature_extractor()
						lowerCamelCase_   					=						self.get_tokenizer()

						lowerCamelCase_   					=						SpeechaTextProcessor(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        UpperCamelCase	(	self					:							List[Any] )					->		Optional[Any]:
						lowerCamelCase_   					=						self.get_feature_extractor()
						lowerCamelCase_   					=						self.get_tokenizer()

						lowerCamelCase_   					=						SpeechaTextProcessor(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"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int			)				->    int:
  if not root or root not in binary_tree_mirror_dictionary:
    return
  lowerCamelCase_					,     lowerCamelCase_   					=						binary_tree_mirror_dictionary[root][:2]
  lowerCamelCase_   					=						[right_child, left_child]
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int = 1			)				->    dict:
  if not binary_tree:
    raise ValueError('binary tree cannot be empty'			)
  if root not in binary_tree:
    lowerCamelCase_   					=						F'''root {root} is not present in the binary_tree'''
    raise ValueError(_lowerCamelCase			)
  lowerCamelCase_   					=						dict(_lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Tuple    =						{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F'''Binary tree: {binary_tree}''')
  _SCREAMING_SNAKE_CASE :    Dict    =						binary_tree_mirror(binary_tree, 5)
  print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')




