



'''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


UpperCAmelCase			=    get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')

@require_torch
class __snake_case(      unittest.TestCase					):


						'''simple docstring'''



						def 				__snake_case    (					self      )	->	Union[str, Any]:
							lowerCAmelCase    =	tempfile.mkdtemp()

							lowerCAmelCase    =	SpeechTaTokenizer(A_      )
							tokenizer.save_pretrained(self.tmpdirname      )

							lowerCAmelCase    =	{
							    """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,
							}

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


						def 				__snake_case    (					self	,       **A_      )	->	Union[str, Any]:
							return SpeechTaTokenizer.from_pretrained(self.tmpdirname	,       **A_      )


						def 				__snake_case    (					self	,       **A_      )	->	Tuple:
							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname	,       **A_      )


						def 				__snake_case    (					self      )	->	int:
							shutil.rmtree(self.tmpdirname      )


						def 				__snake_case    (					self      )	->	List[Any]:
							lowerCAmelCase    =	self.get_tokenizer()
							lowerCAmelCase    =	self.get_feature_extractor()

							lowerCAmelCase    =	SpeechTaProcessor(tokenizer=A_	,       feature_extractor=A_      )

							processor.save_pretrained(self.tmpdirname      )
							lowerCAmelCase    =	SpeechTaProcessor.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 				__snake_case    (					self      )	->	Optional[int]:
							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=A_	,       padding_value=1.0      )

							lowerCAmelCase    =	SpeechTaProcessor.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 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	self.get_feature_extractor()
							lowerCAmelCase    =	self.get_tokenizer()

							lowerCAmelCase    =	SpeechTaProcessor(tokenizer=A_	,       feature_extractor=A_      )

							lowerCAmelCase    =	floats_list((3, 1000)      )

							lowerCAmelCase    =	feature_extractor(audio=A_	,       return_tensors="""np"""      )
							lowerCAmelCase    =	processor(audio=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 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	self.get_feature_extractor()
							lowerCAmelCase    =	self.get_tokenizer()

							lowerCAmelCase    =	SpeechTaProcessor(tokenizer=A_	,       feature_extractor=A_      )

							lowerCAmelCase    =	floats_list((3, 1000)      )

							lowerCAmelCase    =	feature_extractor(audio_target=A_	,       return_tensors="""np"""      )
							lowerCAmelCase    =	processor(audio_target=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 				__snake_case    (					self      )	->	Dict:
							lowerCAmelCase    =	self.get_feature_extractor()
							lowerCAmelCase    =	self.get_tokenizer()

							lowerCAmelCase    =	SpeechTaProcessor(tokenizer=A_	,       feature_extractor=A_      )

							lowerCAmelCase    =	"""This is a test string"""

							lowerCAmelCase    =	processor(text=A_      )
							lowerCAmelCase    =	tokenizer(A_      )

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


						def 				__snake_case    (					self      )	->	List[Any]:
							lowerCAmelCase    =	self.get_feature_extractor()
							lowerCAmelCase    =	self.get_tokenizer()

							lowerCAmelCase    =	SpeechTaProcessor(tokenizer=A_	,       feature_extractor=A_      )

							lowerCAmelCase    =	"""This is a test string"""

							lowerCAmelCase    =	processor(text_target=A_      )
							lowerCAmelCase    =	tokenizer(A_      )

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


						def 				__snake_case    (					self      )	->	str:
							lowerCAmelCase    =	self.get_feature_extractor()
							lowerCAmelCase    =	self.get_tokenizer()

							lowerCAmelCase    =	SpeechTaProcessor(tokenizer=A_	,       feature_extractor=A_      )

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

							lowerCAmelCase    =	processor.batch_decode(A_      )
							lowerCAmelCase    =	tokenizer.batch_decode(A_      )

							self.assertListEqual(A_	,       A_      )




						def 				__snake_case    (					self      )	->	List[str]:
							lowerCAmelCase    =	self.get_feature_extractor()
							lowerCAmelCase    =	self.get_tokenizer()

							lowerCAmelCase    =	SpeechTaProcessor(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"""	,       )




'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCAmelCase			=    TypeVar('T', bound=bool)

class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	None:
							lowerCAmelCase    =	value
							lowerCAmelCase    =	None
							lowerCAmelCase    =	None


						@property
						def 				__snake_case    (					self      )	->	T:
							return self._value




						@staticmethod
						def 				__snake_case    (					A_	,       A_      )	->	RandomizedHeapNode[T] | None:
							if not roota:
								return roota

							if not roota:
								return roota

							if roota.value > roota.value:
								lowerCAmelCase,   lowerCAmelCase    =	roota, roota

							if random.choice([True, False]      ):
								lowerCAmelCase,   lowerCAmelCase    =	roota.right, roota.left

							lowerCAmelCase    =	RandomizedHeapNode.merge(roota.left	,       A_      )

							return roota







class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_ = ()      )	->	None:
							lowerCAmelCase    =	None

							if data:
								for item in data:
									self.insert(A_      )


						def 				__snake_case    (					self	,       A_      )	->	None:
							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root	,       RandomizedHeapNode(A_      )      )


						def 				__snake_case    (					self      )	->	T | None:
							lowerCAmelCase    =	self.top()

							if self._root is None:
								return None

							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root.left	,       self._root.right      )

							return result


						def 				__snake_case    (					self      )	->	T:
							if not self._root:
								raise IndexError("""Can't get top element for the empty heap."""      )
							return self._root.value


						def 				__snake_case    (					self      )	->	None:
							lowerCAmelCase    =	None


						def 				__snake_case    (					self      )	->	list[Any]:
							lowerCAmelCase    =	[]
							while self:
								result.append(self.pop()      )

							return result




						def __bool__(					self      )	->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()