



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


snake_case     :    Union[str, Any]						=    get_tests_dir('fixtures/test_sentencepiece.model')




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







					def 			_lowercase      (							self   :  Optional[Any]):
											A__						:				List[Any]         =			tempfile.mkdtemp()

											A__						:				Union[str, Any]         =			["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
											A__						:				Dict         =			dict(zip(UpperCamelCase_      ,		range(len(UpperCamelCase_))))
											A__						:				Dict         =			Path(self.tmpdirname)
											save_json(UpperCamelCase_      ,		save_dir / VOCAB_FILES_NAMES["vocab_file"])
											if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																	copyfile(UpperCamelCase_      ,		save_dir / VOCAB_FILES_NAMES["spm_file"])

											A__						:				Tuple         =			SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
											tokenizer.save_pretrained(self.tmpdirname)

											A__						:				int         =			{
											    "feature_size": 24,
											    "num_mel_bins": 24,
											    "padding_value": 0.0,
											    "sampling_rate": 1_6000,
											    "return_attention_mask": False,
											    "do_normalize": True,
											}
											save_json(UpperCamelCase_      ,		save_dir / FEATURE_EXTRACTOR_NAME)







					def 			_lowercase      (							self   :  Tuple      ,		**_A   :  List[str]):
											return SpeechaTextTokenizer.from_pretrained(self.tmpdirname      ,		**UpperCamelCase_)







					def 			_lowercase      (							self   :  int      ,		**_A   :  int):
											return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname      ,		**UpperCamelCase_)







					def 			_lowercase      (							self   :  int):
											shutil.rmtree(self.tmpdirname)







					def 			_lowercase      (							self   :  Optional[int]):
											A__						:				Dict         =			self.get_tokenizer()
											A__						:				List[Any]         =			self.get_feature_extractor()

											A__						:				Any         =			SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,		feature_extractor=UpperCamelCase_)

											processor.save_pretrained(self.tmpdirname)
											A__						:				List[Any]         =			SpeechaTextProcessor.from_pretrained(self.tmpdirname)

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

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







					def 			_lowercase      (							self   :  Tuple):
											A__						:				Any         =			SpeechaTextProcessor(
											    tokenizer=self.get_tokenizer()      ,		feature_extractor=self.get_feature_extractor())
											processor.save_pretrained(self.tmpdirname)

											A__						:				int         =			self.get_tokenizer(bos_token="(BOS)"      ,		eos_token="(EOS)")
											A__						:				Optional[int]         =			self.get_feature_extractor(do_normalize=UpperCamelCase_      ,		padding_value=1.0)

											A__						:				Optional[Any]         =			SpeechaTextProcessor.from_pretrained(
											    self.tmpdirname      ,		bos_token="(BOS)"      ,		eos_token="(EOS)"      ,		do_normalize=UpperCamelCase_      ,		padding_value=1.0)

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

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







					def 			_lowercase      (							self   :  List[Any]):
											A__						:				str         =			self.get_feature_extractor()
											A__						:				List[str]         =			self.get_tokenizer()

											A__						:				Optional[int]         =			SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,		feature_extractor=UpperCamelCase_)

											A__						:				Optional[int]         =			floats_list((3, 1000))

											A__						:				int         =			feature_extractor(UpperCamelCase_      ,		return_tensors="np")
											A__						:				Optional[Any]         =			processor(UpperCamelCase_      ,		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 			_lowercase      (							self   :  Optional[int]):
											A__						:				str         =			self.get_feature_extractor()
											A__						:				Tuple         =			self.get_tokenizer()

											A__						:				Tuple         =			SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,		feature_extractor=UpperCamelCase_)

											A__						:				Any         =			"This is a test string"

											A__						:				Tuple         =			processor(text=UpperCamelCase_)

											A__						:				Tuple         =			tokenizer(UpperCamelCase_)

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







					def 			_lowercase      (							self   :  Union[str, Any]):
											A__						:				Optional[int]         =			self.get_feature_extractor()
											A__						:				int         =			self.get_tokenizer()

											A__						:				int         =			SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,		feature_extractor=UpperCamelCase_)

											A__						:				Optional[int]         =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											A__						:				List[Any]         =			processor.batch_decode(UpperCamelCase_)
											A__						:				Optional[Any]         =			tokenizer.batch_decode(UpperCamelCase_)

											self.assertListEqual(UpperCamelCase_      ,		UpperCamelCase_)







					def 			_lowercase      (							self   :  Optional[Any]):
											A__						:				Union[str, Any]         =			self.get_feature_extractor()
											A__						:				Union[str, Any]         =			self.get_tokenizer()

											A__						:				int         =			SpeechaTextProcessor(tokenizer=UpperCamelCase_      ,		feature_extractor=UpperCamelCase_)

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





from typing import Callable




def    lowerCamelCase__			(		A__			:   Callable      ):





       '''simple docstring'''



       def wrapper(A__			:   int      ):
              __lowerCamelCase  =		function_with_docstring.__doc__
              return target_function

       return wrapper

