



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 				lowerCAmelCase__	(						unittest.TestCase    ):







					def 			_lowercase      (							self   :  Tuple):
											A__						:				List[Any]         =			"<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z".split(" ")
											A__						:				Optional[int]         =			dict(zip(_A      ,		range(len(_A))))

											A__						:				Tuple         =			{
											    "pad_token": "<pad>",
											    "unk_token": "<unk>",
											    "bos_token": "<s>",
											    "eos_token": "</s>",
											}
											A__						:				List[str]         =			{
											    "feature_size": 1,
											    "padding_value": 0.0,
											    "sampling_rate": 1_6000,
											    "return_attention_mask": False,
											    "do_normalize": True,
											}

											A__						:				Tuple         =			tempfile.mkdtemp()
											A__						:				Optional[int]         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["vocab_file"])
											A__						:				Any         =			os.path.join(self.tmpdirname      ,		_A)
											with open(self.vocab_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write(json.dumps(_A) + "\n")

											with open(self.feature_extraction_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write(json.dumps(_A) + "\n")







					def 			_lowercase      (							self   :  Optional[int]      ,		**_A   :  str):
											A__						:				Optional[int]         =			self.add_kwargs_tokens_map.copy()
											kwargs.update(_A)
											return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Any      ,		**_A   :  List[str]):
											return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,		**_A)







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







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

											A__						:				Optional[Any]         =			WavaVecaProcessor(tokenizer=_A      ,		feature_extractor=_A)

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

											A__						:				List[str]         =			self.get_tokenizer(bos_token="(BOS)"      ,		eos_token="(EOS)")
											A__						:				Union[str, Any]         =			self.get_feature_extractor(do_normalize=_A      ,		padding_value=1.0)

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

											A__						:				Tuple         =			WavaVecaProcessor(tokenizer=_A      ,		feature_extractor=_A)

											A__						:				Any         =			floats_list((3, 1000))

											A__						:				List[str]         =			feature_extractor(_A      ,		return_tensors="np")
											A__						:				Dict         =			processor(_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 			_lowercase      (							self   :  str):
											A__						:				str         =			self.get_feature_extractor()
											A__						:				Optional[int]         =			self.get_tokenizer()

											A__						:				Optional[int]         =			WavaVecaProcessor(tokenizer=_A      ,		feature_extractor=_A)

											A__						:				List[Any]         =			"This is a test string"

											A__						:				Union[str, Any]         =			processor(text=_A)

											A__						:				Tuple         =			tokenizer(_A)

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







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

											A__						:				str         =			WavaVecaProcessor(tokenizer=_A      ,		feature_extractor=_A)

											A__						:				List[Any]         =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											A__						:				Optional[int]         =			processor.batch_decode(_A)
											A__						:				List[str]         =			tokenizer.batch_decode(_A)

											self.assertListEqual(_A      ,		_A)







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

											A__						:				List[Any]         =			WavaVecaProcessor(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"      ,		)






from math import pi




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						float						):
     return degree / (180 / pi)


if __name__ == "__main__":
    from doctest import testmod

    testmod()