



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 					a_  (    self  :   Tuple					)       ->		str:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[int]     					=	'''<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(''' '''					)
						_UpperCAmelCase						:  Any     					=	dict(zip(UpperCAmelCase_       ,		range(len(UpperCAmelCase_					)					)					)					)

						_UpperCAmelCase						:  Any     					=	{
						    '''pad_token''': '''<pad>''',
						    '''unk_token''': '''<unk>''',
						    '''bos_token''': '''<s>''',
						    '''eos_token''': '''</s>''',
						}
						_UpperCAmelCase						:  List[Any]     					=	{
						    '''feature_size''': 1,
						    '''padding_value''': 0.0,
						    '''sampling_rate''': 16000,
						    '''return_attention_mask''': False,
						    '''do_normalize''': True,
						}

						_UpperCAmelCase						:  Tuple     					=	tempfile.mkdtemp()
						_UpperCAmelCase						:  Union[str, Any]     					=	os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES['''vocab_file''']					)
						_UpperCAmelCase						:  List[str]     					=	os.path.join(self.tmpdirname       ,		UpperCAmelCase_					)
						with open(self.vocab_file       ,		'''w'''       ,		encoding='''utf-8'''					) as fp:
										fp.write(json.dumps(UpperCAmelCase_					) + '''\n'''					)

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







		def 					a_  (    self  :   Dict       ,		**UpperCAmelCase_  :   str					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	self.add_kwargs_tokens_map.copy()
						kwargs.update(UpperCAmelCase_					)
						return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,		**UpperCAmelCase_					)







		def 					a_  (    self  :   Optional[Any]       ,		**UpperCAmelCase_  :   List[Any]					)       ->		Union[str, Any]:


						'''simple docstring'''







						return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,		**UpperCAmelCase_					)







		def 					a_  (    self  :   Optional[Any]					)       ->		Dict:


						'''simple docstring'''







						shutil.rmtree(self.tmpdirname					)







		def 					a_  (    self  :   str					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	self.get_tokenizer()
						_UpperCAmelCase						:  Dict     					=	self.get_feature_extractor()

						_UpperCAmelCase						:  Union[str, Any]     					=	WavaVecaProcessor(tokenizer=UpperCAmelCase_       ,		feature_extractor=UpperCAmelCase_					)

						processor.save_pretrained(self.tmpdirname					)
						_UpperCAmelCase						:  List[str]     					=	WavaVecaProcessor.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 					a_  (    self  :   Optional[Any]					)       ->		List[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,		feature_extractor=self.get_feature_extractor()					)
						processor.save_pretrained(self.tmpdirname					)

						_UpperCAmelCase						:  List[Any]     					=	self.get_tokenizer(bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''					)
						_UpperCAmelCase						:  Optional[Any]     					=	self.get_feature_extractor(do_normalize=UpperCAmelCase_       ,		padding_value=1.0					)

						_UpperCAmelCase						:  Optional[Any]     					=	WavaVecaProcessor.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 					a_  (    self  :   int					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[int]     					=	self.get_feature_extractor()
						_UpperCAmelCase						:  List[str]     					=	self.get_tokenizer()

						_UpperCAmelCase						:  Optional[int]     					=	WavaVecaProcessor(tokenizer=UpperCAmelCase_       ,		feature_extractor=UpperCAmelCase_					)

						_UpperCAmelCase						:  int     					=	floats_list((3, 1000)					)

						_UpperCAmelCase						:  Any     					=	feature_extractor(UpperCAmelCase_       ,		return_tensors='''np'''					)
						_UpperCAmelCase						:  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 					a_  (    self  :   Any					)       ->		Tuple:


						'''simple docstring'''







						_UpperCAmelCase						:  List[Any]     					=	self.get_feature_extractor()
						_UpperCAmelCase						:  Union[str, Any]     					=	self.get_tokenizer()

						_UpperCAmelCase						:  Tuple     					=	WavaVecaProcessor(tokenizer=UpperCAmelCase_       ,		feature_extractor=UpperCAmelCase_					)

						_UpperCAmelCase						:  Optional[Any]     					=	'''This is a test string'''

						_UpperCAmelCase						:  List[str]     					=	processor(text=UpperCAmelCase_					)

						_UpperCAmelCase						:  List[str]     					=	tokenizer(UpperCAmelCase_					)

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







		def 					a_  (    self  :   int					)       ->		Any:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	self.get_feature_extractor()
						_UpperCAmelCase						:  Dict     					=	self.get_tokenizer()

						_UpperCAmelCase						:  str     					=	WavaVecaProcessor(tokenizer=UpperCAmelCase_       ,		feature_extractor=UpperCAmelCase_					)

						_UpperCAmelCase						:  Tuple     					=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						_UpperCAmelCase						:  Tuple     					=	processor.batch_decode(UpperCAmelCase_					)
						_UpperCAmelCase						:  List[Any]     					=	tokenizer.batch_decode(UpperCAmelCase_					)

						self.assertListEqual(UpperCAmelCase_       ,		UpperCAmelCase_					)







		def 					a_  (    self  :   Any					)       ->		List[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[Any]     					=	self.get_feature_extractor()
						_UpperCAmelCase						:  Any     					=	self.get_tokenizer()

						_UpperCAmelCase						:  Dict     					=	WavaVecaProcessor(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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


UpperCAmelCase__			:   Optional[Any]      			=    {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   List[Any]      			=    ['MBart50Tokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   int      			=    ['MBart50TokenizerFast']


if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbartaa import MBartaaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
					import sys

					UpperCAmelCase__			:   str      			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
