

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 					__UpperCamelCase					(       unittest.TestCase      ):







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     '<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(' '   )
						__magic_name__				=     dict(zip(__UpperCamelCase	,			range(len(__UpperCamelCase   )   )   )   )

						__magic_name__				=     {
						    'pad_token': '<pad>',
						    'unk_token': '<unk>',
						    'bos_token': '<s>',
						    'eos_token': '</s>',
						}
						__magic_name__				=     {
						    'feature_size': 1,
						    'padding_value': 0.0,
						    'sampling_rate': 1_60_00,
						    'return_attention_mask': False,
						    'do_normalize': True,
						}

						__magic_name__				=     tempfile.mkdtemp()
						__magic_name__				=     os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['vocab_file']   )
						__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			int	,			**__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     self.add_kwargs_tokens_map.copy()
						kwargs.update(__UpperCamelCase   )
						return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,			**__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]	,			**__UpperCamelCase:			List[str]   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     self.get_tokenizer()
						__magic_name__				=     self.get_feature_extractor()

						__magic_name__				=     WavaVecaProcessor(tokenizer=__UpperCamelCase	,			feature_extractor=__UpperCamelCase   )

						processor.save_pretrained(self.tmpdirname   )
						__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





						__magic_name__				=     WavaVecaProcessor(tokenizer=self.get_tokenizer()	,			feature_extractor=self.get_feature_extractor()   )
						processor.save_pretrained(self.tmpdirname   )

						__magic_name__				=     self.get_tokenizer(bos_token='(BOS)'	,			eos_token='(EOS)'   )
						__magic_name__				=     self.get_feature_extractor(do_normalize=__UpperCamelCase	,			padding_value=1.0   )

						__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.get_feature_extractor()
						__magic_name__				=     self.get_tokenizer()

						__magic_name__				=     WavaVecaProcessor(tokenizer=__UpperCamelCase	,			feature_extractor=__UpperCamelCase   )

						__magic_name__				=     floats_list((3, 10_00)   )

						__magic_name__				=     feature_extractor(__UpperCamelCase	,			return_tensors='np'   )
						__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     self.get_feature_extractor()
						__magic_name__				=     self.get_tokenizer()

						__magic_name__				=     WavaVecaProcessor(tokenizer=__UpperCamelCase	,			feature_extractor=__UpperCamelCase   )

						__magic_name__				=     'This is a test string'

						__magic_name__				=     processor(text=__UpperCamelCase   )

						__magic_name__				=     tokenizer(__UpperCamelCase   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.get_feature_extractor()
						__magic_name__				=     self.get_tokenizer()

						__magic_name__				=     WavaVecaProcessor(tokenizer=__UpperCamelCase	,			feature_extractor=__UpperCamelCase   )

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

						__magic_name__				=     processor.batch_decode(__UpperCamelCase   )
						__magic_name__				=     tokenizer.batch_decode(__UpperCamelCase   )

						self.assertListEqual(__UpperCamelCase	,			__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     self.get_feature_extractor()
						__magic_name__				=     self.get_tokenizer()

						__magic_name__				=     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'	,			)







def        _lowercase  (					a_					:       str       ,a_					:       List[str]					)					->						Optional[Any]:




					'''simple docstring'''







					sequence.append(a_					)

					__magic_name__				=     0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(a_					):
										return None

					return index


if __name__ == "__main__":
						A__							=				input("Enter numbers separated by comma:\n").strip()
						A__							=				[int(item) for item in user_input.split(",")]

						A__							=				input("Enter a single number to be found in the list:\n")
						A__							=				int(target_input)
						A__							=				sentinel_linear_search(sequence, target)
						if result is not None:
												print(f'''{target} found at positions: {result}''')
						else:
												print("Not found")




