





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 							__snake_case    (				unittest.TestCase       ):





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      '<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(' '		)
		snake_case__  					=      dict(zip(UpperCamelCase_			,					range(len(UpperCamelCase_		)		)		)		)

		snake_case__  					=      {
		    'pad_token': '<pad>',
		    'unk_token': '<unk>',
		    'bos_token': '<s>',
		    'eos_token': '</s>',
		}
		snake_case__  					=      {
		    'feature_size': 1,
		    'padding_value': 0.0,
		    'sampling_rate': 1_6000,
		    'return_attention_mask': False,
		    'do_normalize': True,
		}

		snake_case__  					=      tempfile.mkdtemp()
		snake_case__  					=      os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['vocab_file']		)
		snake_case__  					=      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      _snake_case      (		self			,					**UpperCamelCase_		)			->		List[str]:
		snake_case__  					=      self.add_kwargs_tokens_map.copy()
		kwargs.update(UpperCamelCase_		)
		return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		str:
		return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self		)			->		List[str]:
		shutil.rmtree(self.tmpdirname		)





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      self.get_tokenizer()
		snake_case__  					=      self.get_feature_extractor()

		snake_case__  					=      WavaVecaProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)

		processor.save_pretrained(self.tmpdirname		)
		snake_case__  					=      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      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      WavaVecaProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
		processor.save_pretrained(self.tmpdirname		)

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

		snake_case__  					=      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      _snake_case      (		self		)			->		str:
		snake_case__  					=      self.get_feature_extractor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      WavaVecaProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)

		snake_case__  					=      floats_list((3, 1000)		)

		snake_case__  					=      feature_extractor(UpperCamelCase_			,					return_tensors='np'		)
		snake_case__  					=      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      _snake_case      (		self		)			->		Optional[Any]:
		snake_case__  					=      self.get_feature_extractor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      WavaVecaProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)

		snake_case__  					=      'This is a test string'

		snake_case__  					=      processor(text=UpperCamelCase_		)

		snake_case__  					=      tokenizer(UpperCamelCase_		)

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





	def      _snake_case      (		self		)			->		List[Any]:
		snake_case__  					=      self.get_feature_extractor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      WavaVecaProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)

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

		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)

		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Any:
		snake_case__  					=      self.get_feature_extractor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      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'			,					)










'''simple docstring'''




a__				: Any									=    {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

a__				: List[str]									=    [0 for x in range(len(graph))]
a__				: List[Any]									=    []




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
	snake_case__  					=      1
	while stack:
		snake_case__  					=      stack.pop()
		print(UpperCAmelCase_				,							clothes[current_clothing]       )
		order += 1




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
	snake_case__  					=      1
	for v in graph[u]:
		if not visited[v]:
			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )

	stack.append(UpperCAmelCase_       )




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
	for v in range(len(UpperCAmelCase_       )       ):
		if not visited[v]:
			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )


if __name__ == "__main__":
			topological_sort(graph, visited)
			print(stack)
			print_stack(stack, clothes)



