
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


UpperCamelCase_	:    Optional[Any]  					=       get_tests_dir("""fixtures/test_sentencepiece.model""")




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






	def 			_a(self							:   Optional[int]						)   -> List[str]:
							_lowercase					:  int    			=      tempfile.mkdtemp()

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

							_lowercase					:  List[Any]    			=      SpeechaTextTokenizer.from_pretrained(self.tmpdirname						)
							tokenizer.save_pretrained(self.tmpdirname						)

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






	def 			_a(self							:   List[Any]			,  **snake_case							:   Any						)   -> Any:
							return SpeechaTextTokenizer.from_pretrained(self.tmpdirname			,  **snake_case						)






	def 			_a(self							:   Tuple			,  **snake_case							:   Any						)   -> List[str]:
							return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname			,  **snake_case						)






	def 			_a(self							:   Optional[Any]						)   -> Optional[int]:
							shutil.rmtree(self.tmpdirname						)






	def 			_a(self							:   Optional[Any]						)   -> Dict:
							_lowercase					:  Union[str, Any]    			=      self.get_tokenizer()
							_lowercase					:  Tuple    			=      self.get_feature_extractor()

							_lowercase					:  Optional[int]    			=      SpeechaTextProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)

							processor.save_pretrained(self.tmpdirname						)
							_lowercase					:  List[str]    			=      SpeechaTextProcessor.from_pretrained(self.tmpdirname						)

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

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






	def 			_a(self							:   Optional[int]						)   -> str:
							_lowercase					:  Optional[int]    			=      SpeechaTextProcessor(
							    tokenizer=self.get_tokenizer()			,  feature_extractor=self.get_feature_extractor()						)
							processor.save_pretrained(self.tmpdirname						)

							_lowercase					:  Any    			=      self.get_tokenizer(bos_token="(BOS)"			,  eos_token="(EOS)"						)
							_lowercase					:  List[Any]    			=      self.get_feature_extractor(do_normalize=snake_case			,  padding_value=1.0						)

							_lowercase					:  int    			=      SpeechaTextProcessor.from_pretrained(
							    self.tmpdirname			,  bos_token="(BOS)"			,  eos_token="(EOS)"			,  do_normalize=snake_case			,  padding_value=1.0						)

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

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






	def 			_a(self							:   List[Any]						)   -> Union[str, Any]:
							_lowercase					:  List[str]    			=      self.get_feature_extractor()
							_lowercase					:  Optional[Any]    			=      self.get_tokenizer()

							_lowercase					:  List[Any]    			=      SpeechaTextProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)

							_lowercase					:  Dict    			=      floats_list((3, 1000)						)

							_lowercase					:  Optional[int]    			=      feature_extractor(snake_case			,  return_tensors="np"						)
							_lowercase					:  Optional[Any]    			=      processor(snake_case			,  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							:   Tuple						)   -> Union[str, Any]:
							_lowercase					:  List[Any]    			=      self.get_feature_extractor()
							_lowercase					:  Tuple    			=      self.get_tokenizer()

							_lowercase					:  Optional[int]    			=      SpeechaTextProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)

							_lowercase					:  int    			=      "This is a test string"

							_lowercase					:  Any    			=      processor(text=snake_case						)

							_lowercase					:  int    			=      tokenizer(snake_case						)

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






	def 			_a(self							:   Dict						)   -> List[str]:
							_lowercase					:  Optional[Any]    			=      self.get_feature_extractor()
							_lowercase					:  Union[str, Any]    			=      self.get_tokenizer()

							_lowercase					:  str    			=      SpeechaTextProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)

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

							_lowercase					:  List[Any]    			=      processor.batch_decode(snake_case						)
							_lowercase					:  Dict    			=      tokenizer.batch_decode(snake_case						)

							self.assertListEqual(snake_case			,  snake_case						)






	def 			_a(self							:   List[Any]						)   -> Any:
							_lowercase					:  Tuple    			=      self.get_feature_extractor()
							_lowercase					:  int    			=      self.get_tokenizer()

							_lowercase					:  Union[str, Any]    			=      SpeechaTextProcessor(tokenizer=snake_case			,  feature_extractor=snake_case						)

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






import os



def 		UpperCamelCase    (					)      -> Optional[int]:






						'''simple docstring'''

						_lowercase					:  Tuple    			=      0
						_lowercase					:  List[Any]    			=      0
						with open(os.path.dirname(_UpperCAmelCase     ) + "/p022_names.txt"     ) as file:
												_lowercase					:  Any    			=      str(file.readlines()[0]     )
												_lowercase					:  int    			=      name.replace("\""   , ""     ).split(","     )

						name.sort()
						for i in range(len(_UpperCAmelCase     )     ):
												for j in name[i]:
																		temp_sum += ord(_UpperCAmelCase     ) - ord("A"     ) + 1
												total_sum += (i + 1) * temp_sum
												_lowercase					:  Tuple    			=      0
						return total_sum


if __name__ == "__main__":
							print(solution())




