
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__				:  int       					=				get_tests_dir('''fixtures/test_sentencepiece.model''')



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




							'''simple docstring'''

							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										UpperCamelCase__    :		Optional[int]     =				tempfile.mkdtemp()

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

										UpperCamelCase__    :		Dict     =				SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
										tokenizer.save_pretrained(self.tmpdirname)

										UpperCamelCase__    :		Optional[int]     =				{
										    'feature_size': 24,
										    'num_mel_bins': 24,
										    'padding_value': 0.0,
										    'sampling_rate': 1_60_00,
										    'return_attention_mask': False,
										    'do_normalize': True,
										}
										save_json(UpperCamelCase     ,						save_dir / FEATURE_EXTRACTOR_NAME)
							def        lowerCAmelCase__ (				self     ,						**UpperCamelCase) ->	Optional[Any]:
										return SpeechaTextTokenizer.from_pretrained(self.tmpdirname     ,						**UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						**UpperCamelCase) ->	List[str]:
										return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname     ,						**UpperCamelCase)
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										shutil.rmtree(self.tmpdirname)
							def        lowerCAmelCase__ (				self) ->	List[str]:
										UpperCamelCase__    :		Optional[int]     =				self.get_tokenizer()
										UpperCamelCase__    :		Tuple     =				self.get_feature_extractor()

										UpperCamelCase__    :		int     =				SpeechaTextProcessor(tokenizer=UpperCamelCase     ,						feature_extractor=UpperCamelCase)

										processor.save_pretrained(self.tmpdirname)
										UpperCamelCase__    :		List[Any]     =				SpeechaTextProcessor.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        lowerCAmelCase__ (				self) ->	List[str]:
										UpperCamelCase__    :		Dict     =				SpeechaTextProcessor(
										    tokenizer=self.get_tokenizer()     ,						feature_extractor=self.get_feature_extractor())
										processor.save_pretrained(self.tmpdirname)

										UpperCamelCase__    :		int     =				self.get_tokenizer(bos_token='(BOS)'     ,						eos_token='(EOS)')
										UpperCamelCase__    :		List[str]     =				self.get_feature_extractor(do_normalize=UpperCamelCase     ,						padding_value=1.0)

										UpperCamelCase__    :		List[str]     =				SpeechaTextProcessor.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        lowerCAmelCase__ (				self) ->	Dict:
										UpperCamelCase__    :		Any     =				self.get_feature_extractor()
										UpperCamelCase__    :		Optional[int]     =				self.get_tokenizer()

										UpperCamelCase__    :		Union[str, Any]     =				SpeechaTextProcessor(tokenizer=UpperCamelCase     ,						feature_extractor=UpperCamelCase)

										UpperCamelCase__    :		int     =				floats_list((3, 10_00))

										UpperCamelCase__    :		int     =				feature_extractor(UpperCamelCase     ,						return_tensors='np')
										UpperCamelCase__    :		List[str]     =				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        lowerCAmelCase__ (				self) ->	List[Any]:
										UpperCamelCase__    :		Optional[Any]     =				self.get_feature_extractor()
										UpperCamelCase__    :		List[str]     =				self.get_tokenizer()

										UpperCamelCase__    :		Union[str, Any]     =				SpeechaTextProcessor(tokenizer=UpperCamelCase     ,						feature_extractor=UpperCamelCase)

										UpperCamelCase__    :		List[Any]     =				'This is a test string'

										UpperCamelCase__    :		List[Any]     =				processor(text=UpperCamelCase)

										UpperCamelCase__    :		Optional[Any]     =				tokenizer(UpperCamelCase)

										for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]     ,						encoded_processor[key])
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										UpperCamelCase__    :		Any     =				self.get_feature_extractor()
										UpperCamelCase__    :		Union[str, Any]     =				self.get_tokenizer()

										UpperCamelCase__    :		List[Any]     =				SpeechaTextProcessor(tokenizer=UpperCamelCase     ,						feature_extractor=UpperCamelCase)

										UpperCamelCase__    :		Optional[int]     =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										UpperCamelCase__    :		int     =				processor.batch_decode(UpperCamelCase)
										UpperCamelCase__    :		Optional[Any]     =				tokenizer.batch_decode(UpperCamelCase)

										self.assertListEqual(UpperCamelCase     ,						UpperCamelCase)






							def        lowerCAmelCase__ (				self) ->	List[Any]:
										UpperCamelCase__    :		Any     =				self.get_feature_extractor()
										UpperCamelCase__    :		int     =				self.get_tokenizer()

										UpperCamelCase__    :		Tuple     =				SpeechaTextProcessor(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'     ,						)





import numpy as np

UpperCAmelCase__				:  str       					=				[
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]



class 			UpperCamelCase_			:




							'''simple docstring'''

							def __init__(				self) ->	None:
										UpperCamelCase__    :		Any     =				np.array(UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	np.ndarray:
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				np.where(letter == self.SQUARE)
										UpperCamelCase__    :		int     =				np.concatenate([indexa + 1, indexa + 1])
										return indexes
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase) ->	str:
										return self.SQUARE[indexa - 1, indexa - 1]
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	str:
										UpperCamelCase__    :		str     =				message.lower()
										UpperCamelCase__    :		int     =				message.replace('j'     ,						'i')

										UpperCamelCase__    :		List[Any]     =				''
										for letter_index in range(len(UpperCamelCase)):
													if message[letter_index] != " ":
																UpperCamelCase__    :		Union[str, Any]     =				self.letter_to_numbers(message[letter_index])
																UpperCamelCase__    :		int     =				encoded_message + str(numbers[0]) + str(numbers[1])
													elif message[letter_index] == " ":
																UpperCamelCase__    :		str     =				encoded_message + ' '

										return encoded_message






							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	str:
										UpperCamelCase__    :		Union[str, Any]     =				message.replace(' '     ,						'  ')
										UpperCamelCase__    :		int     =				''
										for numbers_index in range(int(len(UpperCamelCase) / 2)):
													if message[numbers_index * 2] != " ":
																UpperCamelCase__    :		List[str]     =				message[numbers_index * 2]
																UpperCamelCase__    :		Tuple     =				message[numbers_index * 2 + 1]

																UpperCamelCase__    :		Tuple     =				self.numbers_to_letter(int(UpperCamelCase)     ,						int(UpperCamelCase))
																UpperCamelCase__    :		List[str]     =				decoded_message + letter
													elif message[numbers_index * 2] == " ":
																UpperCamelCase__    :		str     =				decoded_message + ' '

										return decoded_message



