






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


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




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


						'''simple docstring'''




						def 					_lowerCAmelCase				(		self )       -> List[Any]:
								snake_case_ :							Any         =				tempfile.mkdtemp()

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

								snake_case_ :							Optional[Any]         =				SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
								tokenizer.save_pretrained(self.tmpdirname )

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






						def 					_lowerCAmelCase				(		self						, **_SCREAMING_SNAKE_CASE )       -> Any:
								return SpeechaTextTokenizer.from_pretrained(self.tmpdirname						, **_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self						, **_SCREAMING_SNAKE_CASE )       -> List[str]:
								return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname						, **_SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> Any:
								shutil.rmtree(self.tmpdirname )






						def 					_lowerCAmelCase				(		self )       -> str:
								snake_case_ :							List[Any]         =				self.get_tokenizer()
								snake_case_ :							List[str]         =				self.get_feature_extractor()

								snake_case_ :							List[Any]         =				SpeechaTextProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, feature_extractor=_SCREAMING_SNAKE_CASE )

								processor.save_pretrained(self.tmpdirname )
								snake_case_ :							Tuple         =				SpeechaTextProcessor.from_pretrained(self.tmpdirname )

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

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






						def 					_lowerCAmelCase				(		self )       -> Any:
								snake_case_ :							List[Any]         =				SpeechaTextProcessor(
								    tokenizer=self.get_tokenizer()						, feature_extractor=self.get_feature_extractor() )
								processor.save_pretrained(self.tmpdirname )

								snake_case_ :							Optional[Any]         =				self.get_tokenizer(bos_token="(BOS)"						, eos_token="(EOS)" )
								snake_case_ :							Any         =				self.get_feature_extractor(do_normalize=_SCREAMING_SNAKE_CASE						, padding_value=1.0 )

								snake_case_ :							List[Any]         =				SpeechaTextProcessor.from_pretrained(
								    self.tmpdirname						, bos_token="(BOS)"						, eos_token="(EOS)"						, do_normalize=_SCREAMING_SNAKE_CASE						, padding_value=1.0 )

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

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






						def 					_lowerCAmelCase				(		self )       -> List[Any]:
								snake_case_ :							List[str]         =				self.get_feature_extractor()
								snake_case_ :							Any         =				self.get_tokenizer()

								snake_case_ :							List[str]         =				SpeechaTextProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, feature_extractor=_SCREAMING_SNAKE_CASE )

								snake_case_ :							Any         =				floats_list((3, 1000) )

								snake_case_ :							int         =				feature_extractor(_SCREAMING_SNAKE_CASE						, return_tensors="np" )
								snake_case_ :							List[str]         =				processor(_SCREAMING_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 					_lowerCAmelCase				(		self )       -> Tuple:
								snake_case_ :							List[str]         =				self.get_feature_extractor()
								snake_case_ :							Optional[Any]         =				self.get_tokenizer()

								snake_case_ :							str         =				SpeechaTextProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, feature_extractor=_SCREAMING_SNAKE_CASE )

								snake_case_ :							int         =				"This is a test string"

								snake_case_ :							Any         =				processor(text=_SCREAMING_SNAKE_CASE )

								snake_case_ :							Union[str, Any]         =				tokenizer(_SCREAMING_SNAKE_CASE )

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






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

								snake_case_ :							Any         =				SpeechaTextProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, feature_extractor=_SCREAMING_SNAKE_CASE )

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

								snake_case_ :							List[str]         =				processor.batch_decode(_SCREAMING_SNAKE_CASE )
								snake_case_ :							List[Any]         =				tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )

								self.assertListEqual(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> List[str]:
								snake_case_ :							Dict         =				self.get_feature_extractor()
								snake_case_ :							Tuple         =				self.get_tokenizer()

								snake_case_ :							str         =				SpeechaTextProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, feature_extractor=_SCREAMING_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 glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
							lowercase     :							List[Any]	     =			[]

							lowercase     :							Optional[int]	     =			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
							lowercase     :							Optional[Any]	     =			glob.glob(pattern)
							# TODO: deal with TF/Flax too
							lowercase     :							Tuple	     =			[
							    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
							]

							for test_file in test_files:
														lowercase     :							Union[str, Any]	     =			get_tester_classes(test_file)
														for tester_class in tester_classes:
																					# A few tester classes don't have `parent` parameter in `__init__`.
																					# TODO: deal this better
																					try:
																												lowercase     :							Union[str, Any]	     =			tester_class(parent=None)
																					except Exception:
																												continue
																					if hasattr(tester, '''get_config'''):
																												lowercase     :							Tuple	     =			tester.get_config()
																												for k, v in config.to_dict().items():
																																			if isinstance(v, int):
																																										lowercase     :							Tuple	     =			None
																																										if k in ["vocab_size"]:
																																																	lowercase     :							str	     =			1_00
																																										elif k in ["max_position_embeddings"]:
																																																	lowercase     :							List[str]	     =			1_28
																																										elif k in ["hidden_size", "d_model"]:
																																																	lowercase     :							Union[str, Any]	     =			40
																																										elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																																	lowercase     :							List[str]	     =			5
																																										if target is not None and v > target:
																																																	failures.append(
																																																	    F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
																																																	    F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
																																																	    F""" than {target}."""
																																																	)

							if len(failures) > 0:
														raise Exception(F"""There were {len(failures)} failures:\n""" + '''\n'''.join(failures))
