





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						=      get_tests_dir('fixtures/test_sentencepiece.model')

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



       def  A							(     self	:   Dict			):
           lowerCAmelCase_					:	Any						=   tempfile.mkdtemp()

           lowerCAmelCase_					:	Union[str, Any]						=   ["""<s>""", """<pad>""", """</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est"""]
           lowerCAmelCase_					:	List[Any]						=   dict(zip(UpperCAmelCase					,			range(len(UpperCAmelCase			)			)			)			)
           lowerCAmelCase_					:	int						=   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"""]			)

           lowerCAmelCase_					:	Union[str, Any]						=   SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
           tokenizer.save_pretrained(self.tmpdirname			)

           lowerCAmelCase_					:	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  A							(     self	:   str					,			**UpperCAmelCase	:   List[str]			):
           return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   str					,			**UpperCAmelCase	:   Union[str, Any]			):
           return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           shutil.rmtree(self.tmpdirname			)



       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	int						=   self.get_tokenizer()
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_feature_extractor()

           lowerCAmelCase_					:	Optional[int]						=   SpeechaTextProcessor(tokenizer=UpperCAmelCase					,			feature_extractor=UpperCAmelCase			)

           processor.save_pretrained(self.tmpdirname			)
           lowerCAmelCase_					:	Optional[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  A							(     self	:   Any			):
           lowerCAmelCase_					:	Optional[int]						=   SpeechaTextProcessor(
               tokenizer=self.get_tokenizer()					,			feature_extractor=self.get_feature_extractor()			)
           processor.save_pretrained(self.tmpdirname			)

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

           lowerCAmelCase_					:	Dict						=   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  A							(     self	:   Any			):
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_feature_extractor()
           lowerCAmelCase_					:	Tuple						=   self.get_tokenizer()

           lowerCAmelCase_					:	Union[str, Any]						=   SpeechaTextProcessor(tokenizer=UpperCAmelCase					,			feature_extractor=UpperCAmelCase			)

           lowerCAmelCase_					:	Any						=   floats_list((3, 10_00)			)

           lowerCAmelCase_					:	List[str]						=   feature_extractor(UpperCAmelCase					,			return_tensors="""np"""			)
           lowerCAmelCase_					:	List[Any]						=   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  A							(     self	:   Any			):
           lowerCAmelCase_					:	Dict						=   self.get_feature_extractor()
           lowerCAmelCase_					:	str						=   self.get_tokenizer()

           lowerCAmelCase_					:	Optional[Any]						=   SpeechaTextProcessor(tokenizer=UpperCAmelCase					,			feature_extractor=UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   """This is a test string"""

           lowerCAmelCase_					:	List[str]						=   processor(text=UpperCAmelCase			)

           lowerCAmelCase_					:	int						=   tokenizer(UpperCAmelCase			)

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



       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	str						=   self.get_feature_extractor()
           lowerCAmelCase_					:	str						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   SpeechaTextProcessor(tokenizer=UpperCAmelCase					,			feature_extractor=UpperCAmelCase			)

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

           lowerCAmelCase_					:	Any						=   processor.batch_decode(UpperCAmelCase			)
           lowerCAmelCase_					:	Dict						=   tokenizer.batch_decode(UpperCAmelCase			)

           self.assertListEqual(UpperCAmelCase					,			UpperCAmelCase			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	str						=   self.get_feature_extractor()
           lowerCAmelCase_					:	Dict						=   self.get_tokenizer()

           lowerCAmelCase_					:	Any						=   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 torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


