




'''simple docstring'''






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= (DDIMScheduler,)
     A_       							= (("eta", 0.0), ("num_inference_steps", 5_0))





     def  _lowerCAmelCase			(    self    :				int    ,       **__lowerCamelCase    :				Optional[Any] ):
           snake_case__					:      Dict  			=						{
               'num_train_timesteps': 1000,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
               'clip_sample': True,
           }

           config.update(**__lowerCamelCase )
           return config





     def  _lowerCAmelCase			(    self    :				Tuple    ,       **__lowerCamelCase    :				Any ):
           snake_case__					:      str  			=						self.scheduler_classes[0]
           snake_case__					:      str  			=						self.get_scheduler_config(**__lowerCamelCase )
           snake_case__					:      Optional[int]  			=						scheduler_class(**__lowerCamelCase )

           snake_case__			,       snake_case__					:      List[Any]  			=						10, 0.0

           snake_case__					:      Optional[int]  			=						self.dummy_model()
           snake_case__					:      str  			=						self.dummy_sample_deter

           scheduler.set_timesteps(__lowerCamelCase )

           for t in scheduler.timesteps:
                 snake_case__					:      str  			=						model(__lowerCamelCase    ,       __lowerCamelCase )
                 snake_case__					:      Tuple  			=						scheduler.step(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase ).prev_sample

           return sample





     def  _lowerCAmelCase			(    self    :				int ):
           for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[str] ):
           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowerCamelCase )

           snake_case__					:      str  			=						self.scheduler_classes[0]
           snake_case__					:      Tuple  			=						self.get_scheduler_config(steps_offset=1 )
           snake_case__					:      str  			=						scheduler_class(**__lowerCamelCase )
           scheduler.set_timesteps(5 )
           assert torch.equal(scheduler.timesteps    ,       torch.LongTensor([801, 601, 401, 201, 1] ) )





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,       [0.0_0_2, 0.0_2, 0.2, 2] ):
                 self.check_over_configs(beta_start=__lowerCamelCase    ,       beta_end=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[str] ):
           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Tuple ):
           for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				int ):
           for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Dict ):
           self.check_over_configs(thresholding=__lowerCamelCase )
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=__lowerCamelCase    ,       prediction_type=__lowerCamelCase    ,       sample_max_value=__lowerCamelCase    ,       )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Tuple ):
           for t, num_inference_steps in zip([1, 10, 50]    ,       [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowerCamelCase    ,       num_inference_steps=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Any ):
           for t, eta in zip([1, 10, 49]    ,       [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowerCamelCase    ,       eta=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      Dict  			=						self.scheduler_classes[0]
           snake_case__					:      Optional[Any]  			=						self.get_scheduler_config()
           snake_case__					:      List[str]  			=						scheduler_class(**__lowerCamelCase )

           assert torch.sum(torch.abs(scheduler._get_variance(0    ,       0 ) - 0.0 ) ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(420    ,       400 ) - 0.1_4_7_7_1 ) ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(980    ,       960 ) - 0.3_2_4_6_0 ) ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(0    ,       0 ) - 0.0 ) ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487    ,       486 ) - 0.0_0_9_7_9 ) ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999    ,       998 ) - 0.0_2 ) ) < 1E-5





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__					:      Dict  			=						self.full_loop()

           snake_case__					:      List[Any]  			=						torch.sum(torch.abs(__lowerCamelCase ) )
           snake_case__					:      int  			=						torch.mean(torch.abs(__lowerCamelCase ) )

           assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
           assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__					:      Dict  			=						self.full_loop(prediction_type='v_prediction' )

           snake_case__					:      Optional[int]  			=						torch.sum(torch.abs(__lowerCamelCase ) )
           snake_case__					:      Union[str, Any]  			=						torch.mean(torch.abs(__lowerCamelCase ) )

           assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
           assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3





     def  _lowerCAmelCase			(    self    :				Any ):
           # We specify different beta, so that the first alpha is 0.99
           snake_case__					:      int  			=						self.full_loop(set_alpha_to_one=__lowerCamelCase    ,       beta_start=0.0_1 )
           snake_case__					:      Optional[Any]  			=						torch.sum(torch.abs(__lowerCamelCase ) )
           snake_case__					:      str  			=						torch.mean(torch.abs(__lowerCamelCase ) )

           assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
           assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3





     def  _lowerCAmelCase			(    self    :				Any ):
           # We specify different beta, so that the first alpha is 0.99
           snake_case__					:      Any  			=						self.full_loop(set_alpha_to_one=__lowerCamelCase    ,       beta_start=0.0_1 )
           snake_case__					:      Dict  			=						torch.sum(torch.abs(__lowerCamelCase ) )
           snake_case__					:      Union[str, Any]  			=						torch.mean(torch.abs(__lowerCamelCase ) )

           assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
           assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3






'''simple docstring'''






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


A_ 			=		get_tests_dir("fixtures/test_sentencepiece.model")






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





     def  _lowerCAmelCase			(    self    :				List[str] ):
           snake_case__					:      Dict  			=						tempfile.mkdtemp()

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

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

           snake_case__					:      str  			=						{
               'feature_size': 24,
               'num_mel_bins': 24,
               'padding_value': 0.0,
               'sampling_rate': 16000,
               'return_attention_mask': False,
               'do_normalize': True,
           }
           save_json(__lowerCamelCase    ,       save_dir / FEATURE_EXTRACTOR_NAME )





     def  _lowerCAmelCase			(    self    :				str    ,       **__lowerCamelCase    :				int ):
           return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				int    ,       **__lowerCamelCase    :				List[Any] ):
           return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           shutil.rmtree(self.tmpdirname )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Dict  			=						self.get_tokenizer()
           snake_case__					:      Any  			=						self.get_feature_extractor()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           processor.save_pretrained(self.tmpdirname )
           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(self.tmpdirname )

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

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





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

           snake_case__					:      Tuple  			=						self.get_tokenizer(bos_token='(BOS)'    ,       eos_token='(EOS)' )
           snake_case__					:      int  			=						self.get_feature_extractor(do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(
               self.tmpdirname    ,       bos_token='(BOS)'    ,       eos_token='(EOS)'    ,       do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

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

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





     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      int  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      List[str]  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      List[str]  			=						floats_list((3, 1000) )

           snake_case__					:      Tuple  			=						feature_extractor(__lowerCamelCase    ,       return_tensors='np' )
           snake_case__					:      str  			=						processor(__lowerCamelCase    ,       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    :				Dict ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						'This is a test string'

           snake_case__					:      int  			=						processor(text=__lowerCamelCase )

           snake_case__					:      Tuple  			=						tokenizer(__lowerCamelCase )

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





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      str  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

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

           snake_case__					:      List[str]  			=						processor.batch_decode(__lowerCamelCase )
           snake_case__					:      Optional[int]  			=						tokenizer.batch_decode(__lowerCamelCase )

           self.assertListEqual(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__					:      List[str]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      int  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

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