






'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list






class        a__     (      unittest.TestCase    ):
     '''simple docstring'''







     def      _UpperCamelCase (self     :						Any       )	->							Optional[Any]:
           __A		 =   "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "       )
           __A		 =   dict(zip(lowercase__					,					range(len(lowercase__       )       )       )       )

           __A		 =   {
               "pad_token": "<pad>",
               "unk_token": "<unk>",
               "bos_token": "<s>",
               "eos_token": "</s>",
           }
           __A		 =   {
               "feature_size": 1,
               "padding_value": 0.0,
               "sampling_rate": 16000,
               "return_attention_mask": False,
               "do_normalize": True,
           }

           __A		 =   tempfile.mkdtemp()
           __A		 =   os.path.join(self.tmpdirname					,					VOCAB_FILES_NAMES["vocab_file"]       )
           __A		 =   os.path.join(self.tmpdirname					,					lowercase__       )
           with open(self.vocab_file					,					"w"					,					encoding="utf-8"       ) as fp:
                 fp.write(json.dumps(lowercase__       ) + "\n"       )

           with open(self.feature_extraction_file					,					"w"					,					encoding="utf-8"       ) as fp:
                 fp.write(json.dumps(lowercase__       ) + "\n"       )






     def      _UpperCamelCase (self     :						List[str]					,					**lowercase__     :						List[str]       )	->							int:
           __A		 =   self.add_kwargs_tokens_map.copy()
           kwargs.update(lowercase__       )
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,					**lowercase__       )






     def      _UpperCamelCase (self     :						Dict					,					**lowercase__     :						Tuple       )	->							str:
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,					**lowercase__       )






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






     def      _UpperCamelCase (self     :						Tuple       )	->							List[str]:
           __A		 =   self.get_tokenizer()
           __A		 =   self.get_feature_extractor()

           __A		 =   WavaVecaProcessor(tokenizer=lowercase__					,					feature_extractor=lowercase__       )

           processor.save_pretrained(self.tmpdirname       )
           __A		 =   WavaVecaProcessor.from_pretrained(self.tmpdirname       )

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

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






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							str:
           __A		 =   WavaVecaProcessor(tokenizer=self.get_tokenizer()					,					feature_extractor=self.get_feature_extractor()       )
           processor.save_pretrained(self.tmpdirname       )

           __A		 =   self.get_tokenizer(bos_token="(BOS)"					,					eos_token="(EOS)"       )
           __A		 =   self.get_feature_extractor(do_normalize=lowercase__					,					padding_value=1.0       )

           __A		 =   WavaVecaProcessor.from_pretrained(
               self.tmpdirname					,					bos_token="(BOS)"					,					eos_token="(EOS)"					,					do_normalize=lowercase__					,					padding_value=1.0       )

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

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






     def      _UpperCamelCase (self     :						int       )	->							Dict:
           __A		 =   self.get_feature_extractor()
           __A		 =   self.get_tokenizer()

           __A		 =   WavaVecaProcessor(tokenizer=lowercase__					,					feature_extractor=lowercase__       )

           __A		 =   floats_list((3, 1000)       )

           __A		 =   feature_extractor(lowercase__					,					return_tensors="np"       )
           __A		 =   processor(lowercase__					,					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      _UpperCamelCase (self     :						Optional[int]       )	->							Any:
           __A		 =   self.get_feature_extractor()
           __A		 =   self.get_tokenizer()

           __A		 =   WavaVecaProcessor(tokenizer=lowercase__					,					feature_extractor=lowercase__       )

           __A		 =   "This is a test string"

           __A		 =   processor(text=lowercase__       )

           __A		 =   tokenizer(lowercase__       )

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






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							int:
           __A		 =   self.get_feature_extractor()
           __A		 =   self.get_tokenizer()

           __A		 =   WavaVecaProcessor(tokenizer=lowercase__					,					feature_extractor=lowercase__       )

           __A		 =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __A		 =   processor.batch_decode(lowercase__       )
           __A		 =   tokenizer.batch_decode(lowercase__       )

           self.assertListEqual(lowercase__					,					lowercase__       )






     def      _UpperCamelCase (self     :						Any       )	->							int:
           __A		 =   self.get_feature_extractor()
           __A		 =   self.get_tokenizer()

           __A		 =   WavaVecaProcessor(tokenizer=lowercase__					,					feature_extractor=lowercase__       )

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










'''simple docstring'''







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	(KDPMaAncestralDiscreteScheduler,)
     _snake_case							   =	10






     def      _UpperCamelCase (self     :						int					,					**lowercase__     :						Optional[int]       )	->							Dict:
           __A		 =   {
               "num_train_timesteps": 1100,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**lowercase__       )
           return config






     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,					[0.00_02, 0.0_02, 0.02]       ):
                 self.check_over_configs(beta_start=lowercase__					,					beta_end=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Tuple:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Any:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_77       ) < 1e-2
           assert abs(result_mean.item() - 18.03_31       ) < 5e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							str:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							List[Any]:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config(prediction_type="v_prediction"       )
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           __A		 =   torch.manual_seed(0       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 3_28.99_70       ) < 1e-2
           assert abs(result_mean.item() - 0.42_84       ) < 1e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							int:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps					,					device=lowercase__       )
           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter.to(lowercase__       ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_18       ) < 1e-1
           assert abs(result_mean.item() - 18.03_31       ) < 1e-3


