






'''simple docstring'''







import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class      __SCREAMING_SNAKE_CASE					(__A					):


		"""simple docstring"""


		_a     :   Dict			 =    (HeunDiscreteScheduler,)
		_a     :   List[str]			 =    10







		def       _a					( self      ,  **UpperCamelCase__  ):






									"""simple docstring"""


									a_	    = {
									    'num_train_timesteps': 1_100,
									    'beta_start': 0.0_001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									}

									config.update(**UpperCamelCase__  )
									return config







		def       _a					( self  ):






									"""simple docstring"""


									for timesteps in [10, 50, 100, 1_000]:
																self.check_over_configs(num_train_timesteps=UpperCamelCase__  )







		def       _a					( self  ):






									"""simple docstring"""


									for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,  [0.0_002, 0.002, 0.02]  ):
																self.check_over_configs(beta_start=UpperCamelCase__      ,  beta_end=UpperCamelCase__  )







		def       _a					( self  ):






									"""simple docstring"""


									for schedule in ["linear", "scaled_linear", "exp"]:
																self.check_over_configs(beta_schedule=UpperCamelCase__  )







		def       _a					( self  ):






									"""simple docstring"""


									for clip_sample_range in [1.0, 2.0, 3.0]:
																self.check_over_configs(clip_sample_range=UpperCamelCase__      ,  clip_sample=UpperCamelCase__  )







		def       _a					( self  ):






									"""simple docstring"""


									for prediction_type in ["epsilon", "v_prediction", "sample"]:
																self.check_over_configs(prediction_type=UpperCamelCase__  )







		def       _a					( self  ):






									"""simple docstring"""


									a_	    = self.scheduler_classes[0]
									a_	    = self.get_scheduler_config()
									a_	    = scheduler_class(**UpperCamelCase__  )

									scheduler.set_timesteps(self.num_inference_steps  )

									a_	    = self.dummy_model()
									a_	    = self.dummy_sample_deter * scheduler.init_noise_sigma
									a_	    = sample.to(UpperCamelCase__  )

									for i, t in enumerate(scheduler.timesteps  ):
																a_	    = scheduler.scale_model_input(UpperCamelCase__      ,  UpperCamelCase__  )

																a_	    = model(UpperCamelCase__      ,  UpperCamelCase__  )

																a_	    = scheduler.step(UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  )
																a_	    = output.prev_sample

									a_	    = torch.sum(torch.abs(UpperCamelCase__  )  )
									a_	    = torch.mean(torch.abs(UpperCamelCase__  )  )

									if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 0.1_233  ) < 1e-2
																assert abs(result_mean.item() - 0.0_002  ) < 1e-3
									else:
																# CUDA
																assert abs(result_sum.item() - 0.1_233  ) < 1e-2
																assert abs(result_mean.item() - 0.0_002  ) < 1e-3







		def       _a					( self  ):






									"""simple docstring"""


									a_	    = self.scheduler_classes[0]
									a_	    = self.get_scheduler_config(prediction_type='v_prediction'  )
									a_	    = scheduler_class(**UpperCamelCase__  )

									scheduler.set_timesteps(self.num_inference_steps  )

									a_	    = self.dummy_model()
									a_	    = self.dummy_sample_deter * scheduler.init_noise_sigma
									a_	    = sample.to(UpperCamelCase__  )

									for i, t in enumerate(scheduler.timesteps  ):
																a_	    = scheduler.scale_model_input(UpperCamelCase__      ,  UpperCamelCase__  )

																a_	    = model(UpperCamelCase__      ,  UpperCamelCase__  )

																a_	    = scheduler.step(UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  )
																a_	    = output.prev_sample

									a_	    = torch.sum(torch.abs(UpperCamelCase__  )  )
									a_	    = torch.mean(torch.abs(UpperCamelCase__  )  )

									if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 4.6934e-07  ) < 1e-2
																assert abs(result_mean.item() - 6.1112e-10  ) < 1e-3
									else:
																# CUDA
																assert abs(result_sum.item() - 4.693428650170972e-07  ) < 1e-2
																assert abs(result_mean.item() - 0.0_002  ) < 1e-3







		def       _a					( self  ):






									"""simple docstring"""


									a_	    = self.scheduler_classes[0]
									a_	    = self.get_scheduler_config()
									a_	    = scheduler_class(**UpperCamelCase__  )

									scheduler.set_timesteps(self.num_inference_steps      ,  device=UpperCamelCase__  )

									a_	    = self.dummy_model()
									a_	    = self.dummy_sample_deter.to(UpperCamelCase__  ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
																a_	    = scheduler.scale_model_input(UpperCamelCase__      ,  UpperCamelCase__  )

																a_	    = model(UpperCamelCase__      ,  UpperCamelCase__  )

																a_	    = scheduler.step(UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  )
																a_	    = output.prev_sample

									a_	    = torch.sum(torch.abs(UpperCamelCase__  )  )
									a_	    = torch.mean(torch.abs(UpperCamelCase__  )  )

									if str(UpperCamelCase__  ).startswith('cpu'  ):
																# The following sum varies between 148 and 156 on mps. Why?
																assert abs(result_sum.item() - 0.1_233  ) < 1e-2
																assert abs(result_mean.item() - 0.0_002  ) < 1e-3
									elif str(UpperCamelCase__  ).startswith('mps'  ):
																# Larger tolerance on mps
																assert abs(result_mean.item() - 0.0_002  ) < 1e-2
									else:
																# CUDA
																assert abs(result_sum.item() - 0.1_233  ) < 1e-2
																assert abs(result_mean.item() - 0.0_002  ) < 1e-3







		def       _a					( self  ):






									"""simple docstring"""


									a_	    = self.scheduler_classes[0]
									a_	    = self.get_scheduler_config()
									a_	    = scheduler_class(**UpperCamelCase__      ,  use_karras_sigmas=UpperCamelCase__  )

									scheduler.set_timesteps(self.num_inference_steps      ,  device=UpperCamelCase__  )

									a_	    = self.dummy_model()
									a_	    = self.dummy_sample_deter.to(UpperCamelCase__  ) * scheduler.init_noise_sigma
									a_	    = sample.to(UpperCamelCase__  )

									for t in scheduler.timesteps:
																a_	    = scheduler.scale_model_input(UpperCamelCase__      ,  UpperCamelCase__  )

																a_	    = model(UpperCamelCase__      ,  UpperCamelCase__  )

																a_	    = scheduler.step(UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  )
																a_	    = output.prev_sample

									a_	    = torch.sum(torch.abs(UpperCamelCase__  )  )
									a_	    = torch.mean(torch.abs(UpperCamelCase__  )  )

									assert abs(result_sum.item() - 0.00_015  ) < 1e-2
									assert abs(result_mean.item() - 1.9869554535034695e-07  ) < 1e-2












'''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      __SCREAMING_SNAKE_CASE					(unittest.TestCase					):


  """simple docstring"""


  def       _a					( self  ):






         """simple docstring"""


         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(UpperCamelCase__      ,  range(len(UpperCamelCase__  )  )  )  )

         a_	    = {
             'pad_token': '<pad>',
             'unk_token': '<unk>',
             'bos_token': '<s>',
             'eos_token': '</s>',
         }
         a_	    = {
             'feature_size': 1,
             'padding_value': 0.0,
             'sampling_rate': 16_000,
             '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      ,  UpperCamelCase__  )
         with open(self.vocab_file      ,  'w'      ,  encoding='utf-8'  ) as fp:
                fp.write(json.dumps(UpperCamelCase__  ) + '\n'  )

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







  def       _a					( self      ,  **UpperCamelCase__  ):






         """simple docstring"""


         a_	    = self.add_kwargs_tokens_map.copy()
         kwargs.update(UpperCamelCase__  )
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,  **UpperCamelCase__  )







  def       _a					( self      ,  **UpperCamelCase__  ):






         """simple docstring"""


         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,  **UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         shutil.rmtree(self.tmpdirname  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_tokenizer()
         a_	    = self.get_feature_extractor()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         processor.save_pretrained(self.tmpdirname  )
         a_	    = WavaVecaProcessor.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  ):






         """simple docstring"""


         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=UpperCamelCase__      ,  padding_value=1.0  )

         a_	    = WavaVecaProcessor.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  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         a_	    = floats_list((3, 1_000)  )

         a_	    = feature_extractor(UpperCamelCase__      ,  return_tensors='np'  )
         a_	    = 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  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         a_	    = 'This is a test string'

         a_	    = processor(text=UpperCamelCase__  )

         a_	    = tokenizer(UpperCamelCase__  )

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







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

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

         a_	    = processor.batch_decode(UpperCamelCase__  )
         a_	    = tokenizer.batch_decode(UpperCamelCase__  )

         self.assertListEqual(UpperCamelCase__      ,  UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(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'      ,  )




