



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class  _lowercase				(a_ ):
    '''simple docstring'''







    lowercase__				  =  (DDIMScheduler,)
    lowercase__				  =  (("""eta""", 0.0), ("""num_inference_steps""", 50))

    def  _lowerCamelCase   (	self       ,	**snake_case__      ):

         '''simple docstring'''


         UpperCamelCase_											=				{
             "num_train_timesteps": 1000,
             "beta_start": 0.0_001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**snake_case__      )
         return config

    def  _lowerCamelCase   (	self       ,	**snake_case__      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.scheduler_classes[0]
         UpperCamelCase_											=				self.get_scheduler_config(**snake_case__      )
         UpperCamelCase_											=				scheduler_class(**snake_case__      )

         UpperCamelCase_					,      UpperCamelCase_											=				10, 0.0

         UpperCamelCase_											=				self.dummy_model()
         UpperCamelCase_											=				self.dummy_sample_deter

         scheduler.set_timesteps(snake_case__      )

         for t in scheduler.timesteps:
              UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )
              UpperCamelCase_											=				scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      ).prev_sample

         return sample

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         for timesteps in [100, 500, 1000]:
              self.check_over_configs(num_train_timesteps=snake_case__      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=snake_case__      )

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

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]       ,	[0.002, 0.02, 0.2, 2]      ):
              self.check_over_configs(beta_start=snake_case__       ,	beta_end=snake_case__      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=snake_case__      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


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

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=snake_case__      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=snake_case__      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=snake_case__      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


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

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         for t in [1, 10, 49]:
              self.check_over_forward(time_step=snake_case__      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


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

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


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

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.scheduler_classes[0]
         UpperCamelCase_											=				self.get_scheduler_config()
         UpperCamelCase_											=				scheduler_class(**snake_case__      )

         assert torch.sum(torch.abs(scheduler._get_variance(0       ,	0      ) - 0.0      )      ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(420       ,	400      ) - 0.14_771      )      ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980       ,	960      ) - 0.32_460      )      ) < 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      ) - 0.00_979      )      ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999       ,	998      ) - 0.02      )      ) < 1e-5

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.full_loop()

         UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
         UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

         assert abs(result_sum.item() - 172.0_067      ) < 1e-2
         assert abs(result_mean.item() - 0.223_967      ) < 1e-3

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.full_loop(prediction_type="v_prediction"      )

         UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
         UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

         assert abs(result_sum.item() - 52.5_302      ) < 1e-2
         assert abs(result_mean.item() - 0.0_684      ) < 1e-3

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.full_loop(set_alpha_to_one=snake_case__       ,	beta_start=0.01      )
         UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
         UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

         assert abs(result_sum.item() - 149.8_295      ) < 1e-2
         assert abs(result_mean.item() - 0.1_951      ) < 1e-3

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.full_loop(set_alpha_to_one=snake_case__       ,	beta_start=0.01      )
         UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
         UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

         assert abs(result_sum.item() - 149.0_784      ) < 1e-2
         assert abs(result_mean.item() - 0.1_941      ) < 1e-3





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  _lowercase				(unittest.TestCase ):
				'''simple docstring'''







				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				"<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(" "      )
									UpperCamelCase_											=				dict(zip(snake_case__       ,	range(len(snake_case__      )      )      )      )

									UpperCamelCase_											=				{
									    "pad_token": "<pad>",
									    "unk_token": "<unk>",
									    "bos_token": "<s>",
									    "eos_token": "</s>",
									}
									UpperCamelCase_											=				{
									    "feature_size": 1,
									    "padding_value": 0.0,
									    "sampling_rate": 1_6000,
									    "return_attention_mask": False,
									    "do_normalize": True,
									}

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

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

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.add_kwargs_tokens_map.copy()
									kwargs.update(snake_case__      )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,	**snake_case__      )

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									shutil.rmtree(self.tmpdirname      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_tokenizer()
									UpperCamelCase_											=				self.get_feature_extractor()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

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

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

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,	feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

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

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

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

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

									UpperCamelCase_											=				floats_list((3, 1000)      )

									UpperCamelCase_											=				feature_extractor(snake_case__       ,	return_tensors="np"      )
									UpperCamelCase_											=				processor(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      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

									UpperCamelCase_											=				"This is a test string"

									UpperCamelCase_											=				processor(text=snake_case__      )

									UpperCamelCase_											=				tokenizer(snake_case__      )

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

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

									UpperCamelCase_											=				processor.batch_decode(snake_case__      )
									UpperCamelCase_											=				tokenizer.batch_decode(snake_case__      )

									self.assertListEqual(snake_case__       ,	snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_feature_extractor()
									UpperCamelCase_											=				self.get_tokenizer()

									UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

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