



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


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







				lowercase__				  =  (ScoreSdeVeScheduler,)
				lowercase__				  =  ()

				@property
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				4
									UpperCamelCase_											=				3
									UpperCamelCase_											=				8
									UpperCamelCase_											=				8

									UpperCamelCase_											=				torch.rand((batch_size, num_channels, height, width)      )

									return sample

				@property
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				4
									UpperCamelCase_											=				3
									UpperCamelCase_											=				8
									UpperCamelCase_											=				8

									UpperCamelCase_											=				batch_size * num_channels * height * width
									UpperCamelCase_											=				torch.arange(snake_case__      )
									UpperCamelCase_											=				sample.reshape(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      )
									UpperCamelCase_											=				sample / num_elems
									UpperCamelCase_											=				sample.permute(3       ,	0       ,	1       ,	2      )

									return sample

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									def model(snake_case__       ,	snake_case__       ,	*snake_case__      ):
														return sample * t / (t + 1)

									return model

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				{
									    "num_train_timesteps": 2000,
									    "snr": 0.15,
									    "sigma_min": 0.01,
									    "sigma_max": 1348,
									    "sampling_eps": 1e-5,
									}

									config.update(**snake_case__      )
									return config

				def  _lowerCamelCase   (	self       ,	snake_case__=0       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				dict(self.forward_default_kwargs      )

									for scheduler_class in self.scheduler_classes:
														UpperCamelCase_											=				self.dummy_sample
														UpperCamelCase_											=				0.1 * sample

														UpperCamelCase_											=				self.get_scheduler_config(**snake_case__      )
														UpperCamelCase_											=				scheduler_class(**snake_case__      )

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case__      )
																			UpperCamelCase_											=				scheduler_class.from_pretrained(snake_case__      )

														UpperCamelCase_											=				scheduler.step_pred(
														    snake_case__       ,	snake_case__       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample
														UpperCamelCase_											=				new_scheduler.step_pred(
														    snake_case__       ,	snake_case__       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample

														assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

														UpperCamelCase_											=				scheduler.step_correct(snake_case__       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample
														UpperCamelCase_											=				new_scheduler.step_correct(
														    snake_case__       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample

														assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler correction are not identical"

				def  _lowerCamelCase   (	self       ,	snake_case__=0       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				dict(self.forward_default_kwargs      )
									kwargs.update(snake_case__      )

									for scheduler_class in self.scheduler_classes:
														UpperCamelCase_											=				self.dummy_sample
														UpperCamelCase_											=				0.1 * sample

														UpperCamelCase_											=				self.get_scheduler_config()
														UpperCamelCase_											=				scheduler_class(**snake_case__      )

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case__      )
																			UpperCamelCase_											=				scheduler_class.from_pretrained(snake_case__      )

														UpperCamelCase_											=				scheduler.step_pred(
														    snake_case__       ,	snake_case__       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample
														UpperCamelCase_											=				new_scheduler.step_pred(
														    snake_case__       ,	snake_case__       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample

														assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

														UpperCamelCase_											=				scheduler.step_correct(snake_case__       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample
														UpperCamelCase_											=				new_scheduler.step_correct(
														    snake_case__       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample

														assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler correction are not identical"

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for timesteps in [10, 100, 1000]:
														self.check_over_configs(num_train_timesteps=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,	[1, 100, 1000]      ):
														self.check_over_configs(sigma_min=snake_case__       ,	sigma_max=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for t in [0.1, 0.5, 0.75]:
														self.check_over_forward(time_step=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				dict(self.forward_default_kwargs      )

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

									UpperCamelCase_											=				3

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

									scheduler.set_sigmas(snake_case__      )
									scheduler.set_timesteps(snake_case__      )
									UpperCamelCase_											=				torch.manual_seed(0      )

									for i, t in enumerate(scheduler.timesteps      ):
														UpperCamelCase_											=				scheduler.sigmas[i]

														for _ in range(scheduler.config.correct_steps      ):
																			with torch.no_grad():
																								UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )
																			UpperCamelCase_											=				scheduler.step_correct(snake_case__       ,	snake_case__       ,	generator=snake_case__       ,	**snake_case__      ).prev_sample

														with torch.no_grad():
																			UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				scheduler.step_pred(snake_case__       ,	snake_case__       ,	snake_case__       ,	generator=snake_case__       ,	**snake_case__      )
														UpperCamelCase_					,      UpperCamelCase_											=				output.prev_sample, output.prev_sample_mean

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

									assert np.isclose(result_sum.item()       ,	14_372_758_528.0      )
									assert np.isclose(result_mean.item()       ,	18_714_530.0      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				dict(self.forward_default_kwargs      )

									UpperCamelCase_											=				kwargs.pop("num_inference_steps"       ,	snake_case__      )

									for scheduler_class in self.scheduler_classes:
														UpperCamelCase_											=				self.get_scheduler_config()
														UpperCamelCase_											=				scheduler_class(**snake_case__      )

														UpperCamelCase_											=				self.dummy_sample
														UpperCamelCase_											=				0.1 * sample

														if num_inference_steps is not None and hasattr(snake_case__       ,	"set_timesteps"      ):
																			scheduler.set_timesteps(snake_case__      )
														elif num_inference_steps is not None and not hasattr(snake_case__       ,	"set_timesteps"      ):
																			UpperCamelCase_											=				num_inference_steps

														UpperCamelCase_											=				scheduler.step_pred(snake_case__       ,	0       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample
														UpperCamelCase_											=				scheduler.step_pred(snake_case__       ,	1       ,	snake_case__       ,	generator=torch.manual_seed(0      )       ,	**snake_case__      ).prev_sample

														self.assertEqual(output_a.shape       ,	sample.shape      )
														self.assertEqual(output_a.shape       ,	output_a.shape      )





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"       ,	)
