





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''
	__UpperCamelCase        =		(ScoreSdeVeScheduler,)
	__UpperCamelCase        =		()




	@property
	def       UpperCAmelCase (	self       :    str      )					->   Union[str, Any]:

						'''simple docstring'''




						__lowercase					=    4
						__lowercase					=    3
						__lowercase					=    8
						__lowercase					=    8

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

						return sample




	@property
	def       UpperCAmelCase (	self       :    str      )					->   Any:

						'''simple docstring'''




						__lowercase					=    4
						__lowercase					=    3
						__lowercase					=    8
						__lowercase					=    8

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

						return sample




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

						'''simple docstring'''




						def model(__lowerCamelCase       :    Any				,			__lowerCamelCase       :    Optional[int]				,			*__lowerCamelCase       :    Union[str, Any]      ):
											return sample * t / (t + 1)

						return model




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			**__lowerCamelCase       :    int      )					->   Tuple:

						'''simple docstring'''




						__lowercase					=    {
						    """num_train_timesteps""": 2_000,
						    """snr""": 0.15,
						    """sigma_min""": 0.01,
						    """sigma_max""": 1_348,
						    """sampling_eps""": 1E-5,
						}

						config.update(**_lowerCamelCase      )
						return config




	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    int=0				,			**__lowerCamelCase       :    Dict      )					->   Optional[Any]:

						'''simple docstring'''




						__lowercase					=    dict(self.forward_default_kwargs      )

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

											__lowercase					=    self.get_scheduler_config(**_lowerCamelCase      )
											__lowercase					=    scheduler_class(**_lowerCamelCase      )

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_lowerCamelCase      )
																__lowercase					=    scheduler_class.from_pretrained(_lowerCamelCase      )

											__lowercase					=    scheduler.step_pred(
											    _lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample
											__lowercase					=    new_scheduler.step_pred(
											    _lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample

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

											__lowercase					=    scheduler.step_correct(_lowerCamelCase				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample
											__lowercase					=    new_scheduler.step_correct(
											    _lowerCamelCase				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample

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




	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    Optional[int]=0				,			**__lowerCamelCase       :    Dict      )					->   int:

						'''simple docstring'''




						__lowercase					=    dict(self.forward_default_kwargs      )
						kwargs.update(_lowerCamelCase      )

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

											__lowercase					=    self.get_scheduler_config()
											__lowercase					=    scheduler_class(**_lowerCamelCase      )

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_lowerCamelCase      )
																__lowercase					=    scheduler_class.from_pretrained(_lowerCamelCase      )

											__lowercase					=    scheduler.step_pred(
											    _lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample
											__lowercase					=    new_scheduler.step_pred(
											    _lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample

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

											__lowercase					=    scheduler.step_correct(_lowerCamelCase				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample
											__lowercase					=    new_scheduler.step_correct(
											    _lowerCamelCase				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample

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




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   int:

						'''simple docstring'''




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




	def       UpperCAmelCase (	self       :    Any      )					->   Optional[Any]:

						'''simple docstring'''




						for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]				,			[1, 100, 1_000]      ):
											self.check_over_configs(sigma_min=_lowerCamelCase				,			sigma_max=_lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

						'''simple docstring'''




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




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

						'''simple docstring'''




						__lowercase					=    dict(self.forward_default_kwargs      )

						__lowercase					=    self.scheduler_classes[0]
						__lowercase					=    self.get_scheduler_config()
						__lowercase					=    scheduler_class(**_lowerCamelCase      )

						__lowercase					=    3

						__lowercase					=    self.dummy_model()
						__lowercase					=    self.dummy_sample_deter

						scheduler.set_sigmas(_lowerCamelCase      )
						scheduler.set_timesteps(_lowerCamelCase      )
						__lowercase					=    torch.manual_seed(0      )

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

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

											with torch.no_grad():
																__lowercase					=    model(_lowerCamelCase				,			_lowerCamelCase      )

											__lowercase					=    scheduler.step_pred(_lowerCamelCase				,			_lowerCamelCase				,			_lowerCamelCase				,			generator=_lowerCamelCase				,			**_lowerCamelCase      )
											__lowercase					=    output.prev_sample, output.prev_sample_mean

						__lowercase					=    torch.sum(torch.abs(_lowerCamelCase      )      )
						__lowercase					=    torch.mean(torch.abs(_lowerCamelCase      )      )

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






	def       UpperCAmelCase (	self       :    Any      )					->   Dict:

						'''simple docstring'''




						__lowercase					=    dict(self.forward_default_kwargs      )

						__lowercase					=    kwargs.pop('num_inference_steps'				,			_lowerCamelCase      )

						for scheduler_class in self.scheduler_classes:
											__lowercase					=    self.get_scheduler_config()
											__lowercase					=    scheduler_class(**_lowerCamelCase      )

											__lowercase					=    self.dummy_sample
											__lowercase					=    0.1 * sample

											if num_inference_steps is not None and hasattr(_lowerCamelCase				,			'set_timesteps'      ):
																scheduler.set_timesteps(_lowerCamelCase      )
											elif num_inference_steps is not None and not hasattr(_lowerCamelCase				,			'set_timesteps'      ):
																__lowercase					=    num_inference_steps

											__lowercase					=    scheduler.step_pred(_lowerCamelCase				,			0				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample
											__lowercase					=    scheduler.step_pred(_lowerCamelCase				,			1				,			_lowerCamelCase				,			generator=torch.manual_seed(0      )				,			**_lowerCamelCase      ).prev_sample

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











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


SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      get_tests_dir('''fixtures/test_sentencepiece.model''')

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







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    List[Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    tempfile.mkdtemp()

			__lowercase					=    ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
			__lowercase					=    dict(zip(__lowerCamelCase				,			range(len(__lowerCamelCase      )      )      )      )
			__lowercase					=    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']      )

			__lowercase					=    SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
			tokenizer.save_pretrained(self.tmpdirname      )

			__lowercase					=    {
			    'feature_size': 24,
			    'num_mel_bins': 24,
			    'padding_value': 0.0,
			    'sampling_rate': 16_000,
			    'return_attention_mask': False,
			    'do_normalize': True,
			}
			save_json(__lowerCamelCase				,			save_dir / FEATURE_EXTRACTOR_NAME      )




	def       UpperCAmelCase (	self       :    Dict				,			**__lowerCamelCase       :    Dict      )					->   Union[str, Any]:

			'''simple docstring'''




			return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    str				,			**__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			shutil.rmtree(self.tmpdirname      )




	def       UpperCAmelCase (	self       :    str      )					->   Any:

			'''simple docstring'''




			__lowercase					=    self.get_tokenizer()
			__lowercase					=    self.get_feature_extractor()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			processor.save_pretrained(self.tmpdirname      )
			__lowercase					=    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       UpperCAmelCase (	self       :    List[str]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    SpeechaTextProcessor(
			    tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()      )
			processor.save_pretrained(self.tmpdirname      )

			__lowercase					=    self.get_tokenizer(bos_token='(BOS)'				,			eos_token='(EOS)'      )
			__lowercase					=    self.get_feature_extractor(do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			__lowercase					=    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       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    floats_list((3, 1_000)      )

			__lowercase					=    feature_extractor(__lowerCamelCase				,			return_tensors='np'      )
			__lowercase					=    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       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    'This is a test string'

			__lowercase					=    processor(text=__lowerCamelCase      )

			__lowercase					=    tokenizer(__lowerCamelCase      )

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




	def       UpperCAmelCase (	self       :    str      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

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

			__lowercase					=    processor.batch_decode(__lowerCamelCase      )
			__lowercase					=    tokenizer.batch_decode(__lowerCamelCase      )

			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    Tuple      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    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'				,			)




