
"""simple docstring"""




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		Dict         =		(DDIMScheduler,)
	snake_case__							:		Tuple         =		(('eta', 0.0), ('num_inference_steps', 50))


	def    _UpperCamelCase							(  self :List[str]       ,    **__magic_name__ :Tuple			)			->   Dict:

			'''simple docstring'''






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

			config.update(**__magic_name__			)
			return config


	def    _UpperCamelCase							(  self :int       ,    **__magic_name__ :int			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.scheduler_classes[0]
			a__   		=     self.get_scheduler_config(**__magic_name__			)
			a__   		=     scheduler_class(**__magic_name__			)

			a__		,	a__   		=     10, 0.0

			a__   		=     self.dummy_model()
			a__   		=     self.dummy_sample_deter

			scheduler.set_timesteps(__magic_name__			)

			for t in scheduler.timesteps:
					a__   		=     model(__magic_name__       ,    __magic_name__			)
					a__   		=     scheduler.step(__magic_name__       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__			).prev_sample

			return sample


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

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   str:

			'''simple docstring'''






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

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


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

			'''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=__magic_name__       ,    beta_end=__magic_name__			)


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

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   str:

			'''simple docstring'''






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


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

			'''simple docstring'''






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


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

			'''simple docstring'''






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


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

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :List[str]			)			->   int:

			'''simple docstring'''






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


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

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Tuple			)			->   Optional[Any]:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Dict			)			->   Any:

			'''simple docstring'''






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


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

			'''simple docstring'''






			a__   		=     self.scheduler_classes[0]
			a__   		=     self.get_scheduler_config()
			a__   		=     scheduler_class(**__magic_name__			)

			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    _UpperCamelCase							(  self :List[Any]			)			->   Optional[Any]:

			'''simple docstring'''






			a__   		=     self.full_loop()

			a__   		=     torch.sum(torch.abs(__magic_name__			)			)
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

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


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

			'''simple docstring'''






			a__   		=     self.full_loop(prediction_type='''v_prediction'''			)

			a__   		=     torch.sum(torch.abs(__magic_name__			)			)
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

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


	def    _UpperCamelCase							(  self :Dict			)			->   str:

			'''simple docstring'''






			a__   		=     self.full_loop(set_alpha_to_one=__magic_name__       ,    beta_start=0.01			)
			a__   		=     torch.sum(torch.abs(__magic_name__			)			)
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

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






	def    _UpperCamelCase							(  self :Any			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.full_loop(set_alpha_to_one=__magic_name__       ,    beta_start=0.01			)
			a__   		=     torch.sum(torch.abs(__magic_name__			)			)
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

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






"""simple docstring"""




import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
					pass

if is_speech_available():
					from transformers import EncodecFeatureExtractor, MusicgenProcessor


__lowerCAmelCase							:  Union[str, Any] 				=		random.Random()

def __snake_case (      UpperCamelCase						,					UpperCamelCase=1.0						,					UpperCamelCase=None						,					UpperCamelCase=None    )       ->    Tuple:


		"""simple docstring"""





		if rng is None:
				a__   		=     global_rng

		a__   		=     []
		for batch_idx in range(shape[0]    ):
				values.append([]    )
				for _ in range(shape[1]    ):
						values[-1].append(rng.random() * scale    )

		return values




@require_torch
@require_sentencepiece
class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







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

			'''simple docstring'''






			a__   		=     '''facebook/musicgen-small'''
			a__   		=     tempfile.mkdtemp()


	def    _UpperCamelCase							(  self :Union[str, Any]       ,    **__magic_name__ :Union[str, Any]			)			->   str:

			'''simple docstring'''






			return TaTokenizer.from_pretrained(self.checkpoint       ,    **__magic_name__			)


	def    _UpperCamelCase							(  self :Optional[int]       ,    **__magic_name__ :Any			)			->   Optional[int]:

			'''simple docstring'''






			return EncodecFeatureExtractor.from_pretrained(self.checkpoint       ,    **__magic_name__			)


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

			'''simple docstring'''






			shutil.rmtree(self.tmpdirname			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			a__   		=     self.get_tokenizer()
			a__   		=     self.get_feature_extractor()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			processor.save_pretrained(self.tmpdirname			)
			a__   		=     MusicgenProcessor.from_pretrained(self.tmpdirname			)

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

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


	def    _UpperCamelCase							(  self :Dict			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     MusicgenProcessor(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=__magic_name__       ,    padding_value=1.0			)

			a__   		=     MusicgenProcessor.from_pretrained(
			    self.tmpdirname       ,    bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''       ,    do_normalize=__magic_name__       ,    padding_value=1.0			)

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

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


	def    _UpperCamelCase							(  self :Any			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     floats_list((3, 1000)			)

			a__   		=     feature_extractor(__magic_name__       ,    return_tensors='''np'''			)
			a__   		=     processor(__magic_name__       ,    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 :Tuple			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     '''This is a test string'''

			a__   		=     processor(text=__magic_name__			)

			a__   		=     tokenizer(__magic_name__			)

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


	def    _UpperCamelCase							(  self :Tuple			)			->   Optional[Any]:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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

			a__   		=     processor.batch_decode(sequences=__magic_name__			)
			a__   		=     tokenizer.batch_decode(__magic_name__			)

			self.assertListEqual(__magic_name__       ,    __magic_name__			)


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

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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






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

			'''simple docstring'''






			a__   		=     self.get_feature_extractor(padding_side='''left'''			)
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     [floats_list((1, x)			)[0] for x in range(5       ,    20       ,    5			)]
			a__   		=     processor(__magic_name__			).padding_mask

			a__   		=     np.asarray(floats_list((3, 20)			)			)[:, None, :]
			a__   		=     processor.batch_decode(__magic_name__       ,    padding_mask=__magic_name__			)

			self.assertIsInstance(__magic_name__       ,    __magic_name__			)

			for audio in decoded_audios:
					self.assertIsInstance(__magic_name__       ,    np.ndarray			)

			self.assertTrue(decoded_audios[0].shape == (1, 10)			)
			self.assertTrue(decoded_audios[1].shape == (1, 15)			)
			self.assertTrue(decoded_audios[2].shape == (1, 20)			)




