


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: List[Any]       =					(EulerAncestralDiscreteScheduler,)
							_lowerCamelCase				: int       =					10





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   {
								    """num_train_timesteps""": 1100,
								    """beta_start""": 0.0_0_0_1,
								    """beta_end""": 0.0_2,
								    """beta_schedule""": """linear""",
								}

								config.update(**_SCREAMING_SNAKE_CASE    )
								return config





							def   __magic_name__      (							self    ):
								for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,       [0.0_0_0_2, 0.0_0_2, 0.0_2]    ):
									self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE			,       beta_end=_SCREAMING_SNAKE_CASE    )





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





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





							def   __magic_name__      (							self    ):
								a_     			=   self.scheduler_classes[0]
								a_     			=   self.get_scheduler_config()
								a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								scheduler.set_timesteps(self.num_inference_steps    )

								a_     			=   torch.manual_seed(0    )

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

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

									a_     			=   model(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       generator=_SCREAMING_SNAKE_CASE    )
									a_     			=   output.prev_sample

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

								assert abs(result_sum.item() - 1_5_2.3_1_9_2    ) < 1E-2
								assert abs(result_mean.item() - 0.1_9_8_3    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.scheduler_classes[0]
								a_     			=   self.get_scheduler_config(prediction_type="""v_prediction"""    )
								a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								scheduler.set_timesteps(self.num_inference_steps    )

								a_     			=   torch.manual_seed(0    )

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

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

									a_     			=   model(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       generator=_SCREAMING_SNAKE_CASE    )
									a_     			=   output.prev_sample

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

								assert abs(result_sum.item() - 1_0_8.4_4_3_9    ) < 1E-2
								assert abs(result_mean.item() - 0.1_4_1_2    ) < 1E-3





							def   __magic_name__      (							self    ):
								a_     			=   self.scheduler_classes[0]
								a_     			=   self.get_scheduler_config()
								a_     			=   scheduler_class(**_SCREAMING_SNAKE_CASE    )

								scheduler.set_timesteps(self.num_inference_steps			,       device=_SCREAMING_SNAKE_CASE    )
								a_     			=   torch.manual_seed(0    )

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

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

									a_     			=   model(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

									a_     			=   scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       generator=_SCREAMING_SNAKE_CASE    )
									a_     			=   output.prev_sample

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

								assert abs(result_sum.item() - 1_5_2.3_1_9_2    ) < 1E-2
								assert abs(result_mean.item() - 0.1_9_8_3    ) < 1E-3



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


_A							     =       random.Random()

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any=1.0     ,					UpperCamelCase		:      Union[str, Any]=None     ,					UpperCamelCase		:      Union[str, Any]=None	)     ->	List[Any]:



	"""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 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								a_     			=   """facebook/musicgen-small"""
								a_     			=   tempfile.mkdtemp()





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return TaTokenizer.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								shutil.rmtree(self.tmpdirname    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_tokenizer()
								a_     			=   self.get_feature_extractor()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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

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

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





							def   __magic_name__      (							self    ):
								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=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								a_     			=   MusicgenProcessor.from_pretrained(
								    self.tmpdirname			,       bos_token="""(BOS)"""			,       eos_token="""(EOS)"""			,       do_normalize=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

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

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   floats_list((3, 1000)    )

								a_     			=   feature_extractor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )
								a_     			=   processor(_SCREAMING_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   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   """This is a test string"""

								a_     			=   processor(text=_SCREAMING_SNAKE_CASE    )

								a_     			=   tokenizer(_SCREAMING_SNAKE_CASE    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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

								a_     			=   processor.batch_decode(sequences=_SCREAMING_SNAKE_CASE    )
								a_     			=   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    )

								self.assertListEqual(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor(padding_side="""left"""    )
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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

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

								self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								for audio in decoded_audios:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       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)    )