


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Union[str, Any]										=						(DDIMParallelScheduler,)
	_lowerCAmelCase     :		str										=						(('''eta''', 0.0), ('''num_inference_steps''', 5_0))


	def  A(    self				,     **lowercase__):
							__UpperCAmelCase			:							List[Any]	    =	{
							    '''num_train_timesteps''': 1_0_0_0,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**lowercase__)
							return config


	def  A(    self				,     **lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	self.scheduler_classes[0]
							__UpperCAmelCase			:							Optional[int]	    =	self.get_scheduler_config(**lowercase__)
							__UpperCAmelCase			:							List[str]	    =	scheduler_class(**lowercase__)

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Any	    =	1_0, 0.0

							__UpperCAmelCase			:							Dict	    =	self.dummy_model()
							__UpperCAmelCase			:							Optional[int]	    =	self.dummy_sample_deter

							scheduler.set_timesteps(lowercase__)

							for t in scheduler.timesteps:
													__UpperCAmelCase			:							Optional[Any]	    =	model(lowercase__				,     lowercase__)
													__UpperCAmelCase			:							List[str]	    =	scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     lowercase__).prev_sample

							return sample


	def  A(    self):
							for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=lowercase__)


	def  A(    self):
							for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	self.scheduler_classes[0]
							__UpperCAmelCase			:							List[str]	    =	self.get_scheduler_config(steps_offset=1)
							__UpperCAmelCase			:							str	    =	scheduler_class(**lowercase__)
							scheduler.set_timesteps(5)
							assert torch.equal(scheduler.timesteps				,     torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]))


	def  A(    self):
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,     [0.0_0_2, 0.0_2, 0.2, 2]):
													self.check_over_configs(beta_start=lowercase__				,     beta_end=lowercase__)


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


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


	def  A(    self):
							for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=lowercase__)


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


	def  A(    self):
							for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=lowercase__)


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


	def  A(    self):
							for t in [1, 1_0, 4_9]:
													self.check_over_forward(time_step=lowercase__)


	def  A(    self):
							for t, num_inference_steps in zip([1, 1_0, 5_0]				,     [1_0, 5_0, 5_0_0]):
													self.check_over_forward(time_step=lowercase__				,     num_inference_steps=lowercase__)


	def  A(    self):
							for t, eta in zip([1, 1_0, 4_9]				,     [0.0, 0.5, 1.0]):
													self.check_over_forward(time_step=lowercase__				,     eta=lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.scheduler_classes[0]
							__UpperCAmelCase			:							str	    =	self.get_scheduler_config()
							__UpperCAmelCase			:							List[Any]	    =	scheduler_class(**lowercase__)

							assert torch.sum(torch.abs(scheduler._get_variance(0				,     0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_2_0				,     4_0_0) - 0.1_4_7_7_1)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_8_0				,     9_6_0) - 0.3_2_4_6_0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0				,     0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_8_7				,     4_8_6) - 0.0_0_9_7_9)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9				,     9_9_8) - 0.0_2)) < 1e-5


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	self.scheduler_classes[0]
							__UpperCAmelCase			:							str	    =	self.get_scheduler_config()
							__UpperCAmelCase			:							Optional[int]	    =	scheduler_class(**lowercase__)

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Union[str, Any]	    =	1_0, 0.0
							scheduler.set_timesteps(lowercase__)

							__UpperCAmelCase			:							Optional[Any]	    =	self.dummy_model()
							__UpperCAmelCase			:							str	    =	self.dummy_sample_deter
							__UpperCAmelCase			:							Optional[Any]	    =	self.dummy_sample_deter + 0.1
							__UpperCAmelCase			:							Optional[Any]	    =	self.dummy_sample_deter - 0.1

							__UpperCAmelCase			:							Dict	    =	samplea.shape[0]
							__UpperCAmelCase			:							Dict	    =	torch.stack([samplea, samplea, samplea]				,     dim=0)
							__UpperCAmelCase			:							int	    =	torch.arange(lowercase__)[0:3, None].repeat(1				,     lowercase__)

							__UpperCAmelCase			:							Any	    =	model(samples.flatten(0				,     1)				,     timesteps.flatten(0				,     1))
							__UpperCAmelCase			:							Optional[int]	    =	scheduler.batch_step_no_noise(lowercase__				,     timesteps.flatten(0				,     1)				,     samples.flatten(0				,     1)				,     lowercase__)

							__UpperCAmelCase			:							int	    =	torch.sum(torch.abs(lowercase__))
							__UpperCAmelCase			:							List[Any]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 1_1_4_7.7_9_0_4) < 1e-2
							assert abs(result_mean.item() - 0.4_9_8_2) < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	self.full_loop()

							__UpperCAmelCase			:							List[Any]	    =	torch.sum(torch.abs(lowercase__))
							__UpperCAmelCase			:							List[Any]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 1_7_2.0_0_6_7) < 1e-2
							assert abs(result_mean.item() - 0.2_2_3_9_6_7) < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	self.full_loop(prediction_type='''v_prediction''')

							__UpperCAmelCase			:							Union[str, Any]	    =	torch.sum(torch.abs(lowercase__))
							__UpperCAmelCase			:							int	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 5_2.5_3_0_2) < 1e-2
							assert abs(result_mean.item() - 0.0_6_8_4) < 1e-3


	def  A(    self):
							# We specify different beta, so that the first alpha is 0.99
							__UpperCAmelCase			:							str	    =	self.full_loop(set_alpha_to_one=lowercase__				,     beta_start=0.0_1)
							__UpperCAmelCase			:							str	    =	torch.sum(torch.abs(lowercase__))
							__UpperCAmelCase			:							List[str]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 1_4_9.8_2_9_5) < 1e-2
							assert abs(result_mean.item() - 0.1_9_5_1) < 1e-3


	def  A(    self):
							# We specify different beta, so that the first alpha is 0.99
							__UpperCAmelCase			:							Any	    =	self.full_loop(set_alpha_to_one=lowercase__				,     beta_start=0.0_1)
							__UpperCAmelCase			:							Any	    =	torch.sum(torch.abs(lowercase__))
							__UpperCAmelCase			:							Optional[Any]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 1_4_9.0_7_8_4) < 1e-2
							assert abs(result_mean.item() - 0.1_9_4_1) < 1e-3





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch

if is_datasets_available():
				from datasets import load_dataset

lowerCAmelCase       		=    random.Random()




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




						'''simple docstring'''







						if rng is None:
												__UpperCAmelCase			:							str	    =	global_rng

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

						return values




class 							lowerCamelCase   (  unittest.TestCase ):


	def __init__(    self				,     lowercase__				,     lowercase__=7				,     lowercase__=4_0_0				,     lowercase__=2_0_0_0				,     lowercase__=2_0_4_8				,     lowercase__=1_2_8				,     lowercase__=1				,     lowercase__=5_1_2				,     lowercase__=3_0				,     lowercase__=4_4_1_0_0				,     ):
							__UpperCAmelCase			:							Optional[Any]	    =	parent
							__UpperCAmelCase			:							Any	    =	batch_size
							__UpperCAmelCase			:							int	    =	min_seq_length
							__UpperCAmelCase			:							List[str]	    =	max_seq_length
							__UpperCAmelCase			:							Dict	    =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__UpperCAmelCase			:							Any	    =	spectrogram_length
							__UpperCAmelCase			:							List[Any]	    =	feature_size
							__UpperCAmelCase			:							Union[str, Any]	    =	num_audio_channels
							__UpperCAmelCase			:							Optional[int]	    =	hop_length
							__UpperCAmelCase			:							Tuple	    =	chunk_length
							__UpperCAmelCase			:							Any	    =	sampling_rate


	def  A(    self):
							return {
							    "spectrogram_length": self.spectrogram_length,
							    "feature_size": self.feature_size,
							    "num_audio_channels": self.num_audio_channels,
							    "hop_length": self.hop_length,
							    "chunk_length": self.chunk_length,
							    "sampling_rate": self.sampling_rate,
							}


	def  A(    self				,     lowercase__=False				,     lowercase__=False):
							def _flatten(lowercase__):
													return list(itertools.chain(*lowercase__))

							if equal_length:
													__UpperCAmelCase			:							str	    =	[floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
							else:
													# make sure that inputs increase in size
													__UpperCAmelCase			:							List[str]	    =	[
													    floats_list((x, self.feature_size))
													    for x in range(self.min_seq_length				,     self.max_seq_length				,     self.seq_length_diff)
													]
							if numpify:
													__UpperCAmelCase			:							List[str]	    =	[np.asarray(lowercase__) for x in speech_inputs]
							return speech_inputs






@require_torch
@require_torchaudio
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Optional[int]										=						TvltFeatureExtractor


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	TvltFeatureExtractionTester(self)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)
							self.assertTrue(hasattr(lowercase__				,     '''spectrogram_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''feature_size'''))
							self.assertTrue(hasattr(lowercase__				,     '''num_audio_channels'''))
							self.assertTrue(hasattr(lowercase__				,     '''hop_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''chunk_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''sampling_rate'''))


	def  A(    self):
							__UpperCAmelCase			:							str	    =	self.feature_extraction_class(**self.feat_extract_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							str	    =	feat_extract_first.save_pretrained(lowercase__)[0]
													check_json_file_has_correct_format(lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class.from_pretrained(lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	feat_extract_first.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	feat_extract_second.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	dict_first.pop('''mel_filters''')
							__UpperCAmelCase			:							Union[str, Any]	    =	dict_second.pop('''mel_filters''')
							self.assertTrue(np.allclose(lowercase__				,     lowercase__))
							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							Union[str, Any]	    =	os.path.join(lowercase__				,     '''feat_extract.json''')
													feat_extract_first.to_json_file(lowercase__)
													__UpperCAmelCase			:							str	    =	self.feature_extraction_class.from_json_file(lowercase__)

							__UpperCAmelCase			:							Any	    =	feat_extract_first.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	feat_extract_second.to_dict()
							__UpperCAmelCase			:							Tuple	    =	dict_first.pop('''mel_filters''')
							__UpperCAmelCase			:							List[str]	    =	dict_second.pop('''mel_filters''')
							self.assertTrue(np.allclose(lowercase__				,     lowercase__))
							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							# Initialize feature_extractor
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)

							# create three inputs of length 800, 1000, and 1200
							__UpperCAmelCase			:							Optional[int]	    =	[floats_list((1, x))[0] for x in range(8_0_0				,     1_4_0_0				,     2_0_0)]
							__UpperCAmelCase			:							int	    =	[np.asarray(lowercase__) for speech_input in speech_inputs]

							# Test not batched input
							__UpperCAmelCase			:							Dict	    =	feature_extractor(np_speech_inputs[0]				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test batched
							__UpperCAmelCase			:							List[str]	    =	feature_extractor(lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test audio masking
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(
							    lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0				,     mask_audio=lowercase__).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test 2-D numpy arrays are batched.
							__UpperCAmelCase			:							Any	    =	[floats_list((1, x))[0] for x in (8_0_0, 8_0_0, 8_0_0)]
							__UpperCAmelCase			:							Optional[Any]	    =	np.asarray(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values
							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[int]	    =	load_dataset('''hf-internal-testing/librispeech_asr_dummy'''				,     '''clean'''				,     split='''validation''')
							# automatic decoding with librispeech
							__UpperCAmelCase			:							int	    =	ds.sort('''id''').select(range(lowercase__))[:num_samples]['''audio''']

							return [x["array"] for x in speech_samples]


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	self._load_datasamples(1)
							__UpperCAmelCase			:							Tuple	    =	TvltFeatureExtractor()
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(lowercase__				,     return_tensors='''pt''').audio_values

							self.assertEquals(audio_values.shape				,     (1, 1, 1_9_2, 1_2_8))

							__UpperCAmelCase			:							int	    =	torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]])
							self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,     lowercase__				,     atol=1e-4))

