




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

_A     =			random.Random()





def __UpperCamelCase			(       _A ,  _A=1.0 ,  _A=None ,  _A=None  ):
						if rng is None:
												lowerCAmelCase_      			=  global_rng

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

						return values


class  A		(  unittest.TestCase     ):



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__=7,	UpperCamelCase__=400,	UpperCamelCase__=2000,	UpperCamelCase__=2048,	UpperCamelCase__=128,	UpperCamelCase__=1,	UpperCamelCase__=512,	UpperCamelCase__=30,	UpperCamelCase__=4_4100,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  parent
								lowerCAmelCase_      			=  batch_size
								lowerCAmelCase_      			=  min_seq_length
								lowerCAmelCase_      			=  max_seq_length
								lowerCAmelCase_      			=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								lowerCAmelCase_      			=  spectrogram_length
								lowerCAmelCase_      			=  feature_size
								lowerCAmelCase_      			=  num_audio_channels
								lowerCAmelCase_      			=  hop_length
								lowerCAmelCase_      			=  chunk_length
								lowerCAmelCase_      			=  sampling_rate



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								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        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__=False,	UpperCamelCase__=False   ):







								"""simple docstring"""



								def _flatten(UpperCamelCase__   ):
														return list(itertools.chain(*UpperCamelCase__   )   )

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







@require_torch
@require_torchaudio
class  A		(  __UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   TvltFeatureExtractor



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  TvltFeatureExtractionTester(self   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.feature_extraction_class(**self.feat_extract_dict   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''spectrogram_length'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''feature_size'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''num_audio_channels'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''hop_length'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''chunk_length'''   )   )
								self.assertTrue(hasattr(UpperCamelCase__,	'''sampling_rate'''   )   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.feature_extraction_class(**self.feat_extract_dict   )

								with tempfile.TemporaryDirectory() as tmpdirname:
														lowerCAmelCase_      			=  feat_extract_first.save_pretrained(UpperCamelCase__   )[0]
														check_json_file_has_correct_format(UpperCamelCase__   )
														lowerCAmelCase_      			=  self.feature_extraction_class.from_pretrained(UpperCamelCase__   )

								lowerCAmelCase_      			=  feat_extract_first.to_dict()
								lowerCAmelCase_      			=  feat_extract_second.to_dict()
								lowerCAmelCase_      			=  dict_first.pop('''mel_filters'''   )
								lowerCAmelCase_      			=  dict_second.pop('''mel_filters'''   )
								self.assertTrue(np.allclose(UpperCamelCase__,	UpperCamelCase__   )   )
								self.assertEqual(UpperCamelCase__,	UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.feature_extraction_class(**self.feat_extract_dict   )

								with tempfile.TemporaryDirectory() as tmpdirname:
														lowerCAmelCase_      			=  os.path.join(UpperCamelCase__,	'''feat_extract.json'''   )
														feat_extract_first.to_json_file(UpperCamelCase__   )
														lowerCAmelCase_      			=  self.feature_extraction_class.from_json_file(UpperCamelCase__   )

								lowerCAmelCase_      			=  feat_extract_first.to_dict()
								lowerCAmelCase_      			=  feat_extract_second.to_dict()
								lowerCAmelCase_      			=  dict_first.pop('''mel_filters'''   )
								lowerCAmelCase_      			=  dict_second.pop('''mel_filters'''   )
								self.assertTrue(np.allclose(UpperCamelCase__,	UpperCamelCase__   )   )
								self.assertEqual(UpperCamelCase__,	UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.feature_extraction_class(**self.feat_extract_dict   )

								# create three inputs of length 800, 1000, and 1200
								lowerCAmelCase_      			=  [floats_list((1, x)   )[0] for x in range(800,	1400,	200   )]
								lowerCAmelCase_      			=  [np.asarray(UpperCamelCase__   ) for speech_input in speech_inputs]

								# Test not batched input
								lowerCAmelCase_      			=  feature_extractor(np_speech_inputs[0],	return_tensors='''np''',	sampling_rate=4_4100   ).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
								lowerCAmelCase_      			=  feature_extractor(UpperCamelCase__,	return_tensors='''np''',	sampling_rate=4_4100   ).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
								lowerCAmelCase_      			=  feature_extractor(
								    UpperCamelCase__,	return_tensors='''np''',	sampling_rate=4_4100,	mask_audio=UpperCamelCase__   ).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.
								lowerCAmelCase_      			=  [floats_list((1, x)   )[0] for x in (800, 800, 800)]
								lowerCAmelCase_      			=  np.asarray(UpperCamelCase__   )
								lowerCAmelCase_      			=  feature_extractor(UpperCamelCase__,	return_tensors='''np''',	sampling_rate=4_4100   ).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        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  load_dataset('''hf-internal-testing/librispeech_asr_dummy''',	'''clean''',	split='''validation'''   )
								# automatic decoding with librispeech
								lowerCAmelCase_      			=  ds.sort('''id'''   ).select(range(UpperCamelCase__   )   )[:num_samples]['''audio''']

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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self._load_datasamples(1   )
								lowerCAmelCase_      			=  TvltFeatureExtractor()
								lowerCAmelCase_      			=  feature_extractor(UpperCamelCase__,	return_tensors='''pt'''   ).audio_values

								self.assertEquals(audio_values.shape,	(1, 1, 192, 128)   )

								lowerCAmelCase_      			=  torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]   )
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2],	UpperCamelCase__,	atol=1E-4   )   )










import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   (UnCLIPScheduler,)



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''num_train_timesteps''': 1000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**UpperCamelCase__   )
								return config



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for time_step in [0, 500, 999]:
														for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																										continue

																				self.check_over_forward(time_step=UpperCamelCase__,	prev_timestep=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''fixed_small_log'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_549_625   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_994_987   )   ) < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''learned_range'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  0.5

								assert scheduler._get_variance(1,	predicted_variance=UpperCamelCase__   ) - -10.1_712_790 < 1E-5
								assert scheduler._get_variance(487,	predicted_variance=UpperCamelCase__   ) - -5.7_998_052 < 1E-5
								assert scheduler._get_variance(999,	predicted_variance=UpperCamelCase__   ) - -0.0_010_011 < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 252.2_682_495   ) < 1E-2
								assert abs(result_mean.item() - 0.3_284_743   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								scheduler.set_timesteps(25   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														if i + 1 == timesteps.shape[0]:
																				lowerCAmelCase_      			=  None
														else:
																				lowerCAmelCase_      			=  timesteps[i + 1]

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(
														    UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	prev_timestep=UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 258.2_044_983   ) < 1E-2
								assert abs(result_mean.item() - 0.3_362_038   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass




