import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp





@slow
@require_flax
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return f"""gaussian_noise_s={seed}_shape={'_'.join([str(UpperCamelCase__		) for s in shape]		)}.npy"""


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												super().tearDown()
												gc.collect()


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=(4, 4, 64, 64)					,	UpperCamelCase__			:       Optional[Any]=False		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							jnp.bfloataa if fpaa else jnp.floataa
												SCREAMING_SNAKE_CASE							:					Any           =							jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__					,	UpperCamelCase__		)		)					,	dtype=UpperCamelCase__		)
												return image


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       Dict=False					,	UpperCamelCase__			:       List[Any]="CompVis/stable-diffusion-v1-4"		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							jnp.bfloataa if fpaa else jnp.floataa
												SCREAMING_SNAKE_CASE							:					Any           =							'''bf16''' if fpaa else None

												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[Any]           =							FlaxUNetaDConditionModel.from_pretrained(
												    UpperCamelCase__					,	subfolder='''unet'''					,	dtype=UpperCamelCase__					,	revision=UpperCamelCase__		)
												return model, params


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       List[str]=0					,	UpperCamelCase__			:       Dict=(4, 77, 768)					,	UpperCamelCase__			:       Optional[int]=False		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							jnp.bfloataa if fpaa else jnp.floataa
												SCREAMING_SNAKE_CASE							:					List[Any]           =							jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__					,	UpperCamelCase__		)		)					,	dtype=UpperCamelCase__		)
												return hidden_states


						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
						        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
						        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
						        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
						        # fmt: on
						    ]		)
						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					str           =							self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''					,	fpaa=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_latents(UpperCamelCase__					,	fpaa=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_encoder_hidden_states(UpperCamelCase__					,	fpaa=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							model.apply(
												    {'''params''': params}					,	UpperCamelCase__					,	jnp.array(UpperCamelCase__					,	dtype=jnp.intaa		)					,	encoder_hidden_states=UpperCamelCase__					,	).sample

												assert sample.shape == latents.shape

												SCREAMING_SNAKE_CASE							:					List[Any]           =							jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)					,	dtype=jnp.floataa		)
												SCREAMING_SNAKE_CASE							:					str           =							jnp.array(UpperCamelCase__					,	dtype=jnp.floataa		)

												# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
												assert jnp.allclose(UpperCamelCase__					,	UpperCamelCase__					,	atol=1E-2		)



						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
						        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
						        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
						        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
						        # fmt: on
						    ]		)
						def  __A ( self			:       str					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''					,	fpaa=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_latents(UpperCamelCase__					,	shape=(4, 4, 96, 96)					,	fpaa=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.get_encoder_hidden_states(UpperCamelCase__					,	shape=(4, 77, 1024)					,	fpaa=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							model.apply(
												    {'''params''': params}					,	UpperCamelCase__					,	jnp.array(UpperCamelCase__					,	dtype=jnp.intaa		)					,	encoder_hidden_states=UpperCamelCase__					,	).sample

												assert sample.shape == latents.shape

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)					,	dtype=jnp.floataa		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							jnp.array(UpperCamelCase__					,	dtype=jnp.floataa		)

												# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
												assert jnp.allclose(UpperCamelCase__					,	UpperCamelCase__					,	atol=1E-2		)






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

__UpperCamelCase	: Dict    		=  random.Random()
def    A			(					_lowercase	,						_lowercase=1.0	,						_lowercase=None	,						_lowercase=None		):
						if rng is None:
												SCREAMING_SNAKE_CASE							:					Any           =							global_rng

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

						return values





class       lowercase__		(     unittest.TestCase):


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       str=7					,	UpperCamelCase__			:       Any=400					,	UpperCamelCase__			:       List[str]=2000					,	UpperCamelCase__			:       List[Any]=2048					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Union[str, Any]=1					,	UpperCamelCase__			:       List[Any]=512					,	UpperCamelCase__			:       str=30					,	UpperCamelCase__			:       Tuple=4_4100					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							parent
												SCREAMING_SNAKE_CASE							:					str           =							batch_size
												SCREAMING_SNAKE_CASE							:					str           =							min_seq_length
												SCREAMING_SNAKE_CASE							:					Dict           =							max_seq_length
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							spectrogram_length
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							feature_size
												SCREAMING_SNAKE_CASE							:					Tuple           =							num_audio_channels
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							hop_length
												SCREAMING_SNAKE_CASE							:					List[Any]           =							chunk_length
												SCREAMING_SNAKE_CASE							:					str           =							sampling_rate


						def  __A ( self			:       Optional[Any]		):





												'''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  __A ( self			:       Tuple					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       Optional[int]=False		):





												'''simple docstring'''





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

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






@require_torch
@require_torchaudio
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	TvltFeatureExtractor


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							TvltFeatureExtractionTester(self		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							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  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.feature_extraction_class(**self.feat_extract_dict		)

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							feat_extract_first.to_dict()
												SCREAMING_SNAKE_CASE							:					str           =							feat_extract_second.to_dict()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict_first.pop('''mel_filters'''		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict_second.pop('''mel_filters'''		)
												self.assertTrue(np.allclose(UpperCamelCase__					,	UpperCamelCase__		)		)
												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.feature_extraction_class(**self.feat_extract_dict		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							os.path.join(UpperCamelCase__					,	'''feat_extract.json'''		)
																		feat_extract_first.to_json_file(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[str]           =							self.feature_extraction_class.from_json_file(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							feat_extract_first.to_dict()
												SCREAMING_SNAKE_CASE							:					str           =							feat_extract_second.to_dict()
												SCREAMING_SNAKE_CASE							:					int           =							dict_first.pop('''mel_filters'''		)
												SCREAMING_SNAKE_CASE							:					Any           =							dict_second.pop('''mel_filters'''		)
												self.assertTrue(np.allclose(UpperCamelCase__					,	UpperCamelCase__		)		)
												self.assertEqual(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							self.feature_extraction_class(**self.feat_extract_dict		)

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

												# Test not batched input
												SCREAMING_SNAKE_CASE							:					List[Any]           =							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
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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
												SCREAMING_SNAKE_CASE							:					List[str]           =							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.
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[floats_list((1, x)		)[0] for x in (800, 800, 800)]
												SCREAMING_SNAKE_CASE							:					Dict           =							np.asarray(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							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  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





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

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



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self._load_datasamples(1		)
												SCREAMING_SNAKE_CASE							:					int           =							TvltFeatureExtractor()
												SCREAMING_SNAKE_CASE							:					List[Any]           =							feature_extractor(UpperCamelCase__					,	return_tensors='''pt'''		).audio_values

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

												SCREAMING_SNAKE_CASE							:					str           =							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
												self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,	UpperCamelCase__					,	atol=1E-4		)		)





