





import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__UpperCamelCase:  Tuple   =   logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Dict      ,						_lowercase    :   bool = True )      ->       Union[str, Any]:




		'''simple docstring'''
		lowercase__				:			Optional[Any]            =		{}
		for name in model.attn_processors.keys():
				lowercase__				:			Union[str, Any]            =		name.endswith('attn2.processor' ) and not (
				    name.startswith('transformer_in' ) or 'temp_attentions' in name.split('.' )
				)
				lowercase__				:			List[Any]            =		model.config.cross_attention_dim if has_cross_attention else None
				if name.startswith('mid_block' ):
						lowercase__				:			Tuple            =		model.config.block_out_channels[-1]
				elif name.startswith('up_blocks' ):
						lowercase__				:			Optional[int]            =		int(name[len('up_blocks.' )] )
						lowercase__				:			Dict            =		list(reversed(model.config.block_out_channels ) )[block_id]
				elif name.startswith('down_blocks' ):
						lowercase__				:			Any            =		int(name[len('down_blocks.' )] )
						lowercase__				:			List[str]            =		model.config.block_out_channels[block_id]
				elif name.startswith('transformer_in' ):
						# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
						lowercase__				:			Dict            =		8 * model.config.attention_head_dim

				lowercase__				:			str            =		LoRAAttnProcessor(hidden_size=_lowercase      ,						cross_attention_dim=_lowercase )
				lowercase__				:			Optional[int]            =		lora_attn_procs[name].to(model.device )

				if mock_weights:
						# add 1 to weights to mock trained weights
						with torch.no_grad():
								lora_attn_procs[name].to_q_lora.up.weight += 1
								lora_attn_procs[name].to_k_lora.up.weight += 1
								lora_attn_procs[name].to_v_lora.up.weight += 1
								lora_attn_procs[name].to_out_lora.up.weight += 1

		return lora_attn_procs





@skip_mps
class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							UNetaDConditionModel
	_A       	=							"sample"

	@property
	def       snake_case__(	self:	Dict ):
			lowercase__				:			Any            =		4
			lowercase__				:			Any            =		4
			lowercase__				:			str            =		4
			lowercase__				:			List[str]            =		(32, 32)

			lowercase__				:			Optional[int]            =		floats_tensor((batch_size, num_channels, num_frames) + sizes ).to(lowerCamelCase_ )
			lowercase__				:			Dict            =		torch.tensor([10] ).to(lowerCamelCase_ )
			lowercase__				:			str            =		floats_tensor((batch_size, 4, 32) ).to(lowerCamelCase_ )

			return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}

	@property
	def       snake_case__(	self:	Dict ):
			return (4, 4, 32, 32)

	@property
	def       snake_case__(	self:	Any ):
			return (4, 4, 32, 32)

	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			Optional[Any]            =		{
			    'block_out_channels': (32, 64),
			    'down_block_types': (
			        'CrossAttnDownBlock3D',
			        'DownBlock3D',
			    ),
			    'up_block_types': ('UpBlock3D', 'CrossAttnUpBlock3D'),
			    'cross_attention_dim': 32,
			    'attention_head_dim': 8,
			    'out_channels': 4,
			    'in_channels': 4,
			    'layers_per_block': 1,
			    'sample_size': 32,
			}
			lowercase__				:			List[Any]            =		self.dummy_input
			return init_dict, inputs_dict

	@unittest.skipIf(
	    torch_device != 'cuda' or not is_xformers_available(),				reason='XFormers attention is only available with CUDA and `xformers` installed',				)
	def       snake_case__(	self:	List[str] ):
			lowercase__      ,							lowercase__				:			Any            =		self.prepare_init_args_and_inputs_for_common()
			lowercase__				:			Union[str, Any]            =		self.model_class(**lowerCamelCase_ )

			model.enable_xformers_memory_efficient_attention()

			assert (
			    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
			    == "XFormersAttnProcessor"
			), "xformers is not enabled"

	def       snake_case__(	self:	List[str] ):
			lowercase__      ,							lowercase__				:			Any            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			List[Any]            =		32

			lowercase__				:			Optional[int]            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )
			model.eval()

			with torch.no_grad():
					lowercase__				:			Optional[Any]            =		model(**lowerCamelCase_ )

					if isinstance(lowerCamelCase_,				lowerCamelCase_ ):
							lowercase__				:			int            =		output.sample

			self.assertIsNotNone(lowerCamelCase_ )
			lowercase__				:			Dict            =		inputs_dict['sample'].shape
			self.assertEqual(output.shape,				lowerCamelCase_,				'Input and output shapes do not match' )

	def       snake_case__(	self:	List[str] ):
			lowercase__      ,							lowercase__				:			Tuple            =		self.prepare_init_args_and_inputs_for_common()
			lowercase__				:			int            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )
			model.eval()

			with torch.no_grad():
					# Warmup pass when using mps (see #372)
					if torch_device == "mps" and isinstance(lowerCamelCase_,				lowerCamelCase_ ):
							model(**self.dummy_input )

					lowercase__				:			int            =		model(**lowerCamelCase_ )
					if isinstance(lowerCamelCase_,				lowerCamelCase_ ):
							lowercase__				:			Union[str, Any]            =		first.sample

					lowercase__				:			Union[str, Any]            =		model(**lowerCamelCase_ )
					if isinstance(lowerCamelCase_,				lowerCamelCase_ ):
							lowercase__				:			Tuple            =		second.sample

			lowercase__				:			Tuple            =		first.cpu().numpy()
			lowercase__				:			List[str]            =		second.cpu().numpy()
			lowercase__				:			str            =		out_a[~np.isnan(lowerCamelCase_ )]
			lowercase__				:			str            =		out_a[~np.isnan(lowerCamelCase_ )]
			lowercase__				:			int            =		np.amax(np.abs(out_a - out_a ) )
			self.assertLessEqual(lowerCamelCase_,				1E-5 )

	def       snake_case__(	self:	Tuple ):
			lowercase__      ,							lowercase__				:			Any            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Optional[int]            =		8

			lowercase__				:			Any            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )
			model.eval()

			model.set_attention_slice('auto' )
			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_ )
			assert output is not None

			model.set_attention_slice('max' )
			with torch.no_grad():
					lowercase__				:			Tuple            =		model(**lowerCamelCase_ )
			assert output is not None

			model.set_attention_slice(2 )
			with torch.no_grad():
					lowercase__				:			Any            =		model(**lowerCamelCase_ )
			assert output is not None

	def       snake_case__(	self:	Optional[int] ):
			lowercase__      ,							lowercase__				:			Union[str, Any]            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Tuple            =		8

			lowercase__				:			Dict            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			Union[str, Any]            =		model(**lowerCamelCase_ ).sample

			lowercase__				:			List[Any]            =		create_lora_layers(lowerCamelCase_ )

			# make sure we can set a list of attention processors
			model.set_attn_processor(lowerCamelCase_ )
			model.to(lowerCamelCase_ )

			# test that attn processors can be set to itself
			model.set_attn_processor(model.attn_processors )

			with torch.no_grad():
					lowercase__				:			Dict            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.0} ).sample
					lowercase__				:			Any            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.5} ).sample
					lowercase__				:			int            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.5} ).sample

			assert (samplea - samplea).abs().max() < 3E-3
			assert (samplea - samplea).abs().max() < 3E-3

			# sample 2 and sample 3 should be different
			assert (samplea - samplea).abs().max() > 3E-3

	def       snake_case__(	self:	List[str] ):
			lowercase__      ,							lowercase__				:			Optional[int]            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Optional[int]            =		8

			torch.manual_seed(0 )
			lowercase__				:			List[Any]            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			Optional[Any]            =		model(**lowerCamelCase_ ).sample

			lowercase__				:			str            =		create_lora_layers(lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.5} ).sample

			with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_attn_procs(lowerCamelCase_ )
					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_,				'pytorch_lora_weights.bin' ) ) )
					torch.manual_seed(0 )
					lowercase__				:			Tuple            =		self.model_class(**lowerCamelCase_ )
					new_model.to(lowerCamelCase_ )
					new_model.load_attn_procs(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			Union[str, Any]            =		new_model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.5} ).sample

			assert (sample - new_sample).abs().max() < 1E-3

			# LoRA and no LoRA should NOT be the same
			assert (sample - old_sample).abs().max() > 1E-4

	def       snake_case__(	self:	Tuple ):
			lowercase__      ,							lowercase__				:			str            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Tuple            =		8

			torch.manual_seed(0 )
			lowercase__				:			Optional[int]            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			Optional[Any]            =		model(**lowerCamelCase_ ).sample

			lowercase__				:			List[Any]            =		create_lora_layers(lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.5} ).sample

			with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_attn_procs(lowerCamelCase_,				safe_serialization=lowerCamelCase_ )
					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_,				'pytorch_lora_weights.safetensors' ) ) )
					torch.manual_seed(0 )
					lowercase__				:			Union[str, Any]            =		self.model_class(**lowerCamelCase_ )
					new_model.to(lowerCamelCase_ )
					new_model.load_attn_procs(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			List[str]            =		new_model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.5} ).sample

			assert (sample - new_sample).abs().max() < 3E-3

			# LoRA and no LoRA should NOT be the same
			assert (sample - old_sample).abs().max() > 1E-4

	def       snake_case__(	self:	int ):
			# enable deterministic behavior for gradient checkpointing
			lowercase__      ,							lowercase__				:			Any            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Dict            =		8

			torch.manual_seed(0 )
			lowercase__				:			str            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )

			lowercase__				:			Union[str, Any]            =		create_lora_layers(lowerCamelCase_,				mock_weights=lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )
			# Saving as torch, properly reloads with directly filename
			with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_attn_procs(lowerCamelCase_ )
					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_,				'pytorch_lora_weights.bin' ) ) )
					torch.manual_seed(0 )
					lowercase__				:			int            =		self.model_class(**lowerCamelCase_ )
					new_model.to(lowerCamelCase_ )
					new_model.load_attn_procs(lowerCamelCase_,				weight_name='pytorch_lora_weights.bin' )

	def       snake_case__(	self:	int ):
			lowercase__      ,							lowercase__				:			int            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			int            =		8

			torch.manual_seed(0 )
			lowercase__				:			List[str]            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )

			lowercase__				:			str            =		create_lora_layers(lowerCamelCase_,				mock_weights=lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )
			# Saving as torch, properly reloads with directly filename
			with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_attn_procs(lowerCamelCase_ )
					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_,				'pytorch_lora_weights.bin' ) ) )
					torch.manual_seed(0 )
					lowercase__				:			Union[str, Any]            =		self.model_class(**lowerCamelCase_ )
					new_model.to(lowerCamelCase_ )
					with self.assertRaises(lowerCamelCase_ ) as e:
							new_model.load_attn_procs(lowerCamelCase_,				use_safetensors=lowerCamelCase_ )
					self.assertIn('Error no file named pytorch_lora_weights.safetensors',				str(e.exception ) )

	def       snake_case__(	self:	Optional[Any] ):
			lowercase__      ,							lowercase__				:			Dict            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			List[str]            =		8

			torch.manual_seed(0 )
			lowercase__				:			Tuple            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			Union[str, Any]            =		model(**lowerCamelCase_ ).sample

			lowercase__				:			List[Any]            =		create_lora_layers(lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			str            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.0} ).sample

			model.set_attn_processor(AttnProcessor() )

			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_ ).sample

			assert (sample - new_sample).abs().max() < 1E-4
			assert (sample - old_sample).abs().max() < 3E-3

	@unittest.skipIf(
	    torch_device != 'cuda' or not is_xformers_available(),				reason='XFormers attention is only available with CUDA and `xformers` installed',				)
	def       snake_case__(	self:	List[Any] ):
			# enable deterministic behavior for gradient checkpointing
			lowercase__      ,							lowercase__				:			Tuple            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Optional[int]            =		4

			torch.manual_seed(0 )
			lowercase__				:			Union[str, Any]            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )
			lowercase__				:			Optional[Any]            =		create_lora_layers(lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )

			# default
			with torch.no_grad():
					lowercase__				:			List[str]            =		model(**lowerCamelCase_ ).sample

					model.enable_xformers_memory_efficient_attention()
					lowercase__				:			Union[str, Any]            =		model(**lowerCamelCase_ ).sample

					model.disable_xformers_memory_efficient_attention()
					lowercase__				:			Optional[int]            =		model(**lowerCamelCase_ ).sample

			assert (sample - on_sample).abs().max() < 1E-4
			assert (sample - off_sample).abs().max() < 1E-4





	def       snake_case__(	self:	List[Any] ):
			lowercase__      ,							lowercase__				:			Union[str, Any]            =		self.prepare_init_args_and_inputs_for_common()
			lowercase__				:			Union[str, Any]            =		32

			lowercase__				:			str            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )
			model.eval()

			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_ )[0]

			model.enable_forward_chunking()
			with torch.no_grad():
					lowercase__				:			int            =		model(**lowerCamelCase_ )[0]

			self.assertEqual(output.shape,				output_a.shape,				'Shape doesn\'t match' )
			assert np.abs(output.cpu() - output_a.cpu() ).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.









import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
			import torch


__UpperCamelCase:  int   =   random.Random()


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int      ,						_lowercase    :   List[Any]=1.0      ,						_lowercase    :   Dict=None      ,						_lowercase    :   List[str]=None )      ->       List[str]:




		'''simple docstring'''
		if rng is None:
				lowercase__				:			int            =		global_rng

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

		return values


@require_torch
class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any],				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	int=7,				lowerCamelCase_:	Tuple=400,				lowerCamelCase_:	Union[str, Any]=2000,				lowerCamelCase_:	Optional[int]=1,				lowerCamelCase_:	int=0.0,				lowerCamelCase_:	Any=24000,				lowerCamelCase_:	Dict=True,				):
			lowercase__				:			List[str]            =		parent
			lowercase__				:			Any            =		batch_size
			lowercase__				:			List[str]            =		min_seq_length
			lowercase__				:			Tuple            =		max_seq_length
			lowercase__				:			Optional[Any]            =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			lowercase__				:			int            =		feature_size
			lowercase__				:			Optional[Any]            =		padding_value
			lowercase__				:			Optional[int]            =		sampling_rate
			lowercase__				:			str            =		return_attention_mask

	def       snake_case__(	self:	str ):
			return {
			    "feature_size": self.feature_size,
			    "padding_value": self.padding_value,
			    "sampling_rate": self.sampling_rate,
			    "return_attention_mask": self.return_attention_mask,
			}





	def       snake_case__(	self:	Dict,				lowerCamelCase_:	int=False,				lowerCamelCase_:	Optional[Any]=False ):
			def _flatten(lowerCamelCase_:	int ):
					return list(itertools.chain(*lowerCamelCase_ ) )

			if equal_length:
					lowercase__				:			Tuple            =		floats_list((self.batch_size, self.max_seq_length) )
			else:
					# make sure that inputs increase in size
					lowercase__				:			Dict            =		[
					    _flatten(floats_list((x, self.feature_size) ) )
					    for x in range(self.min_seq_length,				self.max_seq_length,				self.seq_length_diff )
					]

			if numpify:
					lowercase__				:			Any            =		[np.asarray(lowerCamelCase_ ) for x in audio_inputs]

			return audio_inputs


@require_torch
class 			__lowerCAmelCase				(					_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							EncodecFeatureExtractor

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			List[Any]            =		EnCodecFeatureExtractionTester(self )

	def       snake_case__(	self:	Optional[int] ):
			# Tests that all call wrap to encode_plus and batch_encode_plus
			lowercase__				:			List[str]            =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
			# create three inputs of length 800, 1000, and 1200
			lowercase__				:			Dict            =		[floats_list((1, x) )[0] for x in range(800,				1400,				200 )]
			lowercase__				:			List[Any]            =		[np.asarray(lowerCamelCase_ ) for audio_input in audio_inputs]

			# Test not batched input
			lowercase__				:			List[str]            =		feat_extract(audio_inputs[0],				return_tensors='np' ).input_values
			lowercase__				:			List[str]            =		feat_extract(np_audio_inputs[0],				return_tensors='np' ).input_values
			self.assertTrue(np.allclose(lowerCamelCase_,				lowerCamelCase_,				atol=1E-3 ) )

			# Test batched
			lowercase__				:			Any            =		feat_extract(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='np' ).input_values
			lowercase__				:			List[str]            =		feat_extract(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='np' ).input_values
			for enc_seq_a, enc_seq_a in zip(lowerCamelCase_,				lowerCamelCase_ ):
					self.assertTrue(np.allclose(lowerCamelCase_,				lowerCamelCase_,				atol=1E-3 ) )

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			Union[str, Any]            =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
			lowercase__				:			Optional[int]            =		np.random.rand(100 ).astype(np.floataa )
			lowercase__				:			Optional[int]            =		np_audio_inputs.tolist()

			for inputs in [py_audio_inputs, np_audio_inputs]:
					lowercase__				:			int            =		feature_extractor.pad([{'input_values': inputs}],				return_tensors='np' )
					self.assertTrue(np_processed.input_values.dtype == np.floataa )
					lowercase__				:			int            =		feature_extractor.pad([{'input_values': inputs}],				return_tensors='pt' )
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa )

	def       snake_case__(	self:	Any,				lowerCamelCase_:	Any ):
			from datasets import load_dataset

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

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

	def       snake_case__(	self:	List[str] ):
			# fmt: off
			lowercase__				:			Optional[int]            =		torch.tensor(
			    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
			     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
			     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
			     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
			     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
			     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03] )
			# fmt: on
			lowercase__				:			Tuple            =		self._load_datasamples(1 )
			lowercase__				:			List[Any]            =		EncodecFeatureExtractor()
			lowercase__				:			Optional[int]            =		feature_extractor(lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(input_values.shape,				(1, 1, 93680) )
			self.assertTrue(torch.allclose(input_values[0, 0, :30],				lowerCamelCase_,				atol=1E-6 ) )

	def       snake_case__(	self:	Dict ):
			# fmt: off
			lowercase__				:			Union[str, Any]            =		torch.tensor(
			    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
			     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
			     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
			     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
			     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
			     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03] )
			# fmt: on
			lowercase__				:			Dict            =		self._load_datasamples(1 )
			lowercase__				:			Tuple            =		[np.tile(input_audio[0][None],				reps=(2, 1) )]
			input_audio[0][1] *= 0.5
			lowercase__				:			Tuple            =		EncodecFeatureExtractor(feature_size=2 )
			lowercase__				:			Dict            =		feature_extractor(lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(input_values.shape,				(1, 2, 93680) )
			self.assertTrue(torch.allclose(input_values[0, 0, :30],				lowerCamelCase_,				atol=1E-6 ) )
			self.assertTrue(torch.allclose(input_values[0, 1, :30],				EXPECTED_INPUT_VALUES * 0.5,				atol=1E-6 ) )





	def       snake_case__(	self:	List[str] ):
			lowercase__				:			List[Any]            =		self._load_datasamples(2 )
			# would be easier if the stride was like
			lowercase__				:			List[str]            =		EncodecFeatureExtractor(feature_size=1,				chunk_length_s=1,				overlap=0.0_1 )

			# pad and trunc raise an error ?
			with self.assertRaisesRegex(
			    lowerCamelCase_,				'^Both padding and truncation were set. Make sure you only set one.$',				):
					lowercase__				:			Tuple            =		feature_extractor(
					    lowerCamelCase_,				padding='max_length',				truncation=lowerCamelCase_,				return_tensors='pt' ).input_values

			# truncate to chunk
			lowercase__				:			List[str]            =		feature_extractor(lowerCamelCase_,				truncation=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 71520) )  # 2 chunks

			# force truncate to max_length
			lowercase__				:			int            =		feature_extractor(
			    lowerCamelCase_,				truncation=lowerCamelCase_,				max_length=48000,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 48000) )

			# pad to chunk
			lowercase__				:			str            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(padded_outputs.shape,				(2, 1, 95280) )

			# pad to chunk
			lowercase__				:			Tuple            =		feature_extractor(lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 95280) )

			# force pad to max length
			lowercase__				:			Any            =		feature_extractor(
			    lowerCamelCase_,				padding='max_length',				max_length=100000,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 100000) )

			# force no pad
			with self.assertRaisesRegex(
			    lowerCamelCase_,				'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',				):
					lowercase__				:			List[str]            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values

			lowercase__				:			List[str]            =		feature_extractor(input_audio[0],				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(1, 1, 93680) )

			# no pad if no chunk_length_s
			lowercase__				:			Dict            =		None
			with self.assertRaisesRegex(
			    lowerCamelCase_,				'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',				):
					lowercase__				:			List[Any]            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values

			lowercase__				:			Any            =		feature_extractor(input_audio[0],				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(1, 1, 93680) )

			# no pad if no overlap
			lowercase__				:			str            =		2
			lowercase__				:			Tuple            =		None
			with self.assertRaisesRegex(
			    lowerCamelCase_,				'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',				):
					lowercase__				:			Optional[Any]            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values

			lowercase__				:			List[str]            =		feature_extractor(input_audio[0],				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(1, 1, 93680) )


