



import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any    ):
				"""simple docstring"""







				if model.config.model_type == "gpt2":
								return model.transformer.h[0].mlp.c_fc
				return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
						import torch
						import torch.nn as nn






						class    SCREAMING_SNAKE_CASE			(							nn.Module					):





													def __init__(   self		:		int      ,				__lowercase		:		nn.Module      ,				__lowercase		:		int	):

																	'''simple docstring'''
																	super().__init__()
																	__a       =		module
																	__a       =		nn.Sequential(
																	    nn.Linear(module.in_features      ,				__lowercase      ,				bias=__lowercase	)      ,				nn.Linear(__lowercase      ,				module.out_features      ,				bias=__lowercase	)      ,				)
																	__a       =		(2.0 / (5 * min(module.in_features      ,				module.out_features	))) ** 0.5
																	nn.init.normal_(self.adapter[0].weight      ,				std=__lowercase	)
																	nn.init.zeros_(self.adapter[1].weight	)
																	self.adapter.to(module.weight.device	)







													def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		int      ,				*__lowercase		:		Tuple      ,				**__lowercase		:		str	):

																	'''simple docstring'''
																	return self.module(__lowercase      ,				*__lowercase      ,				**__lowercase	) + self.adapter(__lowercase	)






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):
							# We keep the constants inside the init function and model loading inside setUp function

							# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
							# Therefore here we use only bloom-1b3 to test our module
							__lowerCamelCase	:	Tuple    ='bigscience/bloom-1b7'

							# Constant values
							__lowerCamelCase	:	Tuple    =2.109_6595_5269_2574

							__lowerCamelCase	:	int    ='Hello my name is'
							__lowerCamelCase	:	Optional[Any]    =set()
							EXPECTED_OUTPUTS.add('Hello my name is John and I am a professional photographer. I'					)
							EXPECTED_OUTPUTS.add('Hello my name is John.\nI am a friend of your father.\n'					)
							EXPECTED_OUTPUTS.add('Hello my name is John Doe, I am a student at the University'					)
							__lowerCamelCase	:	int    =10





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		AutoTokenizer.from_pretrained(self.model_name	)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											super().setUp()

											# Models and tokenizer
											__a       =		AutoModelForCausalLM.from_pretrained(
											    self.model_name      ,				torch_dtype=torch.floataa      ,				device_map="""auto"""	)
											__a       =		AutoModelForCausalLM.from_pretrained(self.model_name      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											del self.model_fpaa
											del self.model_abit

											gc.collect()
											torch.cuda.empty_cache()





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		self.model_abit.config

											self.assertTrue(hasattr(__lowercase      ,				"""quantization_config"""	)	)

											__a       =		config.to_dict()
											__a       =		config.to_diff_dict()

											__a       =		config.to_json_string()





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											from bitsandbytes.nn import Paramsabit

											__a       =		self.model_fpaa.get_memory_footprint()
											__a       =		self.model_abit.get_memory_footprint()

											self.assertAlmostEqual(mem_fpaa / mem_abit      ,				self.EXPECTED_RELATIVE_DIFFERENCE	)
											__a       =		get_some_linear_layer(self.model_abit	)
											self.assertTrue(linear.weight.__class__ == Paramsabit	)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											from transformers import TaPreTrainedModel

											self.model_fpaa.get_memory_footprint()
											self.model_abit.get_memory_footprint()

											for name, module in self.model_abit.named_modules():
															if isinstance(__lowercase      ,				torch.nn.Linear	):
																			if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																							# 4-bit parameters are packed in uint8 variables
																							self.assertTrue(module.weight.dtype == torch.uinta	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		self.tokenizer(self.input_text      ,				return_tensors="""pt"""	)
											__a       =		self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0	)      ,				max_new_tokens=10	)

											self.assertIn(self.tokenizer.decode(output_sequences[0]      ,				skip_special_tokens=__lowercase	)      ,				self.EXPECTED_OUTPUTS	)





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		BitsAndBytesConfig()
											__a       =		True

											__a       =		AutoModelForCausalLM.from_pretrained(
											    self.model_name      ,				quantization_config=__lowercase      ,				device_map="""auto"""	)

											__a       =		self.tokenizer(self.input_text      ,				return_tensors="""pt"""	)
											__a       =		model_abit_from_config.generate(
											    input_ids=encoded_input["""input_ids"""].to(0	)      ,				max_new_tokens=10	)

											self.assertIn(self.tokenizer.decode(output_sequences[0]      ,				skip_special_tokens=__lowercase	)      ,				self.EXPECTED_OUTPUTS	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											with self.assertRaises(__lowercase	), tempfile.TemporaryDirectory() as tmpdirname:
															self.model_abit.save_pretrained(__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		BitsAndBytesConfig()

											with self.assertRaises(__lowercase	):
															__a       =		AutoModelForCausalLM.from_pretrained(
															    self.model_name      ,				quantization_config=__lowercase      ,				load_in_abit=__lowercase      ,				device_map="""auto"""      ,				bnb_abit_quant_type="""nf4"""      ,				)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											with self.assertRaises(__lowercase	):
															# Tries with `str`
															self.model_abit.to("""cpu"""	)

											with self.assertRaises(__lowercase	):
															# Tries with a `dtype``
															self.model_abit.to(torch.floataa	)

											with self.assertRaises(__lowercase	):
															# Tries with a `device`
															self.model_abit.to(torch.device("""cuda:0"""	)	)

											with self.assertRaises(__lowercase	):
															# Tries with a `device`
															self.model_abit.float()

											with self.assertRaises(__lowercase	):
															# Tries with a `device`
															self.model_abit.half()

											# Test if we did not break anything
											__a       =		self.tokenizer(self.input_text      ,				return_tensors="""pt"""	)

											__a       =		self.model_fpaa.to(torch.floataa	)
											__a       =		self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0	)      ,				max_new_tokens=10	)

											# Check this does not throw an error
											__a       =		self.model_fpaa.to("""cpu"""	)

											# Check this does not throw an error
											__a       =		self.model_fpaa.half()

											# Check this does not throw an error
											__a       =		self.model_fpaa.float()







							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)
											self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa	)






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							@classmethod
							def 						UpperCamelCase_					(   cls		:		List[str]	):

											'''simple docstring'''
											__a       =		"""t5-small"""
											__a       =		"""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
											__a       =		AutoTokenizer.from_pretrained(cls.model_name	)
											__a       =		"""Translate in German: Hello, my dog is cute"""





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											gc.collect()
											torch.cuda.empty_cache()





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											from transformers import TaForConditionalGeneration

											__a       =		TaForConditionalGeneration._keep_in_fpaa_modules
											__a       =		None

											# test with `t5-small`
											__a       =		TaForConditionalGeneration.from_pretrained(self.model_name      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)
											__a       =		self.tokenizer(self.input_text      ,				return_tensors="""pt"""	).to(0	)
											__a       =		model.generate(**__lowercase	)

											# test with `flan-t5-small`
											__a       =		TaForConditionalGeneration.from_pretrained(
											    self.dense_act_model_name      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)
											__a       =		self.tokenizer(self.input_text      ,				return_tensors="""pt"""	).to(0	)
											__a       =		model.generate(**__lowercase	)
											__a       =		modules







							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											import bitsandbytes as bnb

											from transformers import TaForConditionalGeneration

											# test with `t5-small`
											__a       =		TaForConditionalGeneration.from_pretrained(self.model_name      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)

											# there was a bug with decoders - this test checks that it is fixed
											self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q      ,				bnb.nn.Linearabit	)	)

											__a       =		self.tokenizer(self.input_text      ,				return_tensors="""pt"""	).to(0	)
											__a       =		model.generate(**__lowercase	)

											# test with `flan-t5-small`
											__a       =		TaForConditionalGeneration.from_pretrained(
											    self.dense_act_model_name      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)
											__a       =		self.tokenizer(self.input_text      ,				return_tensors="""pt"""	).to(0	)
											__a       =		model.generate(**__lowercase	)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											super().setUp()
											# model_name
											__a       =		"""bigscience/bloom-560m"""
											__a       =		"""t5-small"""

											# Different types of model

											__a       =		AutoModel.from_pretrained(self.model_name      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)
											# Sequence classification model
											__a       =		AutoModelForSequenceClassification.from_pretrained(
											    self.model_name      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)
											# CausalLM model
											__a       =		AutoModelForCausalLM.from_pretrained(self.model_name      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)
											# Seq2seq model
											__a       =		AutoModelForSeqaSeqLM.from_pretrained(
											    self.seq_to_seq_name      ,				load_in_abit=__lowercase      ,				device_map="""auto"""	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											del self.base_model
											del self.sequence_model
											del self.model_abit
											del self.seq_to_seq_model

											gc.collect()
											torch.cuda.empty_cache()







							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											from bitsandbytes.nn import Paramsabit

											self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit	)

											# Other heads should be nn.Parameter
											self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter	)
											self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter	)
											self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter	)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											super().setUp()





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											del self.pipe

											gc.collect()
											torch.cuda.empty_cache()







							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		pipeline(
											    """text-generation"""      ,				model=self.model_name      ,				model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}      ,				max_new_tokens=self.MAX_NEW_TOKENS      ,				)

											# Real second forward pass
											__a       =		self.pipe(self.input_text	)
											self.assertIn(pipeline_output[0]["""generated_text"""]      ,				self.EXPECTED_OUTPUTS	)






@require_torch_multi_gpu
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											super().setUp()







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		AutoModelForCausalLM.from_pretrained(
											    self.model_name      ,				load_in_abit=__lowercase      ,				device_map="""balanced"""	)

											# Check correct device map
											self.assertEqual(set(model_parallel.hf_device_map.values()	)      ,				{0, 1}	)

											# Check that inference pass works on the model
											__a       =		self.tokenizer(self.input_text      ,				return_tensors="""pt"""	)

											# Second real batch
											__a       =		model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0	)      ,				max_new_tokens=10	)
											self.assertIn(self.tokenizer.decode(output_parallel[0]      ,				skip_special_tokens=__lowercase	)      ,				self.EXPECTED_OUTPUTS	)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		"""facebook/opt-350m"""
											super().setUp()







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											if version.parse(importlib.metadata.version("""bitsandbytes"""	)	) < version.parse("""0.37.0"""	):
															return

											# Step 1: freeze all parameters
											__a       =		AutoModelForCausalLM.from_pretrained(self.model_name      ,				load_in_abit=__lowercase	)

											self.assertEqual(set(model.hf_device_map.values()	)      ,				{torch.cuda.current_device()}	)

											for param in model.parameters():
															__a       =		False  # freeze the model - train adapters later
															if param.ndim == 1:
																			# cast the small parameters (e.g. layernorm) to fp32 for stability
																			__a       =		param.data.to(torch.floataa	)

        # Step 2: add adapters
											for _, module in model.named_modules():
															if "OPTAttention" in repr(type(__lowercase	)	):
																			__a       =		LoRALayer(module.q_proj      ,				rank=16	)
																			__a       =		LoRALayer(module.k_proj      ,				rank=16	)
																			__a       =		LoRALayer(module.v_proj      ,				rank=16	)

        # Step 3: dummy batch
											__a       =		self.tokenizer("""Test batch """      ,				return_tensors="""pt"""	).to(0	)

											# Step 4: Check if the gradient is not None
											with torch.cuda.amp.autocast():
															__a       =		model.forward(**__lowercase	)
															out.logits.norm().backward()

											for module in model.modules():
															if isinstance(__lowercase      ,				__lowercase	):
																			self.assertTrue(module.adapter[1].weight.grad is not None	)
																			self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0	)
															elif isinstance(__lowercase      ,				nn.Embedding	):
																			self.assertTrue(module.weight.grad is None	)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Optional[Any]    ='gpt2-xl'
							__lowerCamelCase	:	Dict    =3.3191_8548_5415_2187





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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			int=1.0				,     _SCREAMING_SNAKE_CASE       :			Any=None				,     _SCREAMING_SNAKE_CASE       :			Any=None    ):
				"""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






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def __init__(   self		:		Dict      ,				__lowercase		:		Tuple      ,				__lowercase		:		Tuple=7      ,				__lowercase		:		Optional[int]=400      ,				__lowercase		:		int=2000      ,				__lowercase		:		List[Any]=2048      ,				__lowercase		:		List[str]=128      ,				__lowercase		:		Union[str, Any]=1      ,				__lowercase		:		str=512      ,				__lowercase		:		List[str]=30      ,				__lowercase		:		Tuple=44100      ,				):

											'''simple docstring'''
											__a       =		parent
											__a       =		batch_size
											__a       =		min_seq_length
											__a       =		max_seq_length
											__a       =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											__a       =		spectrogram_length
											__a       =		feature_size
											__a       =		num_audio_channels
											__a       =		hop_length
											__a       =		chunk_length
											__a       =		sampling_rate





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''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 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Optional[int]=False      ,				__lowercase		:		Any=False	):

											'''simple docstring'''
											def _flatten(__lowercase		:		Tuple	):
															return list(itertools.chain(*__lowercase	)	)

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






@require_torch
@require_torchaudio
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Dict    =TvltFeatureExtractor





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		TvltFeatureExtractionTester(self	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		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 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

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

											__a       =		feat_extract_first.to_dict()
											__a       =		feat_extract_second.to_dict()
											__a       =		dict_first.pop("""mel_filters"""	)
											__a       =		dict_second.pop("""mel_filters"""	)
											self.assertTrue(np.allclose(__lowercase      ,				__lowercase	)	)
											self.assertEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
															__a       =		os.path.join(__lowercase      ,				"""feat_extract.json"""	)
															feat_extract_first.to_json_file(__lowercase	)
															__a       =		self.feature_extraction_class.from_json_file(__lowercase	)

											__a       =		feat_extract_first.to_dict()
											__a       =		feat_extract_second.to_dict()
											__a       =		dict_first.pop("""mel_filters"""	)
											__a       =		dict_second.pop("""mel_filters"""	)
											self.assertTrue(np.allclose(__lowercase      ,				__lowercase	)	)
											self.assertEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											# Initialize feature_extractor
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

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

											# Test not batched input
											__a       =		feature_extractor(np_speech_inputs[0]      ,				return_tensors="""np"""      ,				sampling_rate=44100	).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
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100	).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
											__a       =		feature_extractor(
											    __lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100      ,				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.
											__a       =		[floats_list((1, x)	)[0] for x in (800, 800, 800)]
											__a       =		np.asarray(__lowercase	)
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100	).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 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		str	):

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

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







							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self._load_datasamples(1	)
											__a       =		TvltFeatureExtractor()
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""pt"""	).audio_values

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

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