



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 						__UpperCamelCase				(							_lowercase					) ->			Optional[Any]:
	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 		lowerCamelCase__      (						nn.Module     ):




							'''simple docstring'''



							def __init__(  self				:	List[str]     ,  UpperCamelCase_				:	nn.Module     ,  UpperCamelCase_				:	int					)  ->       int:



								'''simple docstring'''





								super().__init__()
								_lowercase					:       Dict       =		module
								_lowercase					:       Optional[Any]       =		nn.Sequential(
								    nn.Linear(module.in_features     ,  UpperCamelCase_     ,  bias=UpperCamelCase_					)     ,  nn.Linear(UpperCamelCase_     ,  module.out_features     ,  bias=UpperCamelCase_					)     ,  )
								_lowercase					:       Union[str, Any]       =		(2.0 / (5 * min(module.in_features     ,  module.out_features					))) ** 0.5
								nn.init.normal_(self.adapter[0].weight     ,  std=UpperCamelCase_					)
								nn.init.zeros_(self.adapter[1].weight					)
								self.adapter.to(module.weight.device					)




							def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	List[Any]     ,  *UpperCamelCase_				:	Dict     ,  **UpperCamelCase_				:	Optional[int]					)  ->       List[str]:



								'''simple docstring'''





								return self.module(UpperCamelCase_     ,  *UpperCamelCase_     ,  **UpperCamelCase_					) + self.adapter(UpperCamelCase_					)



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




					'''simple docstring'''



					A_						    =						"""bigscience/bloom-1b7"""

					# Constant values
					A_						    =						2.1_09_65_95_52_69_25_74

					A_						    =						"""Hello my name is"""
					A_						    =						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"""     )
					A_						    =						10




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		AutoTokenizer.from_pretrained(self.model_name					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Optional[int]:



						'''simple docstring'''





						super().setUp()

						# Models and tokenizer
						_lowercase					:       Union[str, Any]       =		AutoModelForCausalLM.from_pretrained(
						    self.model_name     ,  torch_dtype=torch.floataa     ,  device_map='auto'					)
						_lowercase					:       int       =		AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Union[str, Any]:



						'''simple docstring'''





						del self.model_fpaa
						del self.model_abit

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




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Any       =		self.model_abit.config

						self.assertTrue(hasattr(UpperCamelCase_     ,  'quantization_config'					)					)

						_lowercase					:       int       =		config.to_dict()
						_lowercase					:       Union[str, Any]       =		config.to_diff_dict()

						_lowercase					:       Optional[Any]       =		config.to_json_string()




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Tuple:



						'''simple docstring'''





						from bitsandbytes.nn import Paramsabit

						_lowercase					:       Any       =		self.model_fpaa.get_memory_footprint()
						_lowercase					:       str       =		self.model_abit.get_memory_footprint()

						self.assertAlmostEqual(mem_fpaa / mem_abit     ,  self.EXPECTED_RELATIVE_DIFFERENCE					)
						_lowercase					:       Optional[Any]       =		get_some_linear_layer(self.model_abit					)
						self.assertTrue(linear.weight.__class__ == Paramsabit					)




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Tuple:



						'''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(UpperCamelCase_     ,  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]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					)
						_lowercase					:       List[Any]       =		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=UpperCamelCase_					)     ,  self.EXPECTED_OUTPUTS					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		BitsAndBytesConfig()
						_lowercase					:       Optional[int]       =		True

						_lowercase					:       Union[str, Any]       =		AutoModelForCausalLM.from_pretrained(
						    self.model_name     ,  quantization_config=UpperCamelCase_     ,  device_map='auto'					)

						_lowercase					:       str       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					)
						_lowercase					:       Tuple       =		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=UpperCamelCase_					)     ,  self.EXPECTED_OUTPUTS					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       int:



						'''simple docstring'''





						with self.assertRaises(UpperCamelCase_					), tempfile.TemporaryDirectory() as tmpdirname:
							self.model_abit.save_pretrained(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	str					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       int       =		BitsAndBytesConfig()

						with self.assertRaises(UpperCamelCase_					):
							_lowercase					:       int       =		AutoModelForCausalLM.from_pretrained(
							    self.model_name     ,  quantization_config=UpperCamelCase_     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'     ,  bnb_abit_quant_type='nf4'     ,  )




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Optional[Any]:



						'''simple docstring'''





						with self.assertRaises(UpperCamelCase_					):
							# Tries with `str`
							self.model_abit.to('cpu'					)

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

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

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

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

						# Test if we did not break anything
						_lowercase					:       int       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					)

						_lowercase					:       Union[str, Any]       =		self.model_fpaa.to(torch.floataa					)
						_lowercase					:       Tuple       =		self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0					)     ,  max_new_tokens=10					)

						# Check this does not throw an error
						_lowercase					:       Tuple       =		self.model_fpaa.to('cpu'					)

						# Check this does not throw an error
						_lowercase					:       Dict       =		self.model_fpaa.half()

						# Check this does not throw an error
						_lowercase					:       Tuple       =		self.model_fpaa.float()




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       Tuple       =		AutoModelForSeqaSeqLM.from_pretrained('t5-small'     ,  load_in_abit=UpperCamelCase_     ,  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 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Tuple					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		't5-small'
						_lowercase					:       Tuple       =		'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
						_lowercase					:       Optional[int]       =		AutoTokenizer.from_pretrained(cls.model_name					)
						_lowercase					:       Union[str, Any]       =		'Translate in German: Hello, my dog is cute'




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       List[str]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Any:



						'''simple docstring'''





						from transformers import TaForConditionalGeneration

						_lowercase					:       Union[str, Any]       =		TaForConditionalGeneration._keep_in_fpaa_modules
						_lowercase					:       Optional[Any]       =		None

						# test with `t5-small`
						_lowercase					:       List[Any]       =		TaForConditionalGeneration.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						_lowercase					:       Any       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					).to(0					)
						_lowercase					:       Optional[Any]       =		model.generate(**UpperCamelCase_					)

						# test with `flan-t5-small`
						_lowercase					:       Dict       =		TaForConditionalGeneration.from_pretrained(
						    self.dense_act_model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						_lowercase					:       str       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					).to(0					)
						_lowercase					:       int       =		model.generate(**UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		modules




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Tuple:



						'''simple docstring'''





						import bitsandbytes as bnb

						from transformers import TaForConditionalGeneration

						# test with `t5-small`
						_lowercase					:       List[Any]       =		TaForConditionalGeneration.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_     ,  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					)					)

						_lowercase					:       Optional[Any]       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					).to(0					)
						_lowercase					:       List[Any]       =		model.generate(**UpperCamelCase_					)

						# test with `flan-t5-small`
						_lowercase					:       List[str]       =		TaForConditionalGeneration.from_pretrained(
						    self.dense_act_model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						_lowercase					:       Any       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					).to(0					)
						_lowercase					:       List[Any]       =		model.generate(**UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	int					)  ->       int:



						'''simple docstring'''





						super().setUp()
						# model_name
						_lowercase					:       str       =		'bigscience/bloom-560m'
						_lowercase					:       str       =		't5-small'

						# Different types of model

						_lowercase					:       Tuple       =		AutoModel.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						# Sequence classification model
						_lowercase					:       Optional[int]       =		AutoModelForSequenceClassification.from_pretrained(
						    self.model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						# CausalLM model
						_lowercase					:       Tuple       =		AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						# Seq2seq model
						_lowercase					:       List[Any]       =		AutoModelForSeqaSeqLM.from_pretrained(
						    self.seq_to_seq_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Union[str, Any]:



						'''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				:	Dict					)  ->       Any:



						'''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 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Dict:



						'''simple docstring'''





						super().setUp()




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[int]:



						'''simple docstring'''





						del self.pipe

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




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       str       =		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
						_lowercase					:       int       =		self.pipe(self.input_text					)
						self.assertIn(pipeline_output[0]['generated_text']     ,  self.EXPECTED_OUTPUTS					)






@require_torch_multi_gpu
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	int					)  ->       List[Any]:



						'''simple docstring'''





						super().setUp()




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		AutoModelForCausalLM.from_pretrained(
						    self.model_name     ,  load_in_abit=UpperCamelCase_     ,  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
						_lowercase					:       int       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					)

						# Second real batch
						_lowercase					:       str       =		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=UpperCamelCase_					)     ,  self.EXPECTED_OUTPUTS					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		'facebook/opt-350m'
						super().setUp()




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Tuple:



						'''simple docstring'''





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

						# Step 1: freeze all parameters
						_lowercase					:       Optional[int]       =		AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_					)

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

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

        # Step 2: add adapters
						for _, module in model.named_modules():
							if "OPTAttention" in repr(type(UpperCamelCase_					)					):
								_lowercase					:       Optional[Any]       =		LoRALayer(module.q_proj     ,  rank=16					)
								_lowercase					:       Optional[int]       =		LoRALayer(module.k_proj     ,  rank=16					)
								_lowercase					:       Tuple       =		LoRALayer(module.v_proj     ,  rank=16					)

        # Step 3: dummy batch
						_lowercase					:       Dict       =		self.tokenizer('Test batch '     ,  return_tensors='pt'					).to(0					)

						# Step 4: Check if the gradient is not None
						with torch.cuda.amp.autocast():
							_lowercase					:       List[str]       =		model.forward(**UpperCamelCase_					)
							out.logits.norm().backward()

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






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""gpt2-xl"""
					A_						    =						3.31_91_85_48_54_15_21_87





'''simple docstring'''

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_A				:    Optional[int]     						=logging.get_logger(__name__)



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						["""input_features""", """is_longer"""]




					def __init__(  self				:	List[Any]     ,  UpperCamelCase_				:	List[Any]=64     ,  UpperCamelCase_				:	int=4_8000     ,  UpperCamelCase_				:	Union[str, Any]=480     ,  UpperCamelCase_				:	Any=10     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Optional[int]=0.0     ,  UpperCamelCase_				:	Tuple=False     ,  UpperCamelCase_				:	float = 0     ,  UpperCamelCase_				:	float = 1_4000     ,  UpperCamelCase_				:	int = None     ,  UpperCamelCase_				:	str = "fusion"     ,  UpperCamelCase_				:	str = "repeatpad"     ,  **UpperCamelCase_				:	Optional[Any]     ,  )  ->       Dict:



						'''simple docstring'''





						super().__init__(
						    feature_size=UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  padding_value=UpperCamelCase_     ,  return_attention_mask=UpperCamelCase_     ,  **UpperCamelCase_     ,  )
						_lowercase					:       Tuple       =		top_db
						_lowercase					:       Any       =		truncation
						_lowercase					:       str       =		padding
						_lowercase					:       int       =		fft_window_size
						_lowercase					:       Any       =		(fft_window_size >> 1) + 1
						_lowercase					:       int       =		hop_length
						_lowercase					:       Any       =		max_length_s
						_lowercase					:       str       =		max_length_s * sampling_rate
						_lowercase					:       Any       =		sampling_rate
						_lowercase					:       List[Any]       =		frequency_min
						_lowercase					:       Tuple       =		frequency_max
						_lowercase					:       Tuple       =		mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins     ,  num_mel_filters=UpperCamelCase_     ,  min_frequency=UpperCamelCase_     ,  max_frequency=UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  norm=UpperCamelCase_     ,  mel_scale='htk'     ,  )
						_lowercase					:       Any       =		mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins     ,  num_mel_filters=UpperCamelCase_     ,  min_frequency=UpperCamelCase_     ,  max_frequency=UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  norm='slaney'     ,  mel_scale='slaney'     ,  )




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Dict[str, Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		copy.deepcopy(self.__dict__					)
						_lowercase					:       int       =		self.__class__.__name__
						if "mel_filters" in output:
							del output["mel_filters"]
						if "mel_filters_slaney" in output:
							del output["mel_filters_slaney"]
						return output




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	np.array     ,  UpperCamelCase_				:	Optional[np.array] = None					)  ->       np.ndarray:



						'''simple docstring'''





						_lowercase					:       List[str]       =		spectrogram(
						    UpperCamelCase_     ,  window_function(self.fft_window_size     ,  'hann'					)     ,  frame_length=self.fft_window_size     ,  hop_length=self.hop_length     ,  power=2.0     ,  mel_filters=UpperCamelCase_     ,  log_mel='dB'     ,  )
						return log_mel_spectrogram.T




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		np.array_split(list(range(0     ,  total_frames - chunk_frames + 1					)					)     ,  3					)
						if len(ranges[1]					) == 0:
							# if the audio is too short, we just use the first chunk
							_lowercase					:       int       =		[0]
						if len(ranges[2]					) == 0:
							# if the audio is too short, we just use the first chunk
							_lowercase					:       Union[str, Any]       =		[0]
						# randomly choose index for each part
						_lowercase					:       Tuple       =		np.random.choice(ranges[0]					)
						_lowercase					:       int       =		np.random.choice(ranges[1]					)
						_lowercase					:       Any       =		np.random.choice(ranges[2]					)

						_lowercase					:       int       =		mel[idx_front : idx_front + chunk_frames, :]
						_lowercase					:       int       =		mel[idx_middle : idx_middle + chunk_frames, :]
						_lowercase					:       Tuple       =		mel[idx_back : idx_back + chunk_frames, :]

						_lowercase					:       List[Any]       =		torch.tensor(mel[None, None, :]					)
						_lowercase					:       Optional[int]       =		torch.nn.functional.interpolate(
						    UpperCamelCase_     ,  size=[chunk_frames, 64]     ,  mode='bilinear'     ,  align_corners=UpperCamelCase_					)
						_lowercase					:       str       =		mel_shrink[0][0].numpy()
						_lowercase					:       int       =		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,  axis=0					)
						return mel_fusion




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	np.array     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Optional[int]					)  ->       np.array:



						'''simple docstring'''





						if waveform.shape[0] > max_length:
							if truncation == "rand_trunc":
								_lowercase					:       Tuple       =		True
								# random crop to max_length (for compatibility) -> this should be handled by self.pad
								_lowercase					:       Any       =		len(UpperCamelCase_					) - max_length
								_lowercase					:       Dict       =		np.random.randint(0     ,  overflow + 1					)
								_lowercase					:       Optional[int]       =		waveform[idx : idx + max_length]
								_lowercase					:       Dict       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters_slaney					)[None, :]
							elif truncation == "fusion":
								_lowercase					:       List[Any]       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters					)
								_lowercase					:       List[Any]       =		max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
								_lowercase					:       Optional[int]       =		mel.shape[0]
								if chunk_frames == total_frames:
									# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
									# In this case, we just use the whole audio.
									_lowercase					:       Optional[Any]       =		np.stack([mel, mel, mel, mel]     ,  axis=0					)
									_lowercase					:       List[Any]       =		False
								else:
									_lowercase					:       Union[str, Any]       =		self._random_mel_fusion(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
									_lowercase					:       int       =		True
							else:
								raise NotImplementedError(F'''data_truncating {truncation} not implemented'''					)

						else:
							_lowercase					:       Any       =		False
							# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
							if waveform.shape[0] < max_length:
								if padding == "repeat":
									_lowercase					:       List[Any]       =		int(max_length / len(UpperCamelCase_					)					)
									_lowercase					:       List[str]       =		np.stack(np.tile(UpperCamelCase_     ,  n_repeat + 1					)					)[:max_length]
								if padding == "repeatpad":
									_lowercase					:       Union[str, Any]       =		int(max_length / len(UpperCamelCase_					)					)
									_lowercase					:       Union[str, Any]       =		np.stack(np.tile(UpperCamelCase_     ,  UpperCamelCase_					)					)
								_lowercase					:       Dict       =		np.pad(UpperCamelCase_     ,  (0, max_length - waveform.shape[0])     ,  mode='constant'     ,  constant_values=0					)

							if truncation == "fusion":
								_lowercase					:       str       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters					)
								_lowercase					:       Dict       =		np.stack([input_mel, input_mel, input_mel, input_mel]     ,  axis=0					)
							else:
								_lowercase					:       List[Any]       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters_slaney					)[None, :]

						return input_mel, longer




					def __call__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,  UpperCamelCase_				:	str = None     ,  UpperCamelCase_				:	Optional[str] = None     ,  UpperCamelCase_				:	Optional[int] = None     ,  UpperCamelCase_				:	Optional[int] = None     ,  UpperCamelCase_				:	Optional[Union[str, TensorType]] = None     ,  **UpperCamelCase_				:	Dict     ,  )  ->       BatchFeature:



						'''simple docstring'''





						_lowercase					:       Dict       =		truncation if truncation is not None else self.truncation
						_lowercase					:       int       =		padding if padding else self.padding

						if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
								raise ValueError(
								    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
								    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
								    F''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''					)
						else:
							logger.warning(
							    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
							    'Failing to do so can result in silent errors that might be hard to debug.'					)

						_lowercase					:       Optional[Any]       =		isinstance(UpperCamelCase_     ,  np.ndarray					) and len(raw_speech.shape					) > 1
						if is_batched_numpy and len(raw_speech.shape					) > 2:
							raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''					)
						_lowercase					:       List[str]       =		is_batched_numpy or (
						    isinstance(UpperCamelCase_     ,  (list, tuple)					) and (isinstance(raw_speech[0]     ,  (np.ndarray, tuple, list)					))
						)

						if is_batched:
							_lowercase					:       Dict       =		[np.asarray(UpperCamelCase_     ,  dtype=np.floataa					) for speech in raw_speech]
						elif not is_batched and not isinstance(UpperCamelCase_     ,  np.ndarray					):
							_lowercase					:       Any       =		np.asarray(UpperCamelCase_     ,  dtype=np.floataa					)
						elif isinstance(UpperCamelCase_     ,  np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
							_lowercase					:       Tuple       =		raw_speech.astype(np.floataa					)

						# always return batch
						if not is_batched:
							_lowercase					:       int       =		[np.asarray(UpperCamelCase_					)]

						# convert to mel spectrogram, truncate and pad if needed.
						_lowercase					:       Optional[Any]       =		[
						    self._get_input_mel(UpperCamelCase_     ,  max_length if max_length else self.nb_max_samples     ,  UpperCamelCase_     ,  UpperCamelCase_					)
						    for waveform in raw_speech
						]

						_lowercase					:       List[Any]       =		[]
						_lowercase					:       Dict       =		[]
						for mel, longer in padded_inputs:
							input_mel.append(UpperCamelCase_					)
							is_longer.append(UpperCamelCase_					)

						if truncation == "fusion" and sum(UpperCamelCase_					) == 0:
							# if no audio is longer than 10s, then randomly select one audio to be longer
							_lowercase					:       Optional[Any]       =		np.random.randint(0     ,  len(UpperCamelCase_					)					)
							_lowercase					:       str       =		True

						if isinstance(input_mel[0]     ,  UpperCamelCase_					):
							_lowercase					:       str       =		[np.asarray(UpperCamelCase_     ,  dtype=np.floataa					) for feature in input_mel]

						# is_longer is a list of bool
						_lowercase					:       Tuple       =		[[longer] for longer in is_longer]

						_lowercase					:       Optional[Any]       =		{'input_features': input_mel, 'is_longer': is_longer}
						_lowercase					:       Optional[int]       =		BatchFeature(UpperCamelCase_					)

						if return_tensors is not None:
							_lowercase					:       List[Any]       =		input_features.convert_to_tensors(UpperCamelCase_					)

						return input_features
