


import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ASTForAudioClassification, ASTModel
						from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
						    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_torchaudio_available():
						import torchaudio

						from transformers import ASTFeatureExtractor







class        snake_case_   :
					def __init__(      self   :int			,__snake_case   :List[str]			,__snake_case   :Dict=13			,__snake_case   :Optional[Any]=2			,__snake_case   :str=24			,__snake_case   :int=16			,__snake_case   :Dict=True			,__snake_case   :List[Any]=True			,__snake_case   :Any=32			,__snake_case   :Any=5			,__snake_case   :Any=4			,__snake_case   :Optional[Any]=37			,__snake_case   :Any="gelu"			,__snake_case   :Union[str, Any]=0.1			,__snake_case   :Any=0.1			,__snake_case   :Tuple=10			,__snake_case   :Tuple=0.02			,__snake_case   :List[str]=None			,__snake_case   :Optional[int]=2			,__snake_case   :Union[str, Any]=2			,)					->    Tuple:
												a__             =     parent
												a__             =     batch_size
												a__             =     patch_size
												a__             =     max_length
												a__             =     num_mel_bins
												a__             =     is_training
												a__             =     use_labels
												a__             =     hidden_size
												a__             =     num_hidden_layers
												a__             =     num_attention_heads
												a__             =     intermediate_size
												a__             =     hidden_act
												a__             =     hidden_dropout_prob
												a__             =     attention_probs_dropout_prob
												a__             =     type_sequence_label_size
												a__             =     initializer_range
												a__             =     scope
												a__             =     frequency_stride
												a__             =     time_stride

												# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
												a__             =     (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
												a__             =     (self.max_length - self.patch_size) // self.time_stride + 1
												a__             =     frequency_out_dimension * time_out_dimension
												a__             =     num_patches + 2
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Optional[int]:
												a__             =     floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]      )

												a__             =     None
												if self.use_labels:
																			a__             =     ids_tensor([self.batch_size]			,self.type_sequence_label_size      )

												a__             =     self.get_config()

												return config, input_values, labels
					def  lowerCamelCase__(      self   :List[str]      )					->    Dict:
												return ASTConfig(
												    patch_size=self.patch_size			,max_length=self.max_length			,num_mel_bins=self.num_mel_bins			,hidden_size=self.hidden_size			,num_hidden_layers=self.num_hidden_layers			,num_attention_heads=self.num_attention_heads			,intermediate_size=self.intermediate_size			,hidden_act=self.hidden_act			,hidden_dropout_prob=self.hidden_dropout_prob			,attention_probs_dropout_prob=self.attention_probs_dropout_prob			,is_decoder=__snake_case			,initializer_range=self.initializer_range			,frequency_stride=self.frequency_stride			,time_stride=self.time_stride			,)
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :Union[str, Any]			,__snake_case   :List[Any]			,__snake_case   :str      )					->    List[str]:
												a__             =     ASTModel(config=__snake_case      )
												model.to(__snake_case      )
												model.eval()
												a__             =     model(__snake_case      )
												self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.seq_length, self.hidden_size)      )



					def  lowerCamelCase__(      self   :List[Any]      )					->    int:
												a__             =     self.prepare_config_and_inputs()
												(
												    (
												    a__
												)      ,					(
												    a__
												)      ,					(
												    a__
												)      ,					
												)             =     config_and_inputs
												a__             =     {'input_values': input_values}
												return config, inputs_dict







@require_torch
class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Any          =			(
					    (
					        ASTModel,
					        ASTForAudioClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase__					:    Any          =			(
					    {'''audio-classification''': ASTForAudioClassification, '''feature-extraction''': ASTModel}
					    if is_torch_available()
					    else {}
					)
					UpperCAmelCase__					:    Dict          =			False
					UpperCAmelCase__					:    Dict          =			False
					UpperCAmelCase__					:    Union[str, Any]          =			False
					UpperCAmelCase__					:    List[Any]          =			False
					def  lowerCamelCase__(      self   :int			,__snake_case   :Dict			,__snake_case   :Any			,__snake_case   :Optional[Any]			,__snake_case   :int			,__snake_case   :Dict      )					->    Dict:
												if pipeline_test_casse_name == "AudioClassificationPipelineTests":
																			return True

												return False
					def  lowerCamelCase__(      self   :int      )					->    Union[str, Any]:
												a__             =     ASTModelTester(self      )
												a__             =     ConfigTester(self			,config_class=__snake_case			,has_text_modality=__snake_case			,hidden_size=37      )
					def  lowerCamelCase__(      self   :str      )					->    Optional[Any]:
												self.config_tester.run_common_tests()
					@unittest.skip(reason='AST does not use inputs_embeds'      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Tuple:
												pass
					def  lowerCamelCase__(      self   :Dict      )					->    Dict:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__             =     model_class(__snake_case      )
																			self.assertIsInstance(model.get_input_embeddings()			,(nn.Module)      )
																			a__             =     model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__snake_case			,nn.Linear      )      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[Any]:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__             =     model_class(__snake_case      )
																			a__             =     inspect.signature(model.forward      )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			a__             =     [*signature.parameters.keys()]

																			a__             =     ['input_values']
																			self.assertListEqual(arg_names[:1]			,__snake_case      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Union[str, Any]:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__snake_case      )



					@slow
					def  lowerCamelCase__(      self   :Any      )					->    Optional[Any]:
												for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__             =     ASTModel.from_pretrained(__snake_case      )
																			self.assertIsNotNone(__snake_case      )






def 							__lowercase   ( ):
							a__             =     hf_hub_download(
							    repo_id='nielsr/audio-spectogram-transformer-checkpoint'					,    filename='sample_audio.flac'					,    repo_type='dataset'   )

							a__      ,					a__             =     torchaudio.load(__lowerCAmelCase   )

							return audio, sampling_rate







@require_torch
@require_torchaudio
class        snake_case_   (unittest.TestCase      ):
					@cached_property
					def  lowerCamelCase__(      self   :Dict      )					->    List[str]:
												return (
												    ASTFeatureExtractor.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593'      )
												    if is_torchaudio_available()
												    else None
												)



					@slow
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Dict:
												a__             =     self.default_feature_extractor
												a__             =     ASTForAudioClassification.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593'      ).to(__snake_case      )

												a__             =     self.default_feature_extractor
												a__      ,					a__             =     prepare_audio()
												a__             =     audio.squeeze().numpy()
												a__             =     feature_extractor(__snake_case			,sampling_rate=__snake_case			,return_tensors='pt'      ).to(__snake_case      )

												# forward pass
												with torch.no_grad():
																			a__             =     model(**__snake_case      )

												# verify the logits
												a__             =     torch.Size((1, 5_27)      )
												self.assertEqual(outputs.logits.shape			,__snake_case      )

												a__             =     torch.tensor([-0.87_60, -7.00_42, -8.66_02]      ).to(__snake_case      )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]			,__snake_case			,atol=1E-4      )      )








import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


snake_case							:       List[Any]     			=      logging.getLogger(__name__)

snake_case							:       Optional[int]     			=      50  # max width of layer names
snake_case							:       Any     			=      70  # max width of quantizer names






def 							__lowercase   ( __lowerCAmelCase							:				Tuple   ):
							a__             =     parser.add_argument_group('quant_trainer arguments'   )
							group.add_argument('--wprec'					,    type=__lowerCAmelCase					,    default=8					,    help='weight precision'   )
							group.add_argument('--aprec'					,    type=__lowerCAmelCase					,    default=8					,    help='activation precision'   )
							group.add_argument('--quant-per-tensor'					,    action='store_true'					,    help='per tensor weight scaling'   )
							group.add_argument('--quant-disable'					,    action='store_true'					,    help='disable all quantizers'   )
							group.add_argument('--quant-disable-embeddings'					,    action='store_true'					,    help='disable all embeddings quantizers'   )
							group.add_argument('--quant-disable-keyword'					,    type=__lowerCAmelCase					,    nargs='+'					,    help='disable quantizers by keyword'   )
							group.add_argument('--quant-disable-layer-module'					,    type=__lowerCAmelCase					,    help='disable quantizers by keyword under layer.'   )
							group.add_argument('--quant-enable-layer-module'					,    type=__lowerCAmelCase					,    help='enable quantizers by keyword under layer'   )
							group.add_argument('--calibrator'					,    default='max'					,    help='which quantization range calibrator to use'   )
							group.add_argument('--percentile'					,    default=__lowerCAmelCase					,    type=__lowerCAmelCase					,    help='percentile for PercentileCalibrator'   )
							group.add_argument('--fuse-qkv'					,    action='store_true'					,    help='use the same scale factor for qkv'   )
							group.add_argument('--clip-gelu'					,    metavar='N'					,    type=__lowerCAmelCase					,    help='clip gelu output maximum value to N'   )
							group.add_argument(
							    '--recalibrate-weights'					,    action='store_true'					,    help=(
							        'recalibrate weight amaxes by taking the max of the weights.'
							        ' amaxes will be computed with the current quantization granularity (axis).'
							    )					,    )






def 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]   ):

							if args.calibrator == "max":
														a__             =     'max'
							elif args.calibrator == "percentile":
														if args.percentile is None:
																					raise ValueError('Specify --percentile when using percentile calibrator'   )
														a__             =     'histogram'
							elif args.calibrator == "mse":
														a__             =     'histogram'
							else:
														raise ValueError(F'Invalid calibrator {args.calibrator}'   )

							a__             =     QuantDescriptor(num_bits=args.aprec					,    calib_method=__lowerCAmelCase   )
							a__             =     QuantDescriptor(num_bits=args.wprec					,    axis=(None if args.quant_per_tensor else (0,))   )
							quant_nn.QuantLinear.set_default_quant_desc_input(__lowerCAmelCase   )
							quant_nn.QuantLinear.set_default_quant_desc_weight(__lowerCAmelCase   )






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Union[str, Any]=False					,    __lowerCAmelCase							:				Dict=False   ):
							logger.info('Configuring Model for Quantization'   )
							logger.info(F'using quantization package {pytorch_quantization.__file__}'   )

							if not calib:
														if args.quant_disable_embeddings:
																					set_quantizer_by_name(__lowerCAmelCase					,    ['embeddings']					,    which='weight'					,    _disabled=__lowerCAmelCase   )

														if args.quant_disable:
																					set_quantizer_by_name(__lowerCAmelCase					,    ['']					,    _disabled=__lowerCAmelCase   )

														if args.quant_disable_keyword:
																					set_quantizer_by_name(__lowerCAmelCase					,    args.quant_disable_keyword					,    _disabled=__lowerCAmelCase   )

														if args.quant_disable_layer_module:
																					set_quantizer_by_name(__lowerCAmelCase					,    [R'layer.\d+.' + args.quant_disable_layer_module]					,    _disabled=__lowerCAmelCase   )

														if args.quant_enable_layer_module:
																					set_quantizer_by_name(__lowerCAmelCase					,    [R'layer.\d+.' + args.quant_enable_layer_module]					,    _disabled=__lowerCAmelCase   )

														if args.recalibrate_weights:
																					recalibrate_weights(__lowerCAmelCase   )

														if args.fuse_qkv:
																					fuse_qkv(__lowerCAmelCase					,    __lowerCAmelCase   )

							if args.clip_gelu:
														clip_gelu(__lowerCAmelCase					,    args.clip_gelu   )

							# if args.local_rank in [-1, 0] and not calib:
							print_quant_summary(__lowerCAmelCase   )






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):
							logger.info('Enabling Calibration'   )
							for name, module in model.named_modules():
														if name.endswith('_quantizer'   ):
																					if module._calibrator is not None:
																												module.disable_quant()
																												module.enable_calib()
																					else:
																												module.disable()
																					logger.info(F'{name:80}: {module}'   )






def 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				List[Any]   ):
							logger.info('Loading calibrated amax'   )
							for name, module in model.named_modules():
														if name.endswith('_quantizer'   ):
																					if module._calibrator is not None:
																												if isinstance(module._calibrator					,    calib.MaxCalibrator   ):
																																			module.load_calib_amax()
																												else:
																																			module.load_calib_amax('percentile'					,    percentile=args.percentile   )
																												module.enable_quant()
																												module.disable_calib()
																					else:
																												module.enable()
							model.cuda()
							print_quant_summary(__lowerCAmelCase   )






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				str   ):

							def fusea(__lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				List[str]   ):
														for mod in [qq, qk, qv]:
																					if not hasattr(__lowerCAmelCase					,    '_amax'   ):
																												print('          WARNING: NO AMAX BUFFER'   )
																												return
														a__             =     qq._amax.detach().item()
														a__             =     qk._amax.detach().item()
														a__             =     qv._amax.detach().item()

														a__             =     max(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )
														qq._amax.fill_(__lowerCAmelCase   )
														qk._amax.fill_(__lowerCAmelCase   )
														qv._amax.fill_(__lowerCAmelCase   )
														logger.info(F'          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}'   )

							for name, mod in model.named_modules():
														if name.endswith('.attention.self'   ):
																					logger.info(F'FUSE_QKV: {name:{name_width}}'   )
																					fusea(mod.matmul_q_input_quantizer					,    mod.matmul_k_input_quantizer					,    mod.matmul_v_input_quantizer   )
																					if args.quant_per_tensor:
																												fusea(mod.query._weight_quantizer					,    mod.key._weight_quantizer					,    mod.value._weight_quantizer   )






def 							__lowercase   ( __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				List[Any]   ):

							for name, mod in model.named_modules():
														if name.endswith('.output.dense'   ) and not name.endswith('attention.output.dense'   ):
																					a__             =     mod._input_quantizer._amax.data.detach().item()
																					mod._input_quantizer._amax.data.detach().clamp_(max=__lowerCAmelCase   )
																					a__             =     mod._input_quantizer._amax.data.detach().item()
																					logger.info(F'CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'   )






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]   ):

							for name, mod in model.named_modules():
														if hasattr(__lowerCAmelCase					,    '_weight_quantizer'   ) and mod._weight_quantizer.axis is not None:
																					a__             =     mod.weight.shape[0]
																					a__             =     mod._weight_quantizer._amax.detach()
																					a__             =     torch.ones(__lowerCAmelCase					,    dtype=amax.dtype					,    device=amax.device   ) * amax
																					print(F'expanding {name} {amax} -> {mod._weight_quantizer._amax}'   )






def 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]   ):

							for name, mod in model.named_modules():
														if hasattr(__lowerCAmelCase					,    '_weight_quantizer'   ):
																					if not hasattr(mod.weight_quantizer					,    '_amax'   ):
																												print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'   )
																												continue

																					# determine which axes to reduce across
																					# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
																					a__             =     set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
																					a__             =     set(range(len(mod.weight.size()   )   )   ) - axis_set
																					a__             =     pytorch_quantization.utils.reduce_amax(mod.weight					,    axis=__lowerCAmelCase					,    keepdims=__lowerCAmelCase   ).detach()
																					logger.info(F'RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'   )
																					a__             =     amax






def 							__lowercase   ( __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				Optional[int]=2_5					,    __lowerCAmelCase							:				List[Any]=1_8_0					,    __lowerCAmelCase							:				Tuple=None   ):

							if ignore is None:
														a__             =     []
							elif not isinstance(__lowerCAmelCase					,    __lowerCAmelCase   ):
														a__             =     [ignore]

							a__             =     0
							for name, mod in model.named_modules():
														if not hasattr(__lowerCAmelCase					,    'weight'   ):
																					continue
														a__             =     max(__lowerCAmelCase					,    len(__lowerCAmelCase   )   )

							for name, mod in model.named_modules():
														a__             =     getattr(__lowerCAmelCase					,    '_input_quantizer'					,    __lowerCAmelCase   )
														a__             =     getattr(__lowerCAmelCase					,    '_weight_quantizer'					,    __lowerCAmelCase   )
														if not hasattr(__lowerCAmelCase					,    'weight'   ):
																					continue
														if type(__lowerCAmelCase   ) in ignore:
																					continue
														if [True for s in ignore if type(__lowerCAmelCase   ) is str and s in name]:
																					continue
														a__             =     F'Act:{input_q.extra_repr()}'
														a__             =     F'Wgt:{weight_q.extra_repr()}'
														a__             =     F'{name:{name_width}} {act_str} {wgt_str}'
														if len(__lowerCAmelCase   ) <= line_width:
																					logger.info(__lowerCAmelCase   )
														else:
																					logger.info(F'{name:{name_width}} {act_str}'   )
																					logger.info(F'{"  ":{name_width}} {wgt_str}'   )






def 							__lowercase   ( __lowerCAmelCase							:				Dict   ):
							a__             =     0
							for name, mod in model.named_modules():
														if isinstance(__lowerCAmelCase					,    pytorch_quantization.nn.TensorQuantizer   ):
																					print(F'{name:80} {mod}'   )
																					count += 1
							print(F'{count} TensorQuantizers found in model'   )






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Dict   ):
							a__             =     getattr(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )
							if quantizer_mod is not None:
														assert hasattr(__lowerCAmelCase					,    __lowerCAmelCase   )
														setattr(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )
							else:
														logger.warning(F'{name} has no {quantizer}'   )






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Optional[int]="both"					,    **__lowerCAmelCase							:				str   ):
							a__             =     F'Warning: changing {which} quantizers of {name:{qname_width}}'
							for k, v in kwargs.items():
														s += F' {k}={v}'
														if which in ["input", "both"]:
																					set_quantizer(__lowerCAmelCase					,    __lowerCAmelCase					,    '_input_quantizer'					,    __lowerCAmelCase					,    __lowerCAmelCase   )
														if which in ["weight", "both"]:
																					set_quantizer(__lowerCAmelCase					,    __lowerCAmelCase					,    '_weight_quantizer'					,    __lowerCAmelCase					,    __lowerCAmelCase   )
							logger.info(__lowerCAmelCase   )






def 							__lowercase   ( __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				Tuple					,    **__lowerCAmelCase							:				Optional[Any]   ):

							for name, mod in model.named_modules():
														if hasattr(__lowerCAmelCase					,    '_input_quantizer'   ) or hasattr(__lowerCAmelCase					,    '_weight_quantizer'   ):
																					for n in names:
																												if re.search(__lowerCAmelCase					,    __lowerCAmelCase   ):
																																			set_quantizers(__lowerCAmelCase					,    __lowerCAmelCase					,    **__lowerCAmelCase   )
														elif name.endswith('_quantizer'   ):
																					for n in names:
																												if re.search(__lowerCAmelCase					,    __lowerCAmelCase   ):
																																			a__             =     F'Warning: changing {name:{name_width}}'
																																			for k, v in kwargs.items():
																																										s += F' {k}={v}'
																																										setattr(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )
																																			logger.info(__lowerCAmelCase   )




