


import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


snake_case							:       Optional[int]     			=      random.Random()






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				int=1.0					,    __lowerCAmelCase							:				int=None					,    __lowerCAmelCase							:				Tuple=None   ):
							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        snake_case_   (unittest.TestCase      ):
					def __init__(      self   :str			,__snake_case   :Union[str, Any]			,__snake_case   :Optional[int]=7			,__snake_case   :Any=4_00			,__snake_case   :int=20_00			,__snake_case   :str=1			,__snake_case   :List[str]=0.0			,__snake_case   :Optional[int]=1_60_00			,__snake_case   :str=True			,__snake_case   :List[Any]=True			,)					->    Any:
												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__             =     feature_size
												a__             =     padding_value
												a__             =     sampling_rate
												a__             =     return_attention_mask
												a__             =     do_normalize
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    str:
												return {
												    "feature_size": self.feature_size,
												    "padding_value": self.padding_value,
												    "sampling_rate": self.sampling_rate,
												    "return_attention_mask": self.return_attention_mask,
												    "do_normalize": self.do_normalize,
												}



					def  lowerCamelCase__(      self   :Optional[int]			,__snake_case   :Optional[Any]=False			,__snake_case   :Optional[Any]=False      )					->    int:
												def _flatten(__snake_case   :List[Any]      ):
																			return list(itertools.chain(*__snake_case      )      )

												if equal_length:
																			a__             =     floats_list((self.batch_size, self.max_seq_length)      )
												else:
																			# make sure that inputs increase in size
																			a__             =     [
																			    _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:
																			a__             =     [np.asarray(__snake_case      ) for x in speech_inputs]

												return speech_inputs







class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Tuple          =			WavaVecaFeatureExtractor
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												a__             =     WavaVecaFeatureExtractionTester(self      )
					def  lowerCamelCase__(      self   :str			,__snake_case   :int      )					->    Tuple:
												self.assertTrue(np.all(np.mean(__snake_case			,axis=0      ) < 1E-3      )      )
												self.assertTrue(np.all(np.abs(np.var(__snake_case			,axis=0      ) - 1      ) < 1E-3      )      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[int]:
												# Tests that all call wrap to encode_plus and batch_encode_plus
												a__             =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
												# create three inputs of length 800, 1000, and 1200
												a__             =     [floats_list((1, x)      )[0] for x in range(8_00			,14_00			,2_00      )]
												a__             =     [np.asarray(__snake_case      ) for speech_input in speech_inputs]

												# Test not batched input
												a__             =     feat_extract(speech_inputs[0]			,return_tensors='np'      ).input_values
												a__             =     feat_extract(np_speech_inputs[0]			,return_tensors='np'      ).input_values
												self.assertTrue(np.allclose(__snake_case			,__snake_case			,atol=1E-3      )      )

												# Test batched
												a__             =     feat_extract(__snake_case			,return_tensors='np'      ).input_values
												a__             =     feat_extract(__snake_case			,return_tensors='np'      ).input_values
												for enc_seq_a, enc_seq_a in zip(__snake_case			,__snake_case      ):
																			self.assertTrue(np.allclose(__snake_case			,__snake_case			,atol=1E-3      )      )

												# Test 2-D numpy arrays are batched.
												a__             =     [floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
												a__             =     np.asarray(__snake_case      )
												a__             =     feat_extract(__snake_case			,return_tensors='np'      ).input_values
												a__             =     feat_extract(__snake_case			,return_tensors='np'      ).input_values
												for enc_seq_a, enc_seq_a in zip(__snake_case			,__snake_case      ):
																			self.assertTrue(np.allclose(__snake_case			,__snake_case			,atol=1E-3      )      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[int]:
												a__             =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
												a__             =     [floats_list((1, x)      )[0] for x in range(8_00			,14_00			,2_00      )]

												a__             =     ['longest', 'max_length', 'do_not_pad']
												a__             =     [None, 16_00, None]
												for max_length, padding in zip(__snake_case			,__snake_case      ):
																			a__             =     feat_extract(__snake_case			,padding=__snake_case			,max_length=__snake_case			,return_tensors='np'      )
																			a__             =     processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:8_00]      )
																			self.assertTrue(input_values[0][8_00:].sum() < 1E-6      )
																			self._check_zero_mean_unit_variance(input_values[1][:10_00]      )
																			self.assertTrue(input_values[0][10_00:].sum() < 1E-6      )
																			self._check_zero_mean_unit_variance(input_values[2][:12_00]      )
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												a__             =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
												a__             =     range(8_00			,14_00			,2_00      )
												a__             =     [floats_list((1, x)      )[0] for x in lengths]

												a__             =     ['longest', 'max_length', 'do_not_pad']
												a__             =     [None, 16_00, None]

												for max_length, padding in zip(__snake_case			,__snake_case      ):
																			a__             =     feat_extract(__snake_case			,max_length=__snake_case			,padding=__snake_case      )
																			a__             =     processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:8_00]      )
																			self._check_zero_mean_unit_variance(input_values[1][:10_00]      )
																			self._check_zero_mean_unit_variance(input_values[2][:12_00]      )
					def  lowerCamelCase__(      self   :Any      )					->    str:
												a__             =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
												a__             =     [floats_list((1, x)      )[0] for x in range(8_00			,14_00			,2_00      )]
												a__             =     feat_extract(
												    __snake_case			,truncation=__snake_case			,max_length=10_00			,padding='max_length'			,return_tensors='np'      )
												a__             =     processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_00]      )
												self._check_zero_mean_unit_variance(input_values[1]      )
												self._check_zero_mean_unit_variance(input_values[2]      )
					def  lowerCamelCase__(      self   :int      )					->    Tuple:
												a__             =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
												a__             =     [floats_list((1, x)      )[0] for x in range(8_00			,14_00			,2_00      )]
												a__             =     feat_extract(
												    __snake_case			,truncation=__snake_case			,max_length=10_00			,padding='longest'			,return_tensors='np'      )
												a__             =     processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_00]      )
												self._check_zero_mean_unit_variance(input_values[1, :10_00]      )
												self._check_zero_mean_unit_variance(input_values[2]      )

												# make sure that if max_length < longest -> then pad to max_length
												self.assertTrue(input_values.shape == (3, 10_00)      )

												a__             =     [floats_list((1, x)      )[0] for x in range(8_00			,14_00			,2_00      )]
												a__             =     feat_extract(
												    __snake_case			,truncation=__snake_case			,max_length=20_00			,padding='longest'			,return_tensors='np'      )
												a__             =     processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_00]      )
												self._check_zero_mean_unit_variance(input_values[1, :10_00]      )
												self._check_zero_mean_unit_variance(input_values[2]      )

												# make sure that if max_length > longest -> then pad to longest
												self.assertTrue(input_values.shape == (3, 12_00)      )
					@require_torch
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    int:
												import torch

												a__             =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
												a__             =     np.random.rand(1_00      ).astype(np.floataa      )
												a__             =     np_speech_inputs.tolist()

												for inputs in [py_speech_inputs, np_speech_inputs]:
																			a__             =     feature_extractor.pad([{'input_values': inputs}]			,return_tensors='np'      )
																			self.assertTrue(np_processed.input_values.dtype == np.floataa      )
																			a__             =     feature_extractor.pad([{'input_values': inputs}]			,return_tensors='pt'      )
																			self.assertTrue(pt_processed.input_values.dtype == torch.floataa      )



					@slow
					@require_torch
					def  lowerCamelCase__(      self   :Optional[int]      )					->    int:
												# this test makes sure that models that are using
												# group norm don't have their feature extractor return the
												# attention_mask
												for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																			a__             =     WavaVecaConfig.from_pretrained(__snake_case      )
																			a__             =     WavaVecaFeatureExtractor.from_pretrained(__snake_case      )

																			# only "layer" feature extraction norm should make use of
																			# attention_mask
																			self.assertEqual(feat_extract.return_attention_mask			,config.feat_extract_norm == 'layer'      )








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   )




