





"""simple docstring"""





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


_UpperCamelCase							=      logging.getLogger(__name__)

_UpperCamelCase							=      50  # max width of layer names
_UpperCamelCase							=      70  # max width of quantizer names



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =parser.add_argument_group('''quant_trainer arguments'''   )
							group.add_argument('''--wprec'''    ,						type=SCREAMING_SNAKE_CASE    ,						default=8    ,						help='''weight precision'''   )
							group.add_argument('''--aprec'''    ,						type=SCREAMING_SNAKE_CASE    ,						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=SCREAMING_SNAKE_CASE    ,						nargs='''+'''    ,						help='''disable quantizers by keyword'''   )
							group.add_argument('''--quant-disable-layer-module'''    ,						type=SCREAMING_SNAKE_CASE    ,						help='''disable quantizers by keyword under layer.'''   )
							group.add_argument('''--quant-enable-layer-module'''    ,						type=SCREAMING_SNAKE_CASE    ,						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=SCREAMING_SNAKE_CASE    ,						type=SCREAMING_SNAKE_CASE    ,						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=SCREAMING_SNAKE_CASE    ,						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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







							if args.calibrator == "max":
														__lowerCamelCase      :      List[Any]         ='''max'''
							elif args.calibrator == "percentile":
														if args.percentile is None:
																					raise ValueError('''Specify --percentile when using percentile calibrator'''   )
														__lowerCamelCase      :      List[Any]         ='''histogram'''
							elif args.calibrator == "mse":
														__lowerCamelCase      :      Union[str, Any]         ='''histogram'''
							else:
														raise ValueError(F'Invalid calibrator {args.calibrator}'   )

							__lowerCamelCase      :      List[str]         =QuantDescriptor(num_bits=args.aprec    ,						calib_method=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      str         =QuantDescriptor(num_bits=args.wprec    ,						axis=(None if args.quant_per_tensor else (0,))   )
							quant_nn.QuantLinear.set_default_quant_desc_input(SCREAMING_SNAKE_CASE   )
							quant_nn.QuantLinear.set_default_quant_desc_weight(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							int=False    ,						SCREAMING_SNAKE_CASE       :							int=False   ):


							'''simple docstring'''







							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(SCREAMING_SNAKE_CASE    ,						['''embeddings''']    ,						which='''weight'''    ,						_disabled=SCREAMING_SNAKE_CASE   )

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

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

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

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

														if args.recalibrate_weights:
																					recalibrate_weights(SCREAMING_SNAKE_CASE   )

														if args.fuse_qkv:
																					fuse_qkv(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

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

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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							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(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							def fusea(SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Tuple   ):
														for mod in [qq, qk, qv]:
																					if not hasattr(SCREAMING_SNAKE_CASE    ,						'''_amax'''   ):
																												print('''          WARNING: NO AMAX BUFFER'''   )
																												return
														__lowerCamelCase      :      Union[str, Any]         =qq._amax.detach().item()
														__lowerCamelCase      :      Union[str, Any]         =qk._amax.detach().item()
														__lowerCamelCase      :      List[Any]         =qv._amax.detach().item()

														__lowerCamelCase      :      int         =max(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
														qq._amax.fill_(SCREAMING_SNAKE_CASE   )
														qk._amax.fill_(SCREAMING_SNAKE_CASE   )
														qv._amax.fill_(SCREAMING_SNAKE_CASE   )
														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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







							for name, mod in model.named_modules():
														if hasattr(SCREAMING_SNAKE_CASE    ,						'''_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)
																					__lowerCamelCase      :      int         =set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
																					__lowerCamelCase      :      Optional[int]         =set(range(len(mod.weight.size()   )   )   ) - axis_set
																					__lowerCamelCase      :      List[Any]         =pytorch_quantization.utils.reduce_amax(mod.weight    ,						axis=SCREAMING_SNAKE_CASE    ,						keepdims=SCREAMING_SNAKE_CASE   ).detach()
																					logger.info(F'RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'   )
																					__lowerCamelCase      :      str         =amax



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							List[Any]=25    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]=180    ,						SCREAMING_SNAKE_CASE       :							str=None   ):


							'''simple docstring'''







							if ignore is None:
														__lowerCamelCase      :      Dict         =[]
							elif not isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      int         =[ignore]

							__lowerCamelCase      :      str         =0
							for name, mod in model.named_modules():
														if not hasattr(SCREAMING_SNAKE_CASE    ,						'''weight'''   ):
																					continue
														__lowerCamelCase      :      Optional[Any]         =max(SCREAMING_SNAKE_CASE    ,						len(SCREAMING_SNAKE_CASE   )   )

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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =getattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							if quantizer_mod is not None:
														assert hasattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
														setattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							else:
														logger.warning(F'{name} has no {quantizer}'   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Tuple="both"    ,						**SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      int         =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(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						'''_input_quantizer'''    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
														if which in ["weight", "both"]:
																					set_quantizer(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						'''_weight_quantizer'''    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							logger.info(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						**SCREAMING_SNAKE_CASE       :							Tuple   ):


							'''simple docstring'''







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











"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :List[str]						,    *__lowercase  :int						,    **__lowercase  :Any			):
											warnings.warn(
											    '''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
											    ''' use FlavaImageProcessor instead.'''						,    __lowercase						,    )
											super().__init__(*__lowercase						,    **__lowercase			)




