




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_		:					Union[str, Any]          =  logging.getLogger(__name__)

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




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Dict										=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 		lowerCAmelCase_ (						lowerCamelCase ):

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

				__magic_name__						:						Optional[Any]										=QuantDescriptor(num_bits=args.aprec						,  calib_method=lowerCamelCase )
				__magic_name__						:						int										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=False						,  lowerCamelCase=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 		lowerCAmelCase_ (						lowerCamelCase ):
				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_ (						lowerCamelCase						,  lowerCamelCase ):
				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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):

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

								__magic_name__						:						Optional[int]										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):

				for name, mod in model.named_modules():
								if name.endswith(""".output.dense""" ) and not name.endswith("""attention.output.dense""" ):
												__magic_name__						:						int										=mod._input_quantizer._amax.data.detach().item()
												mod._input_quantizer._amax.data.detach().clamp_(max=lowerCamelCase )
												__magic_name__						:						Optional[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_ (						lowerCamelCase ):

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




def 		lowerCAmelCase_ (						lowerCamelCase ):

				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)
												__magic_name__						:						List[str]										=set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
												__magic_name__						:						List[str]										=set(range(len(mod.weight.size() ) ) ) - axis_set
												__magic_name__						:						List[str]										=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()}" )
												__magic_name__						:						Union[str, Any]										=amax




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=25						,  lowerCamelCase=180						,  lowerCamelCase=None ):

				if ignore is None:
								__magic_name__						:						int										=[]
				elif not isinstance(lowerCamelCase						,  lowerCamelCase ):
								__magic_name__						:						Union[str, Any]										=[ignore]

				__magic_name__						:						Tuple										=0
				for name, mod in model.named_modules():
								if not hasattr(lowerCamelCase						,  """weight""" ):
												continue
								__magic_name__						:						Union[str, Any]										=max(lowerCamelCase						,  len(lowerCamelCase ) )

				for name, mod in model.named_modules():
								__magic_name__						:						int										=getattr(lowerCamelCase						,  """_input_quantizer"""						,  lowerCamelCase )
								__magic_name__						:						Tuple										=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
								__magic_name__						:						List[str]										=F"Act:{input_q.extra_repr()}"
								__magic_name__						:						Dict										=F"Wgt:{weight_q.extra_repr()}"
								__magic_name__						:						Optional[Any]										=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 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						List[str]										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase="both"						,  **lowerCamelCase ):
				__magic_name__						:						str										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  **lowerCamelCase ):

				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 ):
																				__magic_name__						:						Tuple										=F"Warning: changing {name:{name_width}}"
																				for k, v in kwargs.items():
																								s += F" {k}={v}"
																								setattr(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
																				logger.info(lowerCamelCase )






'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase          =						{"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ViTMAEForPreTraining""",
						    """ViTMAELayer""",
						    """ViTMAEModel""",
						    """ViTMAEPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """TFViTMAEForPreTraining""",
						    """TFViTMAEModel""",
						    """TFViTMAEPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						_lowercase          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
