


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


lowerCAmelCase__  :int             =							logging.getLogger(__name__)

lowerCAmelCase__  :str             =							5_0  # max width of layer names
lowerCAmelCase__  :List[Any]             =							7_0  # max width of quantizer names

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		int:


       '''simple docstring'''





       _UpperCAmelCase					=      parser.add_argument_group('quant_trainer arguments'	)
       group.add_argument('--wprec'      ,  type=_A      ,  default=8      ,  help='weight precision'	)
       group.add_argument('--aprec'      ,  type=_A      ,  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=_A      ,  nargs='+'      ,  help='disable quantizers by keyword'	)
       group.add_argument('--quant-disable-layer-module'      ,  type=_A      ,  help='disable quantizers by keyword under layer.'	)
       group.add_argument('--quant-enable-layer-module'      ,  type=_A      ,  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=_A      ,  type=_A      ,  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=_A      ,  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__			(      a__:		Tuple	)  ->		Tuple:


       '''simple docstring'''





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

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

def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		Dict      ,  a__:		Dict=False      ,  a__:		Union[str, Any]=False	)  ->		Any:


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

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

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

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

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

              if args.recalibrate_weights:
                     recalibrate_weights(_A	)

              if args.fuse_qkv:
                     fuse_qkv(_A      ,  _A	)

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

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

def 	lowerCAmelCase__			(      a__:		Tuple	)  ->		Optional[int]:


       '''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__			(      a__:		List[str]      ,  a__:		Any	)  ->		Optional[int]:


       '''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(_A	)

def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		int	)  ->		str:


       '''simple docstring'''





       def fusea(a__:		Any      ,  a__:		Any      ,  a__:		str	):
              for mod in [qq, qk, qv]:
                     if not hasattr(_A      ,  '_amax'	):
                            print('          WARNING: NO AMAX BUFFER'	)
                            return
              _UpperCAmelCase					=      qq._amax.detach().item()
              _UpperCAmelCase					=      qk._amax.detach().item()
              _UpperCAmelCase					=      qv._amax.detach().item()

              _UpperCAmelCase					=      max(_A      ,  _A      ,  _A	)
              qq._amax.fill_(_A	)
              qk._amax.fill_(_A	)
              qv._amax.fill_(_A	)
              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__			(      a__:		Optional[Any]      ,  a__:		Dict	)  ->		int:


       '''simple docstring'''





       for name, mod in model.named_modules():
              if name.endswith('.output.dense'	) and not name.endswith('attention.output.dense'	):
                     _UpperCAmelCase					=      mod._input_quantizer._amax.data.detach().item()
                     mod._input_quantizer._amax.data.detach().clamp_(max=_A	)
                     _UpperCAmelCase					=      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__			(      a__:		int	)  ->		str:


       '''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		Dict	)  ->		Tuple:


       '''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		List[str]=2_5      ,  a__:		List[Any]=1_8_0      ,  a__:		Union[str, Any]=None	)  ->		Dict:


       '''simple docstring'''





       if ignore is None:
              _UpperCAmelCase					=      []
       elif not isinstance(_A      ,  _A	):
              _UpperCAmelCase					=      [ignore]

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

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

def 	lowerCAmelCase__			(      a__:		List[Any]	)  ->		str:


       '''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		List[str]      ,  a__:		Dict      ,  a__:		Any      ,  a__:		str	)  ->		str:


       '''simple docstring'''





       _UpperCAmelCase					=      getattr(_A      ,  _A      ,  _A	)
       if quantizer_mod is not None:
              assert hasattr(_A      ,  _A	)
              setattr(_A      ,  _A      ,  _A	)
       else:
              logger.warning(F'''{name} has no {quantizer}'''	)

def 	lowerCAmelCase__			(      a__:		Union[str, Any]      ,  a__:		Optional[Any]      ,  a__:		Optional[int]="both"      ,  **a__:		Optional[int]	)  ->		str:


       '''simple docstring'''





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







def 	lowerCAmelCase__			(      a__:		List[str]      ,  a__:		List[str]      ,  **a__:		Tuple	)  ->		Optional[Any]:


       '''simple docstring'''





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




from typing import TYPE_CHECKING

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


lowerCAmelCase__  :Tuple             =							{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							['''BeitFeatureExtractor''']
		lowerCAmelCase__  :Optional[Any]             =							['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :int             =							[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Tuple             =							[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__  :Dict             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
