


'''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


a_   :      Dict				    =      logging.getLogger(__name__)

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






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




     '''simple docstring'''



     _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 		_A			(lowerCAmelCase__					:Tuple						)				->					Any:




     '''simple docstring'''



     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 		_A			(lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:int=False			,  lowerCAmelCase__					:List[str]=False						)				->					Tuple:




     '''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(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 		_A			(lowerCAmelCase__					:List[Any]						)				->					Tuple:




     '''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 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Union[str, Any]						)				->					Union[str, Any]:




     '''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(lowerCAmelCase__						)






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




     '''simple docstring'''



     def fusea(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:Dict						):
          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 		_A			(lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Optional[int]						)				->					Optional[int]:




     '''simple docstring'''



     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 		_A			(lowerCAmelCase__					:int						)				->					Union[str, Any]:




     '''simple docstring'''



     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 		_A			(lowerCAmelCase__					:Tuple						)				->					Union[str, Any]:




     '''simple docstring'''



     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 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:str=25			,  lowerCAmelCase__					:Optional[Any]=1_80			,  lowerCAmelCase__					:List[str]=None						)				->					List[str]:




     '''simple docstring'''



     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 		_A			(lowerCAmelCase__					:Optional[int]						)				->					List[Any]:




     '''simple docstring'''



     _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 		_A			(lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:List[Any]			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:List[Any]						)				->					int:




     '''simple docstring'''



     _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 		_A			(lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:str			,  lowerCAmelCase__					:Union[str, Any]="both"			,  **lowerCAmelCase__					:str						)				->					Dict:




     '''simple docstring'''



     _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 		_A			(lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Dict			,  **lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     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__						)





'''simple docstring'''






import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible




class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							[
          'safety_checker/pytorch_model.bin',
          'safety_checker/model.safetensors',
          'vae/diffusion_pytorch_model.bin',
          'vae/diffusion_pytorch_model.safetensors',
          'text_encoder/pytorch_model.bin',
          'text_encoder/model.safetensors',
          'unet/diffusion_pytorch_model.bin',
          'unet/diffusion_pytorch_model.safetensors',
      ]
      self.assertTrue(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							[
          'unet/diffusion_pytorch_model.bin',
          'unet/diffusion_pytorch_model.safetensors',
      ]
      self.assertTrue(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							[
          'safety_checker/pytorch_model.bin',
          'safety_checker/model.safetensors',
          'vae/diffusion_pytorch_model.bin',
          'vae/diffusion_pytorch_model.safetensors',
          'text_encoder/pytorch_model.bin',
          'text_encoder/model.safetensors',
          'unet/diffusion_pytorch_model.bin',
          # Removed: 'unet/diffusion_pytorch_model.safetensors',
      ]
      self.assertFalse(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							[
          'text_encoder/pytorch_model.bin',
          'text_encoder/model.safetensors',
      ]
      self.assertTrue(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							[
          'safety_checker/pytorch_model.bin',
          'safety_checker/model.safetensors',
          'vae/diffusion_pytorch_model.bin',
          'vae/diffusion_pytorch_model.safetensors',
          'text_encoder/pytorch_model.bin',
          # Removed: 'text_encoder/model.safetensors',
          'unet/diffusion_pytorch_model.bin',
          'unet/diffusion_pytorch_model.safetensors',
      ]
      self.assertFalse(is_safetensors_compatible(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							[
          'safety_checker/pytorch_model.fp16.bin',
          'safety_checker/model.fp16.safetensors',
          'vae/diffusion_pytorch_model.fp16.bin',
          'vae/diffusion_pytorch_model.fp16.safetensors',
          'text_encoder/pytorch_model.fp16.bin',
          'text_encoder/model.fp16.safetensors',
          'unet/diffusion_pytorch_model.fp16.bin',
          'unet/diffusion_pytorch_model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							[
          'unet/diffusion_pytorch_model.fp16.bin',
          'unet/diffusion_pytorch_model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							int:
      # pass variant but use the non-variant filenames
      _a      =							[
          'unet/diffusion_pytorch_model.bin',
          'unet/diffusion_pytorch_model.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							[
          'safety_checker/pytorch_model.fp16.bin',
          'safety_checker/model.fp16.safetensors',
          'vae/diffusion_pytorch_model.fp16.bin',
          'vae/diffusion_pytorch_model.fp16.safetensors',
          'text_encoder/pytorch_model.fp16.bin',
          'text_encoder/model.fp16.safetensors',
          'unet/diffusion_pytorch_model.fp16.bin',
          # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertFalse(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							[
          'text_encoder/pytorch_model.fp16.bin',
          'text_encoder/model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      # pass variant but use the non-variant filenames
      _a      =							[
          'text_encoder/pytorch_model.bin',
          'text_encoder/model.safetensors',
      ]
      _a      =							'fp16'
      self.assertTrue(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )




 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							[
          'safety_checker/pytorch_model.fp16.bin',
          'safety_checker/model.fp16.safetensors',
          'vae/diffusion_pytorch_model.fp16.bin',
          'vae/diffusion_pytorch_model.fp16.safetensors',
          'text_encoder/pytorch_model.fp16.bin',
          # 'text_encoder/model.fp16.safetensors',
          'unet/diffusion_pytorch_model.fp16.bin',
          'unet/diffusion_pytorch_model.fp16.safetensors',
      ]
      _a      =							'fp16'
      self.assertFalse(is_safetensors_compatible(__magic_name__ ,			variant=__magic_name__  )  )

