

"""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       =							logging.getLogger(__name__)

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




def   _A  (  UpperCamelCase_     :       int)	->						Dict:
   '''simple docstring'''


   __lowercase								=						parser.add_argument_group("quant_trainer arguments")
   group.add_argument("--wprec",			type=lowercase__,			default=8,			help="weight precision")
   group.add_argument("--aprec",			type=lowercase__,			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=lowercase__,			nargs="+",			help="disable quantizers by keyword")
   group.add_argument("--quant-disable-layer-module",			type=lowercase__,			help="disable quantizers by keyword under layer.")
   group.add_argument("--quant-enable-layer-module",			type=lowercase__,			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=lowercase__,			type=lowercase__,			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=lowercase__,			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  (  UpperCamelCase_     :       int)	->						Union[str, Any]:
   '''simple docstring'''


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

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




def   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       List[str],			UpperCamelCase_     :       Any=False,			UpperCamelCase_     :       Optional[int]=False)	->						int:
   '''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(lowercase__,			["embeddings"],			which="weight",			_disabled=lowercase__)

      if args.quant_disable:
         set_quantizer_by_name(lowercase__,			[""],			_disabled=lowercase__)

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

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

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

      if args.recalibrate_weights:
         recalibrate_weights(lowercase__)

      if args.fuse_qkv:
         fuse_qkv(lowercase__,			lowercase__)

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

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




def   _A  (  UpperCamelCase_     :       Any)	->						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   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Union[str, Any])	->						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(lowercase__)




def   _A  (  UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       List[str])	->						Union[str, Any]:
   '''simple docstring'''


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

      __lowercase								=						max(lowercase__,			lowercase__,			lowercase__)
      qq._amax.fill_(lowercase__)
      qk._amax.fill_(lowercase__)
      qv._amax.fill_(lowercase__)
      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  (  UpperCamelCase_     :       List[str],			UpperCamelCase_     :       Dict)	->						str:
   '''simple docstring'''


   for name, mod in model.named_modules():
      if name.endswith(".output.dense") and not name.endswith("attention.output.dense"):
         __lowercase								=						mod._input_quantizer._amax.data.detach().item()
         mod._input_quantizer._amax.data.detach().clamp_(max=lowercase__)
         __lowercase								=						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  (  UpperCamelCase_     :       List[Any])	->						Optional[int]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       str)	->						List[str]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       Any,			UpperCamelCase_     :       Union[str, Any]=25,			UpperCamelCase_     :       str=180,			UpperCamelCase_     :       Optional[int]=None)	->						Optional[int]:
   '''simple docstring'''


   if ignore is None:
      __lowercase								=						[]
   elif not isinstance(lowercase__,			lowercase__):
      __lowercase								=						[ignore]

   __lowercase								=						0
   for name, mod in model.named_modules():
      if not hasattr(lowercase__,			"weight"):
         continue
      __lowercase								=						max(lowercase__,			len(lowercase__))

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




def   _A  (  UpperCamelCase_     :       Union[str, Any])	->						Optional[int]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Any,			UpperCamelCase_     :       Dict)	->						List[Any]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       List[str],			UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       Dict="both",			**UpperCamelCase_     :       Optional[Any])	->						Tuple:
   '''simple docstring'''


   __lowercase								=						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(lowercase__,			lowercase__,			"_input_quantizer",			lowercase__,			lowercase__)
      if which in ["weight", "both"]:
         set_quantizer(lowercase__,			lowercase__,			"_weight_quantizer",			lowercase__,			lowercase__)
   logger.info(lowercase__)




def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       str,			**UpperCamelCase_     :       Tuple)	->						str:
   '''simple docstring'''


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










"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

							# uncondition for scaling
							_lowerCamelCase     :			str    =    nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states