

# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=0			):
  # Format the message.
  if name is None:
    UpperCAmelCase					      =  None
  else:
    UpperCAmelCase					      =  "." * max(0   ,       spaces - 2			) + "# {:" + str(50 - spaces			) + "s}"
    UpperCAmelCase					      =  fmt.format(__A			)

  # Print and recurse (if needed).
  if isinstance(__A   ,       __A			):
    if msg is not None:
      print(__A			)
    for k in val.keys():
      recursive_print(__A   ,       val[k]   ,       spaces + 2			)
  elif isinstance(__A   ,       torch.Tensor			):
    print(__A   ,       ":"   ,       val.size()			)
  else:
    print(__A   ,       ":"   ,       __A			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
  # for compatibility with later versions of NVIDIA Megatron-LM.
  # The inverse operation is performed inside Megatron-LM to read checkpoints:
  # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
  # If param is the weight tensor of the self-attention block, the returned tensor
  # will have to be transposed one more time to be read by HuggingFace GPT2.
  UpperCAmelCase					      =  param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    UpperCAmelCase					      =  (num_heads, hidden_size, num_splits) + input_shape[1:]
    UpperCAmelCase					      =  param.view(*__A			)
    UpperCAmelCase					      =  param.transpose(0   ,       2			)
    UpperCAmelCase					      =  param.transpose(1   ,       2			).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    UpperCAmelCase					      =  (num_heads, num_splits, hidden_size) + input_shape[1:]
    UpperCAmelCase					      =  param.view(*__A			)
    UpperCAmelCase					      =  param.transpose(0   ,       1			).contiguous()
  UpperCAmelCase					      =  param.view(*__A			)
  return param
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  # The converted output model.
  UpperCAmelCase					      =  {}

  # old versions did not store training args
  UpperCAmelCase					      =  input_state_dict.get("args"   ,       __A			)
  if ds_args is not None:
    # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
    # from pprint import pprint
    # pprint(vars(ds_args))

    UpperCAmelCase					      =  ds_args.padded_vocab_size
    UpperCAmelCase					      =  ds_args.max_position_embeddings
    UpperCAmelCase					      =  ds_args.hidden_size
    UpperCAmelCase					      =  ds_args.num_layers
    UpperCAmelCase					      =  ds_args.num_attention_heads
    UpperCAmelCase					      =  ds_args.ffn_hidden_size
    # pprint(config)

  # The number of heads.
  UpperCAmelCase					      =  config.n_head
  # The hidden_size per head.
  UpperCAmelCase					      =  config.n_embd // config.n_head
  # Megatron-LM checkpoint version
  if "checkpoint_version" in input_state_dict.keys():
    UpperCAmelCase					      =  input_state_dict["checkpoint_version"]
  else:
    UpperCAmelCase					      =  0.0

  # The model.
  UpperCAmelCase					      =  input_state_dict["model"]
  # The language model.
  UpperCAmelCase					      =  model["language_model"]
  # The embeddings.
  UpperCAmelCase					      =  lm["embedding"]

  # The word embeddings.
  UpperCAmelCase					      =  embeddings["word_embeddings"]["weight"]
  # Truncate the embedding table to vocab_size rows.
  UpperCAmelCase					      =  word_embeddings[: config.vocab_size, :]
  UpperCAmelCase					      =  word_embeddings

  # The position embeddings.
  UpperCAmelCase					      =  embeddings["position_embeddings"]["weight"]
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  UpperCAmelCase					      =  pos_embeddings.size(0			)
  if n_positions != config.n_positions:
    raise ValueError(
        F"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"			)
  # Store the position embeddings.
  UpperCAmelCase					      =  pos_embeddings

  # The transformer.
  UpperCAmelCase					      =  lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

  # The regex to extract layer names.
  UpperCAmelCase					      =  re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"			)

  # The simple map of names for "automated" rules.
  UpperCAmelCase					      =  {
      "attention.dense": ".attn.c_proj.",
      "self_attention.dense": ".attn.c_proj.",
      "mlp.dense_h_to_4h": ".mlp.c_fc.",
      "mlp.dense_4h_to_h": ".mlp.c_proj.",
  }

  # Extract the layers.
  for key, val in transformer.items():
    # Match the name.
    UpperCAmelCase					      =  layer_re.match(__A			)

    # Stop if that's not a layer
    if m is None:
      break

    # The index of the layer.
    UpperCAmelCase					      =  int(m.group(1			)			)
    # The name of the operation.
    UpperCAmelCase					      =  m.group(2			)
    # Is it a weight or a bias?
    UpperCAmelCase					      =  m.group(3			)

    # The name of the layer.
    UpperCAmelCase					      =  F"transformer.h.{layer_idx}"

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith("layernorm"			):
      UpperCAmelCase					      =  "ln_1" if op_name.startswith("input"			) else "ln_2"
      UpperCAmelCase					      =  val

    # Transpose the QKV matrix.
    elif (
        op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
    ) and weight_or_bias == "weight":
      # Insert a tensor of 1x1xDxD bias.
      UpperCAmelCase					      =  torch.tril(torch.ones((n_positions, n_positions)   ,       dtype=torch.floataa			)			).view(
          1   ,       1   ,       __A   ,       __A			)
      UpperCAmelCase					      =  causal_mask

      # Insert a "dummy" tensor for masked_bias.
      UpperCAmelCase					      =  torch.tensor(-1E4   ,       dtype=torch.floataa			)
      UpperCAmelCase					      =  masked_bias

      UpperCAmelCase					      =  fix_query_key_value_ordering(__A   ,       __A   ,       3   ,       __A   ,       __A			)
      # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
      UpperCAmelCase					      =  out_val.transpose(0   ,       1			).contiguous()
      # Store.
      UpperCAmelCase					      =  out_val

    # Transpose the bias.
    elif (
        op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
    ) and weight_or_bias == "bias":
      UpperCAmelCase					      =  fix_query_key_value_ordering(__A   ,       __A   ,       3   ,       __A   ,       __A			)
      # Store. No change of shape.
      UpperCAmelCase					      =  out_val

    # Transpose the weights.
    elif weight_or_bias == "weight":
      UpperCAmelCase					      =  megatron_to_transformers[op_name]
      UpperCAmelCase					      =  val.transpose(0   ,       1			)

    # Copy the bias.
    elif weight_or_bias == "bias":
      UpperCAmelCase					      =  megatron_to_transformers[op_name]
      UpperCAmelCase					      =  val

    # DEBUG.
  assert config.n_layer == layer_idx + 1

  # The final layernorm.
  UpperCAmelCase					      =  transformer["final_layernorm.weight"]
  UpperCAmelCase					      =  transformer["final_layernorm.bias"]

  # For LM head, transformers' wants the matrix to weight embeddings.
  UpperCAmelCase					      =  word_embeddings

  # It should be done!
  return output_state_dict
def      _lowerCAmelCase(	):
  # Create the argument parser.
  UpperCAmelCase					      =  argparse.ArgumentParser()
  parser.add_argument("--print-checkpoint-structure"   ,       action="store_true"			)
  parser.add_argument(
      "path_to_checkpoint"   ,       type=__A   ,       help="Path to the checkpoint file (.zip archive or direct .pt file)"   ,       )
  parser.add_argument(
      "--config_file"   ,       default=""   ,       type=__A   ,       help="An optional config json file describing the pre-trained model."   ,       )
  UpperCAmelCase					      =  parser.parse_args()

  # Extract the basename.
  UpperCAmelCase					      =  os.path.dirname(args.path_to_checkpoint			)

  # Load the model.
  # the .zip is very optional, let's keep it for backward compatibility
  print(F"Extracting PyTorch state dictionary from {args.path_to_checkpoint}"			)
  if args.path_to_checkpoint.endswith(".zip"			):
    with zipfile.ZipFile(args.path_to_checkpoint   ,       "r"			) as checkpoint:
      with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"			) as pytorch_dict:
        UpperCAmelCase					      =  torch.load(__A   ,       map_location="cpu"			)
  else:
    UpperCAmelCase					      =  torch.load(args.path_to_checkpoint   ,       map_location="cpu"			)

  UpperCAmelCase					      =  input_state_dict.get("args"   ,       __A			)

  # Read the config, or default to the model released by NVIDIA.
  if args.config_file == "":
    if ds_args is not None:
      if ds_args.bias_gelu_fusion:
        UpperCAmelCase					      =  "gelu_fast"
      elif ds_args.openai_gelu:
        UpperCAmelCase					      =  "gelu_new"
      else:
        UpperCAmelCase					      =  "gelu"
    else:
      # in the very early days this used to be "gelu_new"
      UpperCAmelCase					      =  "gelu_new"

    # Spell out all parameters in case the defaults change.
    UpperCAmelCase					      =  GPTaConfig(
        vocab_size=50257   ,       n_positions=1024   ,       n_embd=1024   ,       n_layer=24   ,       n_head=16   ,       n_inner=4096   ,       activation_function=__A   ,       resid_pdrop=0.1   ,       embd_pdrop=0.1   ,       attn_pdrop=0.1   ,       layer_norm_epsilon=1E-5   ,       initializer_range=0.02   ,       summary_type="cls_index"   ,       summary_use_proj=__A   ,       summary_activation=__A   ,       summary_proj_to_labels=__A   ,       summary_first_dropout=0.1   ,       scale_attn_weights=__A   ,       use_cache=__A   ,       bos_token_id=50256   ,       eos_token_id=50256   ,       )
  else:
    UpperCAmelCase					      =  GPTaConfig.from_json_file(args.config_file			)

  UpperCAmelCase					      =  ["GPT2LMHeadModel"]

  # Convert.
  print("Converting"			)
  UpperCAmelCase					      =  convert_megatron_checkpoint(__A   ,       __A   ,       __A			)

  # Print the structure of converted state dict.
  if args.print_checkpoint_structure:
    recursive_print(__A   ,       __A			)

  # Add tokenizer class info to config
  # see https://github.com/huggingface/transformers/issues/13906)
  if ds_args is not None:
    UpperCAmelCase					      =  ds_args.tokenizer_type
    if tokenizer_type == "GPT2BPETokenizer":
      UpperCAmelCase					      =  "gpt2"
    elif tokenizer_type == "PretrainedFromHF":
      UpperCAmelCase					      =  ds_args.tokenizer_name_or_path
    else:
      raise ValueError(F"Unrecognized tokenizer_type {tokenizer_type}"			)
  else:
    UpperCAmelCase					      =  "gpt2"

  UpperCAmelCase					      =  AutoTokenizer.from_pretrained(__A			)
  UpperCAmelCase					      =  type(__A			).__name__
  UpperCAmelCase					      =  tokenizer_class

  # Store the config to file.
  print("Saving config"			)
  config.save_pretrained(__A			)

  # Save tokenizer based on args
  print(F"Adding {tokenizer_class} tokenizer files"			)
  tokenizer.save_pretrained(__A			)

  # Store the state_dict to file.
  UpperCAmelCase					      =  os.path.join(__A   ,       "pytorch_model.bin"			)
  print(F"Saving checkpoint to \"{output_checkpoint_file}\""			)
  torch.save(__A   ,       __A			)


####################################################################################################

if __name__ == "__main__":
      main()

####################################################################################################






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__       = logging.getLogger(__name__)

lowerCAmelCase__       = 50  # max width of layer names
lowerCAmelCase__       = 70  # max width of quantizer names
def      _lowerCAmelCase(	__A			):
  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			):

  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   ,       __A   ,       __A=False   ,       __A=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(__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			):
  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   ,       __A			):
  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   ,       __A			):

  def fusea(__A   ,       __A   ,       __A			):
    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   ,       __A			):

  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			):

  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			):

  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   ,       __A=25   ,       __A=180   ,       __A=None			):

  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			):
  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   ,       __A   ,       __A   ,       __A   ,       __A			):
  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   ,       __A   ,       __A="both"   ,       **__A			):
  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   ,       __A   ,       **__A			):

  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			)



