





'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	str					     =     SwinaSRConfig()

      if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            _snake_case		:	Optional[Any]					     =     4
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            _snake_case		:	Optional[Any]					     =     4
            _snake_case		:	Tuple					     =     48
            _snake_case		:	Optional[Any]					     =     '''pixelshuffle_aux'''
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            _snake_case		:	Any					     =     [6, 6, 6, 6]
            _snake_case		:	Optional[Any]					     =     60
            _snake_case		:	Optional[Any]					     =     [6, 6, 6, 6]
            _snake_case		:	str					     =     '''pixelshuffledirect'''
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            _snake_case		:	Optional[int]					     =     4
            _snake_case		:	Any					     =     '''nearest+conv'''
      elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
            _snake_case		:	str					     =     1
            _snake_case		:	List[Any]					     =     1
            _snake_case		:	Optional[int]					     =     126
            _snake_case		:	List[str]					     =     7
            _snake_case		:	List[str]					     =     255.0
            _snake_case		:	Optional[int]					     =     ''''''

      return config




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      if "patch_embed.proj" in name and "layers" not in name:
            _snake_case		:	List[str]					     =     name.replace('''patch_embed.proj'''			,      '''embeddings.patch_embeddings.projection'''	)
      if "patch_embed.norm" in name:
            _snake_case		:	Optional[Any]					     =     name.replace('''patch_embed.norm'''			,      '''embeddings.patch_embeddings.layernorm'''	)
      if "layers" in name:
            _snake_case		:	List[str]					     =     name.replace('''layers'''			,      '''encoder.stages'''	)
      if "residual_group.blocks" in name:
            _snake_case		:	Union[str, Any]					     =     name.replace('''residual_group.blocks'''			,      '''layers'''	)
      if "attn.proj" in name:
            _snake_case		:	Optional[Any]					     =     name.replace('''attn.proj'''			,      '''attention.output.dense'''	)
      if "attn" in name:
            _snake_case		:	int					     =     name.replace('''attn'''			,      '''attention.self'''	)
      if "norm1" in name:
            _snake_case		:	Union[str, Any]					     =     name.replace('''norm1'''			,      '''layernorm_before'''	)
      if "norm2" in name:
            _snake_case		:	List[str]					     =     name.replace('''norm2'''			,      '''layernorm_after'''	)
      if "mlp.fc1" in name:
            _snake_case		:	Dict					     =     name.replace('''mlp.fc1'''			,      '''intermediate.dense'''	)
      if "mlp.fc2" in name:
            _snake_case		:	List[str]					     =     name.replace('''mlp.fc2'''			,      '''output.dense'''	)
      if "q_bias" in name:
            _snake_case		:	Optional[Any]					     =     name.replace('''q_bias'''			,      '''query.bias'''	)
      if "k_bias" in name:
            _snake_case		:	Any					     =     name.replace('''k_bias'''			,      '''key.bias'''	)
      if "v_bias" in name:
            _snake_case		:	Tuple					     =     name.replace('''v_bias'''			,      '''value.bias'''	)
      if "cpb_mlp" in name:
            _snake_case		:	Optional[Any]					     =     name.replace('''cpb_mlp'''			,      '''continuous_position_bias_mlp'''	)
      if "patch_embed.proj" in name:
            _snake_case		:	Dict					     =     name.replace('''patch_embed.proj'''			,      '''patch_embed.projection'''	)

      if name == "norm.weight":
            _snake_case		:	List[Any]					     =     '''layernorm.weight'''
      if name == "norm.bias":
            _snake_case		:	Optional[Any]					     =     '''layernorm.bias'''

      if "conv_first" in name:
            _snake_case		:	Optional[int]					     =     name.replace('''conv_first'''			,      '''first_convolution'''	)

      if (
          "upsample" in name
          or "conv_before_upsample" in name
          or "conv_bicubic" in name
          or "conv_up" in name
          or "conv_hr" in name
          or "conv_last" in name
          or "aux" in name
      ):
            # heads
            if "conv_last" in name:
                  _snake_case		:	int					     =     name.replace('''conv_last'''			,      '''final_convolution'''	)
            if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
                  if "conv_before_upsample.0" in name:
                        _snake_case		:	List[Any]					     =     name.replace('''conv_before_upsample.0'''			,      '''conv_before_upsample'''	)
                  if "upsample.0" in name:
                        _snake_case		:	int					     =     name.replace('''upsample.0'''			,      '''upsample.convolution_0'''	)
                  if "upsample.2" in name:
                        _snake_case		:	List[str]					     =     name.replace('''upsample.2'''			,      '''upsample.convolution_1'''	)
                  _snake_case		:	List[Any]					     =     '''upsample.''' + name
            elif config.upsampler == "pixelshuffledirect":
                  _snake_case		:	Union[str, Any]					     =     name.replace('''upsample.0.weight'''			,      '''upsample.conv.weight'''	)
                  _snake_case		:	List[Any]					     =     name.replace('''upsample.0.bias'''			,      '''upsample.conv.bias'''	)
            else:
                  pass
      else:
            _snake_case		:	int					     =     '''swin2sr.''' + name

      return name




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      for key in orig_state_dict.copy().keys():
            _snake_case		:	Optional[int]					     =     orig_state_dict.pop(lowerCAmelCase_	)

            if "qkv" in key:
                  _snake_case		:	List[Any]					     =     key.split('''.'''	)
                  _snake_case		:	str					     =     int(key_split[1]	)
                  _snake_case		:	List[str]					     =     int(key_split[4]	)
                  _snake_case		:	List[Any]					     =     config.embed_dim

                  if "weight" in key:
                        _snake_case		:	Any					     =     val[:dim, :]
                        _snake_case		:	Any					     =     val[dim : dim * 2, :]
                        _snake_case		:	Any					     =     val[-dim:, :]
                  else:
                        _snake_case		:	Any					     =     val[:dim]
                        _snake_case		:	Optional[Any]					     =     val[dim : dim * 2]
                        _snake_case		:	Optional[int]					     =     val[-dim:]
                  pass
            else:
                  _snake_case		:	Dict					     =     val

      return orig_state_dict




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Tuple					     =     get_config(lowerCAmelCase_	)
      _snake_case		:	Optional[Any]					     =     SwinaSRForImageSuperResolution(lowerCAmelCase_	)
      model.eval()

      _snake_case		:	Optional[int]					     =     torch.hub.load_state_dict_from_url(lowerCAmelCase_			,      map_location='''cpu'''	)
      _snake_case		:	int					     =     convert_state_dict(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case     ,							_snake_case		:	Tuple					     =     model.load_state_dict(lowerCAmelCase_			,      strict=lowerCAmelCase_	)

      if len(lowerCAmelCase_	) > 0:
            raise ValueError('''Missing keys when converting: {}'''.format(lowerCAmelCase_	)	)
      for key in unexpected_keys:
            if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
                  raise ValueError(f'''Unexpected key {key} in state_dict'''	)

    # verify values
      _snake_case		:	List[str]					     =     '''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
      _snake_case		:	Union[str, Any]					     =     Image.open(requests.get(lowerCAmelCase_			,      stream=lowerCAmelCase_	).raw	).convert('''RGB'''	)
      _snake_case		:	List[Any]					     =     SwinaSRImageProcessor()
      # pixel_values = processor(image, return_tensors="pt").pixel_values

      _snake_case		:	Optional[Any]					     =     126 if '''Jpeg''' in checkpoint_url else 256
      _snake_case		:	Tuple					     =     Compose(
          [
              Resize((image_size, image_size)	),
              ToTensor(),
              Normalize(mean=[0.485, 0.456, 0.406]			,      std=[0.229, 0.224, 0.225]	),
          ]	)
      _snake_case		:	Union[str, Any]					     =     transforms(lowerCAmelCase_	).unsqueeze(0	)

      if config.num_channels == 1:
            _snake_case		:	Optional[int]					     =     pixel_values[:, 0, :, :].unsqueeze(1	)

      _snake_case		:	Optional[int]					     =     model(lowerCAmelCase_	)

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            _snake_case		:	Tuple					     =     torch.Size([1, 3, 512, 512]	)
            _snake_case		:	Union[str, Any]					     =     torch.tensor(
                [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]	)
      elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            _snake_case		:	Dict					     =     torch.Size([1, 3, 1_024, 1_024]	)
            _snake_case		:	Dict					     =     torch.tensor(
                [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]	)
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            # TODO values didn't match exactly here
            _snake_case		:	Tuple					     =     torch.Size([1, 3, 1_024, 1_024]	)
            _snake_case		:	Any					     =     torch.tensor(
                [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]	)
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            _snake_case		:	List[str]					     =     torch.Size([1, 3, 512, 512]	)
            _snake_case		:	Optional[Any]					     =     torch.tensor(
                [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]	)
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            _snake_case		:	Dict					     =     torch.Size([1, 3, 1_024, 1_024]	)
            _snake_case		:	str					     =     torch.tensor(
                [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]	)

      assert (
          outputs.reconstruction.shape == expected_shape
      ), f'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
      assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]			,      lowerCAmelCase_			,      atol=1E-3	)
      print('''Looks ok!'''	)

      _snake_case		:	Dict					     =     {
          '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''': (
              '''swin2SR-classical-sr-x2-64'''
          ),
          '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth''': (
              '''swin2SR-classical-sr-x4-64'''
          ),
          '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth''': (
              '''swin2SR-compressed-sr-x4-48'''
          ),
          '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth''': (
              '''swin2SR-lightweight-x2-64'''
          ),
          '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth''': (
              '''swin2SR-realworld-sr-x4-64-bsrgan-psnr'''
          ),
      }
      _snake_case		:	Optional[Any]					     =     url_to_name[checkpoint_url]

      if pytorch_dump_folder_path is not None:
            print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
            model.save_pretrained(lowerCAmelCase_	)
            print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
            processor.save_pretrained(lowerCAmelCase_	)

      if push_to_hub:
            model.push_to_hub(f'''caidas/{model_name}'''	)
            processor.push_to_hub(f'''caidas/{model_name}'''	)


if __name__ == "__main__":
   UpperCAmelCase					:		Union[str, Any]		=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--checkpoint_url',
       default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
       type=str,
       help='URL of the original Swin2SR checkpoint you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the converted model to the hub.')

   UpperCAmelCase					:		Dict		=							parser.parse_args()
   convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''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


UpperCAmelCase					:		str		=							logging.getLogger(__name__)

UpperCAmelCase					:		Dict		=							5_0  # max width of layer names
UpperCAmelCase					:		Union[str, Any]		=							7_0  # max width of quantizer names




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      if args.calibrator == "max":
            _snake_case		:	Optional[int]					     =     '''max'''
      elif args.calibrator == "percentile":
            if args.percentile is None:
                  raise ValueError('''Specify --percentile when using percentile calibrator'''	)
            _snake_case		:	Tuple					     =     '''histogram'''
      elif args.calibrator == "mse":
            _snake_case		:	int					     =     '''histogram'''
      else:
            raise ValueError(f'''Invalid calibrator {args.calibrator}'''	)

      _snake_case		:	Tuple					     =     QuantDescriptor(num_bits=args.aprec			,      calib_method=lowerCAmelCase_	)
      _snake_case		:	str					     =     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_			,      lowerCAmelCase_			,      lowerCAmelCase_=False			,      lowerCAmelCase_=False	):





      """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_	):





      """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_			,      lowerCAmelCase_	):





      """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_			,      lowerCAmelCase_	):





      """simple docstring"""



      def fusea(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):
            for mod in [qq, qk, qv]:
                  if not hasattr(lowerCAmelCase_			,      '''_amax'''	):
                        print('''          WARNING: NO AMAX BUFFER'''	)
                        return
            _snake_case		:	Tuple					     =     qq._amax.detach().item()
            _snake_case		:	Tuple					     =     qk._amax.detach().item()
            _snake_case		:	List[Any]					     =     qv._amax.detach().item()

            _snake_case		:	List[str]					     =     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_			,      lowerCAmelCase_	):





      """simple docstring"""



      for name, mod in model.named_modules():
            if name.endswith('''.output.dense'''	) and not name.endswith('''attention.output.dense'''	):
                  _snake_case		:	List[Any]					     =     mod._input_quantizer._amax.data.detach().item()
                  mod._input_quantizer._amax.data.detach().clamp_(max=lowerCAmelCase_	)
                  _snake_case		:	List[str]					     =     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_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_	):





      """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)
                  _snake_case		:	int					     =     set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis	)
                  _snake_case		:	Dict					     =     set(range(len(mod.weight.size()	)	)	) - axis_set
                  _snake_case		:	Optional[int]					     =     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()}'''	)
                  _snake_case		:	Tuple					     =     amax




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





      """simple docstring"""



      if ignore is None:
            _snake_case		:	Dict					     =     []
      elif not isinstance(lowerCAmelCase_			,      lowerCAmelCase_	):
            _snake_case		:	Optional[int]					     =     [ignore]

      _snake_case		:	str					     =     0
      for name, mod in model.named_modules():
            if not hasattr(lowerCAmelCase_			,      '''weight'''	):
                  continue
            _snake_case		:	Optional[int]					     =     max(lowerCAmelCase_			,      len(lowerCAmelCase_	)	)

      for name, mod in model.named_modules():
            _snake_case		:	Optional[Any]					     =     getattr(lowerCAmelCase_			,      '''_input_quantizer'''			,      lowerCAmelCase_	)
            _snake_case		:	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
            _snake_case		:	Optional[int]					     =     f'''Act:{input_q.extra_repr()}'''
            _snake_case		:	Any					     =     f'''Wgt:{weight_q.extra_repr()}'''
            _snake_case		:	Optional[int]					     =     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_	):





      """simple docstring"""



      _snake_case		:	str					     =     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_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     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_			,      lowerCAmelCase_			,      lowerCAmelCase_="both"			,      **lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     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_			,      lowerCAmelCase_			,      **lowerCAmelCase_	):





      """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_	):
                              _snake_case		:	Any					     =     f'''Warning: changing {name:{name_width}}'''
                              for k, v in kwargs.items():
                                    s += f''' {k}={v}'''
                                    setattr(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
                              logger.info(lowerCAmelCase_	)


