'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFResNetForImageClassification, TFResNetModel
  from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class 					_lowercase						:
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=3       ,lowerCamelCase_=32       ,lowerCamelCase_=3       ,lowerCamelCase_=10       ,lowerCamelCase_=[10, 20, 30, 40]       ,lowerCamelCase_=[1, 1, 2, 1]       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_="relu"       ,lowerCamelCase_=3       ,lowerCamelCase_=None       ,)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	parent
         UpperCAmelCase__				:					Dict 					=	batch_size
         UpperCAmelCase__				:					List[str] 					=	image_size
         UpperCAmelCase__				:					List[Any] 					=	num_channels
         UpperCAmelCase__				:					List[str] 					=	embeddings_size
         UpperCAmelCase__				:					Tuple 					=	hidden_sizes
         UpperCAmelCase__				:					Optional[Any] 					=	depths
         UpperCAmelCase__				:					Tuple 					=	is_training
         UpperCAmelCase__				:					List[str] 					=	use_labels
         UpperCAmelCase__				:					str 					=	hidden_act
         UpperCAmelCase__				:					str 					=	num_labels
         UpperCAmelCase__				:					Dict 					=	scope
         UpperCAmelCase__				:					Union[str, Any] 					=	len(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

         UpperCAmelCase__				:					Tuple 					=	None
         if self.use_labels:
            UpperCAmelCase__				:					str 					=	ids_tensor([self.batch_size]       ,self.num_labels				)

         UpperCAmelCase__				:					Optional[Any] 					=	self.get_config()

         return config, pixel_values, labels




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return ResNetConfig(
             num_channels=self.num_channels       ,embeddings_size=self.embeddings_size       ,hidden_sizes=self.hidden_sizes       ,depths=self.depths       ,hidden_act=self.hidden_act       ,num_labels=self.num_labels       ,image_size=self.image_size       ,)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	TFResNetModel(config=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	model(lowerCamelCase_				)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.num_labels
         UpperCAmelCase__				:					Union[str, Any] 					=	TFResNetForImageClassification(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	model(lowerCamelCase_       ,labels=lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels)				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	self.prepare_config_and_inputs()
         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Dict 					=	config_and_inputs
         UpperCAmelCase__				:					Union[str, Any] 					=	{'''pixel_values''': pixel_values}
         return config, inputs_dict




@require_tf
class 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Optional[int]   				= (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      UpperCAmelCase_   :   Tuple   				= (
          {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      UpperCAmelCase_   :   Optional[int]   				= False
      UpperCAmelCase_   :   Union[str, Any]   				= False
      UpperCAmelCase_   :   Union[str, Any]   				= False
      UpperCAmelCase_   :   Union[str, Any]   				= False
      UpperCAmelCase_   :   Any   				= False




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	TFResNetModelTester(self				)
         UpperCAmelCase__				:					Tuple 					=	ConfigTester(self       ,config_class=lowerCamelCase_       ,has_text_modality=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         return




      @unittest.skip(reason='''ResNet does not use inputs_embeds'''				)
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         pass




      @unittest.skip(reason='''ResNet does not support input and output embeddings'''				)
      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Dict 					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase__				:					Dict 					=	model_class(lowerCamelCase_				)
            UpperCAmelCase__				:					Tuple 					=	inspect.signature(model.call				)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase__				:					Dict 					=	[*signature.parameters.keys()]

            UpperCAmelCase__				:					Union[str, Any] 					=	['''pixel_values''']
            self.assertListEqual(arg_names[:1]       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         def check_hidden_states_output(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				):
            UpperCAmelCase__				:					Any 					=	model_class(lowerCamelCase_				)
            UpperCAmelCase__				:					List[str] 					=	model(**self._prepare_for_class(lowerCamelCase_       ,lowerCamelCase_				)				)

            UpperCAmelCase__				:					Union[str, Any] 					=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase__				:					Any 					=	self.model_tester.num_stages
            self.assertEqual(len(lowerCamelCase_				)       ,expected_num_stages + 1				)

            # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]				)       ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,)

         UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__				:					int 					=	['''basic''', '''bottleneck''']
         for model_class in self.all_model_classes:
            for layer_type in layers_type:
               UpperCAmelCase__				:					Tuple 					=	layer_type
               UpperCAmelCase__				:					Tuple 					=	True
               check_hidden_states_output(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase__				:					List[str] 					=	True

               check_hidden_states_output(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_				)




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase__				:					int 					=	TFResNetModel.from_pretrained(lowerCamelCase_				)
            self.assertIsNotNone(lowerCamelCase_				)







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Dict 					=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
   return image


@require_tf
@require_vision
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      @cached_property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return (
             AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
             if is_vision_available()
             else None
         )




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

         UpperCAmelCase__				:					Dict 					=	self.default_image_processor
         UpperCAmelCase__				:					Any 					=	prepare_img()
         UpperCAmelCase__				:					int 					=	image_processor(images=lowerCamelCase_       ,return_tensors='''tf'''				)

         # forward pass
         UpperCAmelCase__				:					Optional[int] 					=	model(**lowerCamelCase_				)

         # verify the logits
         UpperCAmelCase__				:					Optional[int] 					=	tf.TensorShape((1, 1000)				)
         self.assertEqual(outputs.logits.shape       ,lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	tf.constant([-11.1069, -9.7877, -8.3777]				)

         self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,lowerCamelCase_       ,atol=1e-4				)				)





'''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__  :						Optional[Any]					=      logging.getLogger(__name__)

UpperCamelCase__  :						Dict					=      50  # max width of layer names
UpperCamelCase__  :						Any					=      70  # max width of quantizer names







def __UpperCamelCase( _A       :					Union[str, Any]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	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 __UpperCamelCase( _A       :					Tuple     ):
   '''simple docstring'''

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

   UpperCAmelCase__				:					Dict 					=	QuantDescriptor(num_bits=args.aprec	,      calib_method=_A     )
   UpperCAmelCase__				:					str 					=	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 __UpperCamelCase( _A       :					Any	,      _A       :					Any	,      _A       :					Any=False	,      _A       :					Optional[Any]=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(_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 __UpperCamelCase( _A       :					str     ):
   '''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 __UpperCamelCase( _A       :					Tuple	,      _A       :					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(_A     )







def __UpperCamelCase( _A       :					Dict	,      _A       :					Optional[int]     ):
   '''simple docstring'''

   def fusea(_A       :					Optional[Any]	,      _A       :					Optional[Any]	,      _A       :					Dict     ):
      for mod in [qq, qk, qv]:
         if not hasattr(_A	,      '''_amax'''     ):
            print('''          WARNING: NO AMAX BUFFER'''     )
            return
      UpperCAmelCase__				:					Dict 					=	qq._amax.detach().item()
      UpperCAmelCase__				:					List[Any] 					=	qk._amax.detach().item()
      UpperCAmelCase__				:					Optional[int] 					=	qv._amax.detach().item()

      UpperCAmelCase__				:					Dict 					=	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 __UpperCamelCase( _A       :					Dict	,      _A       :					Any     ):
   '''simple docstring'''

   for name, mod in model.named_modules():
      if name.endswith('''.output.dense'''     ) and not name.endswith('''attention.output.dense'''     ):
         UpperCAmelCase__				:					Union[str, Any] 					=	mod._input_quantizer._amax.data.detach().item()
         mod._input_quantizer._amax.data.detach().clamp_(max=_A     )
         UpperCAmelCase__				:					Tuple 					=	mod._input_quantizer._amax.data.detach().item()
         logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'''     )







def __UpperCamelCase( _A       :					str     ):
   '''simple docstring'''

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







def __UpperCamelCase( _A       :					List[str]     ):
   '''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__				:					Any 					=	set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis     )
         UpperCAmelCase__				:					Optional[Any] 					=	set(range(len(mod.weight.size()     )     )     ) - axis_set
         UpperCAmelCase__				:					int 					=	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__				:					str 					=	amax







def __UpperCamelCase( _A       :					Dict	,      _A       :					Tuple=25	,      _A       :					Any=1_80	,      _A       :					Optional[int]=None     ):
   '''simple docstring'''

   if ignore is None:
      UpperCAmelCase__				:					Dict 					=	[]
   elif not isinstance(_A	,      _A     ):
      UpperCAmelCase__				:					int 					=	[ignore]

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

   for name, mod in model.named_modules():
      UpperCAmelCase__				:					str 					=	getattr(_A	,      '''_input_quantizer'''	,      _A     )
      UpperCAmelCase__				:					int 					=	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__				:					Dict 					=	F'''Act:{input_q.extra_repr()}'''
      UpperCAmelCase__				:					int 					=	F'''Wgt:{weight_q.extra_repr()}'''
      UpperCAmelCase__				:					Dict 					=	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 __UpperCamelCase( _A       :					Optional[Any]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	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 __UpperCamelCase( _A       :					Dict	,      _A       :					Optional[Any]	,      _A       :					Union[str, Any]	,      _A       :					Union[str, Any]	,      _A       :					Dict     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	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 __UpperCamelCase( _A       :					str	,      _A       :					Any	,      _A       :					Optional[int]="both"	,      **_A       :					List[str]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Tuple 					=	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 __UpperCamelCase( _A       :					Tuple	,      _A       :					List[str]	,      **_A       :					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__				:					str 					=	F'''Warning: changing {name:{name_width}}'''
               for k, v in kwargs.items():
                  s += F''' {k}={v}'''
                  setattr(_A	,      _A	,      _A     )
               logger.info(_A     )




