





'''simple docstring'''

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def    a__							(							lowercase   :	Tuple,   lowercase   :	List[str],   lowercase   :	Optional[int],   lowercase   :	List[str],   lowercase   :	List[str]=True,   lowercase   :	str="pt" )						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			{'''add_prefix_space''': True} if isinstance(lowercase,   lowercase ) and not line.startswith(''' ''' ) else {}
     _UpperCamelCase							       =			padding_side
     return tokenizer(
         [line],   max_length=lowercase,   padding='''max_length''' if pad_to_max_length else None,   truncation=lowercase,   return_tensors=lowercase,   add_special_tokens=lowercase,   **lowercase,   )







def    a__							(							lowercase   :	str,   lowercase   :	int,   lowercase   :	Tuple=None,   )						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			input_ids.ne(lowercase ).any(dim=0 )
     if attention_mask is None:
          return input_ids[:, keep_column_mask]
     else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Optional[int]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						str="train"			,					lowerCAmelCase__  :						List[str]=None			,					lowerCAmelCase__  :						Dict=None			,					lowerCAmelCase__  :						Optional[Any]=None			,					lowerCAmelCase__  :						Dict=""			,					)       ->	Any:

            '''simple docstring'''




            super().__init__()
            _UpperCamelCase							       =			Path(lowerCAmelCase__	).joinpath(type_path + '''.source'''	)
            _UpperCamelCase							       =			Path(lowerCAmelCase__	).joinpath(type_path + '''.target'''	)
            _UpperCamelCase							       =			self.get_char_lens(self.src_file	)
            _UpperCamelCase							       =			max_source_length
            _UpperCamelCase							       =			max_target_length
            assert min(self.src_lens	) > 0, f"""found empty line in {self.src_file}"""
            _UpperCamelCase							       =			tokenizer
            _UpperCamelCase							       =			prefix
            if n_obs is not None:
                 _UpperCamelCase							       =			self.src_lens[:n_obs]
            _UpperCamelCase							       =			src_lang
            _UpperCamelCase							       =			tgt_lang





       def __len__(				self  :						Optional[Any]	)       ->	List[Any]:

            '''simple docstring'''




            return len(self.src_lens	)





       def __getitem__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Any	)       ->	Dict[str, torch.Tensor]:

            '''simple docstring'''




            _UpperCamelCase							       =			index + 1  # linecache starts at 1
            _UpperCamelCase							       =			self.prefix + linecache.getline(str(self.src_file	)			,					lowerCAmelCase__	).rstrip('''\n'''	)
            _UpperCamelCase							       =			linecache.getline(str(self.tgt_file	)			,					lowerCAmelCase__	).rstrip('''\n'''	)
            assert source_line, f"""empty source line for index {index}"""
            assert tgt_line, f"""empty tgt line for index {index}"""

            # Need to add eos token manually for T5
            if isinstance(self.tokenizer			,					lowerCAmelCase__	):
                 source_line += self.tokenizer.eos_token
                 tgt_line += self.tokenizer.eos_token

            # Pad source and target to the right
            _UpperCamelCase							       =			(
                self.tokenizer.question_encoder if isinstance(self.tokenizer			,					lowerCAmelCase__	) else self.tokenizer
            )
            _UpperCamelCase							       =			self.tokenizer.generator if isinstance(self.tokenizer			,					lowerCAmelCase__	) else self.tokenizer

            _UpperCamelCase							       =			encode_line(lowerCAmelCase__			,					lowerCAmelCase__			,					self.max_source_length			,					'''right'''	)
            _UpperCamelCase							       =			encode_line(lowerCAmelCase__			,					lowerCAmelCase__			,					self.max_target_length			,					'''right'''	)

            _UpperCamelCase							       =			source_inputs['''input_ids'''].squeeze()
            _UpperCamelCase							       =			target_inputs['''input_ids'''].squeeze()
            _UpperCamelCase							       =			source_inputs['''attention_mask'''].squeeze()
            return {
                "input_ids": source_ids,
                "attention_mask": src_mask,
                "decoder_input_ids": target_ids,
            }





       @staticmethod
       def snake_case__		(				lowerCAmelCase__  :						List[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            return [len(lowerCAmelCase__	) for x in Path(lowerCAmelCase__	).open().readlines()]







       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						List[str]	)       ->	Dict[str, torch.Tensor]:

            '''simple docstring'''




            _UpperCamelCase							       =			torch.stack([x['''input_ids'''] for x in batch]	)
            _UpperCamelCase							       =			torch.stack([x['''attention_mask'''] for x in batch]	)
            _UpperCamelCase							       =			torch.stack([x['''decoder_input_ids'''] for x in batch]	)
            _UpperCamelCase							       =			(
                self.tokenizer.generator.pad_token_id
                if isinstance(self.tokenizer			,					lowerCAmelCase__	)
                else self.tokenizer.pad_token_id
            )
            _UpperCamelCase							       =			(
                self.tokenizer.question_encoder.pad_token_id
                if isinstance(self.tokenizer			,					lowerCAmelCase__	)
                else self.tokenizer.pad_token_id
            )
            _UpperCamelCase							       =			trim_batch(lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase       ,							_UpperCamelCase							       =			trim_batch(lowerCAmelCase__			,					lowerCAmelCase__			,					attention_mask=lowerCAmelCase__	)
            _UpperCamelCase							       =			{
                '''input_ids''': source_ids,
                '''attention_mask''': source_mask,
                '''decoder_input_ids''': y,
            }
            return batch

lowercase__		:     Union[str, Any]			=							getLogger(__name__)







def    a__							(							lowercase   :	List[List] )						->						Optional[int]:





     """simple docstring"""

     return list(itertools.chain.from_iterable(lowercase ) )







def    a__							(							lowercase   :	str )						->						None:





     """simple docstring"""

     _UpperCamelCase							       =			get_git_info()
     save_json(lowercase,   os.path.join(lowercase,   '''git_log.json''' ) )







def    a__							(							lowercase   :	Optional[int],   lowercase   :	List[str],   lowercase   :	str=4,   **lowercase   :	Optional[Any] )						->						Any:





     """simple docstring"""

     with open(lowercase,   '''w''' ) as f:
          json.dump(lowercase,   lowercase,   indent=lowercase,   **lowercase )







def    a__							(							lowercase   :	Union[str, Any] )						->						Optional[int]:





     """simple docstring"""

     with open(lowercase ) as f:
          return json.load(lowercase )







def    a__							(							)						->						List[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			git.Repo(search_parent_directories=lowercase )
     _UpperCamelCase							       =			{
         '''repo_id''': str(lowercase ),
         '''repo_sha''': str(repo.head.object.hexsha ),
         '''repo_branch''': str(repo.active_branch ),
         '''hostname''': str(socket.gethostname() ),
     }
     return repo_infos







def    a__							(							lowercase   :	Callable,   lowercase   :	Iterable )						->						List:





     """simple docstring"""

     return list(map(lowercase,   lowercase ) )







def    a__							(							lowercase   :	List[Any],   lowercase   :	Dict )						->						Optional[int]:





     """simple docstring"""

     with open(lowercase,   '''wb''' ) as f:
          return pickle.dump(lowercase,   lowercase )







def    a__							(							lowercase   :	Tuple )						->						Tuple:





     """simple docstring"""

     def remove_articles(lowercase   :	Tuple ):
          return re.sub(r'''\b(a|an|the)\b''',   ''' ''',   lowercase )

     def white_space_fix(lowercase   :	Tuple ):
          return " ".join(text.split() )

     def remove_punc(lowercase   :	Optional[int] ):
          _UpperCamelCase							       =			set(string.punctuation )
          return "".join(ch for ch in text if ch not in exclude )

     def lower(lowercase   :	Tuple ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(lowercase ) ) ) )







def    a__							(							lowercase   :	Tuple,   lowercase   :	Union[str, Any] )						->						Dict:





     """simple docstring"""

     _UpperCamelCase							       =			normalize_answer(lowercase ).split()
     _UpperCamelCase							       =			normalize_answer(lowercase ).split()
     _UpperCamelCase							       =			Counter(lowercase ) & Counter(lowercase )
     _UpperCamelCase							       =			sum(common.values() )
     if num_same == 0:
          return 0
     _UpperCamelCase							       =			1.0 * num_same / len(lowercase )
     _UpperCamelCase							       =			1.0 * num_same / len(lowercase )
     _UpperCamelCase							       =			(2 * precision * recall) / (precision + recall)
     return fa







def    a__							(							lowercase   :	Tuple,   lowercase   :	Any )						->						List[str]:





     """simple docstring"""

     return normalize_answer(lowercase ) == normalize_answer(lowercase )







def    a__							(							lowercase   :	List[str],   lowercase   :	List[str] )						->						Dict:





     """simple docstring"""

     assert len(lowercase ) == len(lowercase )
     _UpperCamelCase							       =			0
     for hypo, pred in zip(lowercase,   lowercase ):
          em += exact_match_score(lowercase,   lowercase )
     if len(lowercase ) > 0:
          em /= len(lowercase )
     return {"em": em}







def    a__							(							lowercase   :	Tuple )						->						Union[str, Any]:





     """simple docstring"""

     return model_prefix.startswith('''rag''' )







def    a__							(							lowercase   :	int,   lowercase   :	List[Any],   lowercase   :	List[Any] )						->						Optional[int]:





     """simple docstring"""

     _UpperCamelCase							       =			{p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     _UpperCamelCase							       =			'''dropout_rate'''
     for p in extra_params:
          if getattr(lowercase,   lowercase,   lowercase ):
               if not hasattr(lowercase,   lowercase ) and not hasattr(lowercase,   equivalent_param[p] ):
                    logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase ) )
                    delattr(lowercase,   lowercase )
                    continue
               _UpperCamelCase							       =			p if hasattr(lowercase,   lowercase ) else equivalent_param[p]
               setattr(lowercase,   lowercase,   getattr(lowercase,   lowercase ) )
               delattr(lowercase,   lowercase )
     return hparams, config







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


SCREAMING_SNAKE_CASE							    =							logging.getLogger(__name__)

SCREAMING_SNAKE_CASE							    =							5_0  # max width of layer names
SCREAMING_SNAKE_CASE							    =							7_0  # max width of quantizer names




def  a   (lowerCAmelCase__   ):
   __a					=  parser.add_argument_group("""quant_trainer arguments"""   )
   group.add_argument("""--wprec"""		,				type=lowerCAmelCase__		,				default=8		,				help="""weight precision"""   )
   group.add_argument("""--aprec"""		,				type=lowerCAmelCase__		,				default=8		,				help="""activation precision"""   )
   group.add_argument("""--quant-per-tensor"""		,				action="""store_true"""		,				help="""per tensor weight scaling"""   )
   group.add_argument("""--quant-disable"""		,				action="""store_true"""		,				help="""disable all quantizers"""   )
   group.add_argument("""--quant-disable-embeddings"""		,				action="""store_true"""		,				help="""disable all embeddings quantizers"""   )
   group.add_argument("""--quant-disable-keyword"""		,				type=lowerCAmelCase__		,				nargs="""+"""		,				help="""disable quantizers by keyword"""   )
   group.add_argument("""--quant-disable-layer-module"""		,				type=lowerCAmelCase__		,				help="""disable quantizers by keyword under layer."""   )
   group.add_argument("""--quant-enable-layer-module"""		,				type=lowerCAmelCase__		,				help="""enable quantizers by keyword under layer"""   )
   group.add_argument("""--calibrator"""		,				default="""max"""		,				help="""which quantization range calibrator to use"""   )
   group.add_argument("""--percentile"""		,				default=lowerCAmelCase__		,				type=lowerCAmelCase__		,				help="""percentile for PercentileCalibrator"""   )
   group.add_argument("""--fuse-qkv"""		,				action="""store_true"""		,				help="""use the same scale factor for qkv"""   )
   group.add_argument("""--clip-gelu"""		,				metavar="""N"""		,				type=lowerCAmelCase__		,				help="""clip gelu output maximum value to N"""   )
   group.add_argument(
       """--recalibrate-weights"""		,				action="""store_true"""		,				help=(
           """recalibrate weight amaxes by taking the max of the weights."""
           """ amaxes will be computed with the current quantization granularity (axis)."""
       )		,				)




def  a   (lowerCAmelCase__   ):

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

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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=False		,				lowerCAmelCase__=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(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__   ):
   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__   ):
   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__   ):

   def fusea(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
      for mod in [qq, qk, qv]:
         if not hasattr(lowerCAmelCase__		,				"""_amax"""   ):
            print("""          WARNING: NO AMAX BUFFER"""   )
            return
      __a					=  qq._amax.detach().item()
      __a					=  qk._amax.detach().item()
      __a					=  qv._amax.detach().item()

      __a					=  max(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )
      qq._amax.fill_(lowerCAmelCase__   )
      qk._amax.fill_(lowerCAmelCase__   )
      qv._amax.fill_(lowerCAmelCase__   )
      logger.info(f'''          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}'''   )

   for name, mod in model.named_modules():
      if name.endswith(""".attention.self"""   ):
         logger.info(f'''FUSE_QKV: {name:{name_width}}'''   )
         fusea(mod.matmul_q_input_quantizer		,				mod.matmul_k_input_quantizer		,				mod.matmul_v_input_quantizer   )
         if args.quant_per_tensor:
            fusea(mod.query._weight_quantizer		,				mod.key._weight_quantizer		,				mod.value._weight_quantizer   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):

   for name, mod in model.named_modules():
      if name.endswith(""".output.dense"""   ) and not name.endswith("""attention.output.dense"""   ):
         __a					=  mod._input_quantizer._amax.data.detach().item()
         mod._input_quantizer._amax.data.detach().clamp_(max=lowerCAmelCase__   )
         __a					=  mod._input_quantizer._amax.data.detach().item()
         logger.info(f'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'''   )




def  a   (lowerCAmelCase__   ):

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




def  a   (lowerCAmelCase__   ):

   for name, mod in model.named_modules():
      if hasattr(lowerCAmelCase__		,				"""_weight_quantizer"""   ):
         if not hasattr(mod.weight_quantizer		,				"""_amax"""   ):
            print("""RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER"""   )
            continue

         # determine which axes to reduce across
         # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
         __a					=  set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
         __a					=  set(range(len(mod.weight.size()   )   )   ) - axis_set
         __a					=  pytorch_quantization.utils.reduce_amax(mod.weight		,				axis=lowerCAmelCase__		,				keepdims=lowerCAmelCase__   ).detach()
         logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'''   )
         __a					=  amax




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=25		,				lowerCAmelCase__=180		,				lowerCAmelCase__=None   ):

   if ignore is None:
      __a					=  []
   elif not isinstance(lowerCAmelCase__		,				lowerCAmelCase__   ):
      __a					=  [ignore]

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

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




def  a   (lowerCAmelCase__   ):
   __a					=  0
   for name, mod in model.named_modules():
      if isinstance(lowerCAmelCase__		,				pytorch_quantization.nn.TensorQuantizer   ):
         print(f'''{name:80} {mod}'''   )
         count += 1
   print(f'''{count} TensorQuantizers found in model'''   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  getattr(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )
   if quantizer_mod is not None:
      assert hasattr(lowerCAmelCase__		,				lowerCAmelCase__   )
      setattr(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )
   else:
      logger.warning(f'''{name} has no {quantizer}'''   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__="both"		,				**lowerCAmelCase__   ):
   __a					=  f'''Warning: changing {which} quantizers of {name:{qname_width}}'''
   for k, v in kwargs.items():
      s += f''' {k}={v}'''
      if which in ["input", "both"]:
         set_quantizer(lowerCAmelCase__		,				lowerCAmelCase__		,				"""_input_quantizer"""		,				lowerCAmelCase__		,				lowerCAmelCase__   )
      if which in ["weight", "both"]:
         set_quantizer(lowerCAmelCase__		,				lowerCAmelCase__		,				"""_weight_quantizer"""		,				lowerCAmelCase__		,				lowerCAmelCase__   )
   logger.info(lowerCAmelCase__   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__   ):

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



