





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase__  :     Optional[int]   			=logging.getLogger(__name__)
UpperCAmelCase__  :     Optional[int]   			=list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase__  :     List[Any]   			=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class  __A       :
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={
				        """help""": (
				            """The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."""
				        )
				    }      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__SCREAMING_SNAKE_CASE      )}      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={
				        """help""": (
				            """Override some existing default config settings when a model is trained from scratch. Example: """
				            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
				        )
				    }      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}      ,   )
				__A								=			field(
				    default="""main"""      ,   metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }      ,   )


				def _snake_case    (						self							):

								if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
												raise ValueError(
												    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""							)




@dataclass
class  __A       :
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}      )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
				__A								=			field(default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """The input training data file (a text file)."""}      )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )
				__A								=			field(
				    default=5      ,   metadata={
				        """help""": """The percentage of the train set used as validation set in case there\'s no validation split"""
				    }      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated. Default to the max input length of the model."""
				        )
				    }      ,   )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={"""help""": """The number of processes to use for the preprocessing."""}      ,   )
				__A								=			field(
				    default=0.1_5      ,   metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}      )
				__A								=			field(
				    default=__SCREAMING_SNAKE_CASE      ,   metadata={
				        """help""": (
				            """Whether to pad all samples to `max_seq_length`. """
				            """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
				        )
				    }      ,   )


				def _snake_case    (						self							):

								if self.train_file is not None:
												lowerCamelCase			=self.train_file.split("""."""							)[-1]
												assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
								if self.validation_file is not None:
												lowerCamelCase			=self.validation_file.split("""."""							)[-1]
												assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					int:
				with open(__snake_case     , """r"""     , encoding="""utf-8"""			) as f:
								lowerCamelCase			=[json.loads(__snake_case			) for line in f.read().splitlines() if (len(__snake_case			) > 0 and not line.isspace())]
				assert len(__snake_case			) == len(__snake_case			)

				lowerCamelCase			={c: dataset[c] for c in dataset.column_names}
				lowerCamelCase			=refs
				return Dataset.from_dict(__snake_case			)





def        _lowercase   (  )  ->					Tuple:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowerCamelCase			=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
				if len(sys.argv			) == 2 and sys.argv[1].endswith(""".json"""			):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowerCamelCase			=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
				else:
								lowerCamelCase			=parser.parse_args_into_dataclasses()

				# Detecting last checkpoint.
				lowerCamelCase			=None
				if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
								lowerCamelCase			=get_last_checkpoint(training_args.output_dir			)
								if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
												raise ValueError(
												    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
												    """Use --overwrite_output_dir to overcome."""			)
								elif last_checkpoint is not None:
												logger.info(
												    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
												    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""			)

    # Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""     , datefmt="""%m/%d/%Y %H:%M:%S"""     , handlers=[logging.StreamHandler(sys.stdout			)]     , )
				logger.setLevel(logging.INFO if is_main_process(training_args.local_rank			) else logging.WARN			)

				# Log on each process the small summary:
				logger.warning(
				    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
				    + F"""distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}"""			)
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank			):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""     , __snake_case			)

				# Set seed before initializing model.
				set_seed(training_args.seed			)

				# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
				# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
				# (the dataset will be downloaded automatically from the datasets Hub).
				#
				# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
				# 'text' is found. You can easily tweak this behavior (see below).
				#
				# In distributed training, the load_dataset function guarantee that only one local process can concurrently
				# download the dataset.
				if data_args.dataset_name is not None:
								# Downloading and loading a dataset from the hub.
								lowerCamelCase			=load_dataset(data_args.dataset_name     , data_args.dataset_config_name			)
								if "validation" not in datasets.keys():
												lowerCamelCase			=load_dataset(
												    data_args.dataset_name     , data_args.dataset_config_name     , split=F"""train[:{data_args.validation_split_percentage}%]"""     , )
												lowerCamelCase			=load_dataset(
												    data_args.dataset_name     , data_args.dataset_config_name     , split=F"""train[{data_args.validation_split_percentage}%:]"""     , )
				else:
								lowerCamelCase			={}
								if data_args.train_file is not None:
												lowerCamelCase			=data_args.train_file
								if data_args.validation_file is not None:
												lowerCamelCase			=data_args.validation_file
								lowerCamelCase			=data_args.train_file.split("""."""			)[-1]
								if extension == "txt":
												lowerCamelCase			="text"
								lowerCamelCase			=load_dataset(__snake_case     , data_files=__snake_case			)
				# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
				# https://huggingface.co/docs/datasets/loading_datasets.html.

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				lowerCamelCase			={
				    "cache_dir": model_args.cache_dir,
				    "revision": model_args.model_revision,
				    "use_auth_token": True if model_args.use_auth_token else None,
				}
				if model_args.config_name:
								lowerCamelCase			=AutoConfig.from_pretrained(model_args.config_name     , **__snake_case			)
				elif model_args.model_name_or_path:
								lowerCamelCase			=AutoConfig.from_pretrained(model_args.model_name_or_path     , **__snake_case			)
				else:
								lowerCamelCase			=CONFIG_MAPPING[model_args.model_type]()
								logger.warning("""You are instantiating a new config instance from scratch."""			)
								if model_args.config_overrides is not None:
												logger.info(F"""Overriding config: {model_args.config_overrides}"""			)
												config.update_from_string(model_args.config_overrides			)
												logger.info(F"""New config: {config}"""			)

				lowerCamelCase			={
				    "cache_dir": model_args.cache_dir,
				    "use_fast": model_args.use_fast_tokenizer,
				    "revision": model_args.model_revision,
				    "use_auth_token": True if model_args.use_auth_token else None,
				}
				if model_args.tokenizer_name:
								lowerCamelCase			=AutoTokenizer.from_pretrained(model_args.tokenizer_name     , **__snake_case			)
				elif model_args.model_name_or_path:
								lowerCamelCase			=AutoTokenizer.from_pretrained(model_args.model_name_or_path     , **__snake_case			)
				else:
								raise ValueError(
								    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
								    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""			)

				if model_args.model_name_or_path:
								lowerCamelCase			=AutoModelForMaskedLM.from_pretrained(
								    model_args.model_name_or_path     , from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)     , config=__snake_case     , cache_dir=model_args.cache_dir     , revision=model_args.model_revision     , use_auth_token=True if model_args.use_auth_token else None     , )
				else:
								logger.info("""Training new model from scratch"""			)
								lowerCamelCase			=AutoModelForMaskedLM.from_config(__snake_case			)

				model.resize_token_embeddings(len(__snake_case			)			)

				# Preprocessing the datasets.
				# First we tokenize all the texts.
				if training_args.do_train:
								lowerCamelCase			=datasets["train"].column_names
				else:
								lowerCamelCase			=datasets["validation"].column_names
				lowerCamelCase			="text" if "text" in column_names else column_names[0]

				lowerCamelCase			="max_length" if data_args.pad_to_max_length else False

				def tokenize_function(_UpperCAmelCase			):
								# Remove empty lines
								lowerCamelCase			=[line for line in examples["text"] if len(__snake_case			) > 0 and not line.isspace()]
								return tokenizer(examples["""text"""]     , padding=__snake_case     , truncation=__snake_case     , max_length=data_args.max_seq_length			)

				lowerCamelCase			=datasets.map(
				    __snake_case     , batched=__snake_case     , num_proc=data_args.preprocessing_num_workers     , remove_columns=[text_column_name]     , load_from_cache_file=not data_args.overwrite_cache     , )

				# Add the chinese references if provided
				if data_args.train_ref_file is not None:
								lowerCamelCase			=add_chinese_references(tokenized_datasets["""train"""]     , data_args.train_ref_file			)
				if data_args.validation_ref_file is not None:
								lowerCamelCase			=add_chinese_references(
								    tokenized_datasets["""validation"""]     , data_args.validation_ref_file			)
				# If we have ref files, need to avoid it removed by trainer
				lowerCamelCase			=data_args.train_ref_file or data_args.validation_ref_file
				if has_ref:
								lowerCamelCase			=False

				# Data collator
				# This one will take care of randomly masking the tokens.
				lowerCamelCase			=DataCollatorForWholeWordMask(tokenizer=__snake_case     , mlm_probability=data_args.mlm_probability			)

				# Initialize our Trainer
				lowerCamelCase			=Trainer(
				    model=__snake_case     , args=__snake_case     , train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None     , eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None     , tokenizer=__snake_case     , data_collator=__snake_case     , )

				# Training
				if training_args.do_train:
								if last_checkpoint is not None:
												lowerCamelCase			=last_checkpoint
								elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path			):
												lowerCamelCase			=model_args.model_name_or_path
								else:
												lowerCamelCase			=None
								lowerCamelCase			=trainer.train(resume_from_checkpoint=__snake_case			)
								trainer.save_model()  # Saves the tokenizer too for easy upload

								lowerCamelCase			=os.path.join(training_args.output_dir     , """train_results.txt"""			)
								if trainer.is_world_process_zero():
												with open(__snake_case     , """w"""			) as writer:
																logger.info("""***** Train results *****"""			)
																for key, value in sorted(train_result.metrics.items()			):
																				logger.info(F"""  {key} = {value}"""			)
																				writer.write(F"""{key} = {value}\n"""			)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
												trainer.state.save_to_json(os.path.join(training_args.output_dir     , """trainer_state.json"""			)			)

    # Evaluation
				lowerCamelCase			={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""			)

								lowerCamelCase			=trainer.evaluate()

								lowerCamelCase			=math.exp(eval_output["""eval_loss"""]			)
								lowerCamelCase			=perplexity

								lowerCamelCase			=os.path.join(training_args.output_dir     , """eval_results_mlm_wwm.txt"""			)
								if trainer.is_world_process_zero():
												with open(__snake_case     , """w"""			) as writer:
																logger.info("""***** Eval results *****"""			)
																for key, value in sorted(results.items()			):
																				logger.info(F"""  {key} = {value}"""			)
																				writer.write(F"""{key} = {value}\n"""			)

				return results





def        _lowercase   (  _UpperCAmelCase			)  ->					Tuple:
				# For xla_spawn (TPUs)
				main()


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


UpperCAmelCase__  :     Optional[int]   			=logging.getLogger(__name__)

UpperCAmelCase__  :     Tuple   			=50  # max width of layer names
UpperCAmelCase__  :     List[str]   			=70  # max width of quantizer names





def        _lowercase   (  _UpperCAmelCase			)  ->					List[str]:
				lowerCamelCase			=parser.add_argument_group("""quant_trainer arguments"""			)
				group.add_argument("""--wprec"""     , type=_UpperCAmelCase     , default=8     , help="""weight precision"""			)
				group.add_argument("""--aprec"""     , type=_UpperCAmelCase     , 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=_UpperCAmelCase     , nargs="""+"""     , help="""disable quantizers by keyword"""			)
				group.add_argument("""--quant-disable-layer-module"""     , type=_UpperCAmelCase     , help="""disable quantizers by keyword under layer."""			)
				group.add_argument("""--quant-enable-layer-module"""     , type=_UpperCAmelCase     , 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=_UpperCAmelCase     , type=_UpperCAmelCase     , 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=_UpperCAmelCase     , 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        _lowercase   (  _UpperCAmelCase			)  ->					Dict:

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

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





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=False     , _UpperCAmelCase=False			)  ->					int:
				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(_UpperCAmelCase     , ["""embeddings"""]     , which="""weight"""     , _disabled=_UpperCAmelCase			)

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

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

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

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

								if args.recalibrate_weights:
												recalibrate_weights(_UpperCAmelCase			)

								if args.fuse_qkv:
												fuse_qkv(_UpperCAmelCase     , _UpperCAmelCase			)

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

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





def        _lowercase   (  _UpperCAmelCase			)  ->					Optional[Any]:
				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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[int]:
				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(_UpperCAmelCase			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Tuple:

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

								lowerCamelCase			=max(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)
								qq._amax.fill_(_UpperCAmelCase			)
								qk._amax.fill_(_UpperCAmelCase			)
								qv._amax.fill_(_UpperCAmelCase			)
								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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					int:

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





def        _lowercase   (  _UpperCAmelCase			)  ->					Dict:

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





def        _lowercase   (  _UpperCAmelCase			)  ->					List[str]:

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





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase=25     , _UpperCAmelCase=1_80     , _UpperCAmelCase=None			)  ->					Dict:

				if ignore is None:
								lowerCamelCase			=[]
				elif not isinstance(_UpperCAmelCase     , _UpperCAmelCase			):
								lowerCamelCase			=[ignore]

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

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





def        _lowercase   (  _UpperCAmelCase			)  ->					Dict:
				lowerCamelCase			=0
				for name, mod in model.named_modules():
								if isinstance(_UpperCAmelCase     , pytorch_quantization.nn.TensorQuantizer			):
												print(F"""{name:80} {mod}"""			)
												count += 1
				print(F"""{count} TensorQuantizers found in model"""			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Union[str, Any]:
				lowerCamelCase			=getattr(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)
				if quantizer_mod is not None:
								assert hasattr(_UpperCAmelCase     , _UpperCAmelCase			)
								setattr(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)
				else:
								logger.warning(F"""{name} has no {quantizer}"""			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="both"     , **_UpperCAmelCase			)  ->					List[str]:
				lowerCamelCase			=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(_UpperCAmelCase     , _UpperCAmelCase     , """_input_quantizer"""     , _UpperCAmelCase     , _UpperCAmelCase			)
								if which in ["weight", "both"]:
												set_quantizer(_UpperCAmelCase     , _UpperCAmelCase     , """_weight_quantizer"""     , _UpperCAmelCase     , _UpperCAmelCase			)
				logger.info(_UpperCAmelCase			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , **_UpperCAmelCase			)  ->					int:

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





