






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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


a_							=    logging.getLogger(__name__)


@dataclass
class      lowercase__     :
	a_         =field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
	a_         =field(
	    default=_UpperCAmelCase,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
	a_         =field(
	    default=_UpperCAmelCase,					metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}	)
	a_         =field(
	    default=_UpperCAmelCase,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},					)
	a_         =field(default=_UpperCAmelCase,					metadata={"""help""": """Whether tp freeze the encoder."""}	)
	a_         =field(default=_UpperCAmelCase,					metadata={"""help""": """Whether  to freeze the embeddings."""}	)





@dataclass
class      lowercase__     :
	a_         =field(
	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}	)
	a_         =field(
	    default="""summarization""",					metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""},					)
	a_         =field(
	    default=1_024,					metadata={
	        """help""": (
	            """The maximum total input sequence length after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    },					)
	a_         =field(
	    default=128,					metadata={
	        """help""": (
	            """The maximum total sequence length for target text after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    },					)
	a_         =field(
	    default=142,					metadata={
	        """help""": (
	            """The maximum total sequence length for validation target text after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded. """
	            """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
	            """during ``evaluate`` and ``predict``."""
	        )
	    },					)
	a_         =field(
	    default=142,					metadata={
	        """help""": (
	            """The maximum total sequence length for test target text after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    },					)
	a_         =field(default=-1,					metadata={"""help""": """# training examples. -1 means use all."""}	)
	a_         =field(default=-1,					metadata={"""help""": """# validation examples. -1 means use all."""}	)
	a_         =field(default=-1,					metadata={"""help""": """# test examples. -1 means use all."""}	)
	a_         =field(default=_UpperCAmelCase,					metadata={"""help""": """Source language id for translation."""}	)
	a_         =field(default=_UpperCAmelCase,					metadata={"""help""": """Target language id for translation."""}	)
	a_         =field(default=_UpperCAmelCase,					metadata={"""help""": """# num_beams to use for evaluation."""}	)
	a_         =field(
	    default=_UpperCAmelCase,					metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""},					)





def        _a  (     UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				Optional[Any]   ) ->   int:
						"""simple docstring"""







						logger.info(F"***** {split} metrics *****"   )
						for key in sorted(metrics.keys()   ):
												logger.info(F"  {key} = {metrics[key]}"   )
						save_json(UpperCamelCase_    ,   os.path.join(UpperCamelCase_    ,   F"{split}_results.json"   )   )


def        _a  (     ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

						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__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
						else:
												lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						parser.parse_args_into_dataclasses()

						check_output_dir(UpperCamelCase_   )

						# Setup logging
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,   datefmt="%m/%d/%Y %H:%M:%S"    ,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,   )
						logger.warning(
						    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,   training_args.local_rank    ,   training_args.device    ,   training_args.n_gpu    ,   bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )    ,   training_args.fpaa    ,   )
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
						# 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()
						logger.info("Training/evaluation parameters %s"    ,   UpperCamelCase_   )

						# Set seed
						set_seed(training_args.seed   )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowerCAmelCase__       =						AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,   cache_dir=model_args.cache_dir    ,   )

						lowerCAmelCase__       =						("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
						for p in extra_model_params:
												if getattr(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   ):
																		assert hasattr(UpperCamelCase_    ,   UpperCamelCase_   ), F"({config.__class__.__name__}) doesn't have a `{p}` attribute"
																		setattr(UpperCamelCase_    ,   UpperCamelCase_    ,   getattr(UpperCamelCase_    ,   UpperCamelCase_   )   )

						lowerCAmelCase__       =						AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,   cache_dir=model_args.cache_dir    ,   )
						lowerCAmelCase__       =						AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path    ,   from_tf=".ckpt" in model_args.model_name_or_path    ,   config=UpperCamelCase_    ,   cache_dir=model_args.cache_dir    ,   )

						# use task specific params
						use_task_specific_params(UpperCamelCase_    ,   data_args.task   )

						# set num_beams for evaluation
						if data_args.eval_beams is None:
												lowerCAmelCase__       =						model.config.num_beams

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(UpperCamelCase_    ,   (MBartTokenizer, MBartTokenizerFast)   ):
												assert (
												    data_args.tgt_lang is not None and data_args.src_lang is not None
												), "mBart requires --tgt_lang and --src_lang"
												if isinstance(UpperCamelCase_    ,   UpperCamelCase_   ):
																		lowerCAmelCase__       =						tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		lowerCAmelCase__       =						tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

						if model_args.freeze_embeds:
												freeze_embeds(UpperCamelCase_   )
						if model_args.freeze_encoder:
												freeze_params(model.get_encoder()   )
												assert_all_frozen(model.get_encoder()   )

						lowerCAmelCase__       =						SeqaSeqDataset

						# Get datasets
						lowerCAmelCase__       =						(
						    dataset_class(
						        UpperCamelCase_    ,   type_path="train"    ,   data_dir=data_args.data_dir    ,   n_obs=data_args.n_train    ,   max_target_length=data_args.max_target_length    ,   max_source_length=data_args.max_source_length    ,   prefix=model.config.prefix or ""    ,   )
						    if training_args.do_train
						    else None
						)
						lowerCAmelCase__       =						(
						    dataset_class(
						        UpperCamelCase_    ,   type_path="val"    ,   data_dir=data_args.data_dir    ,   n_obs=data_args.n_val    ,   max_target_length=data_args.val_max_target_length    ,   max_source_length=data_args.max_source_length    ,   prefix=model.config.prefix or ""    ,   )
						    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
						    else None
						)
						lowerCAmelCase__       =						(
						    dataset_class(
						        UpperCamelCase_    ,   type_path="test"    ,   data_dir=data_args.data_dir    ,   n_obs=data_args.n_test    ,   max_target_length=data_args.test_max_target_length    ,   max_source_length=data_args.max_source_length    ,   prefix=model.config.prefix or ""    ,   )
						    if training_args.do_predict
						    else None
						)

						# Initialize our Trainer
						lowerCAmelCase__       =						(
						    build_compute_metrics_fn(data_args.task    ,   UpperCamelCase_   ) if training_args.predict_with_generate else None
						)
						lowerCAmelCase__       =						SeqaSeqTrainer(
						    model=UpperCamelCase_    ,   args=UpperCamelCase_    ,   data_args=UpperCamelCase_    ,   train_dataset=UpperCamelCase_    ,   eval_dataset=UpperCamelCase_    ,   data_collator=SeqaSeqDataCollator(
						        UpperCamelCase_    ,   UpperCamelCase_    ,   model.config.decoder_start_token_id    ,   training_args.tpu_num_cores   )    ,   compute_metrics=UpperCamelCase_    ,   tokenizer=UpperCamelCase_    ,   )

						lowerCAmelCase__       =						{}
						# Training
						if training_args.do_train:
												logger.info("*** Train ***"   )

												lowerCAmelCase__       =						trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
												lowerCAmelCase__       =						train_result.metrics
												lowerCAmelCase__       =						data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics("train"    ,   UpperCamelCase_    ,   training_args.output_dir   )
																		all_metrics.update(UpperCamelCase_   )

																		# 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"   )   )

																		# For convenience, we also re-save the tokenizer to the same directory,
																		# so that you can share your model easily on huggingface.co/models =)
																		tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
						if training_args.do_eval:
												logger.info("*** Evaluate ***"   )

												lowerCAmelCase__       =						trainer.evaluate(metric_key_prefix="val"   )
												lowerCAmelCase__       =						data_args.n_val
												lowerCAmelCase__       =						round(metrics["val_loss"]    ,   4   )

												if trainer.is_world_process_zero():
																		handle_metrics("val"    ,   UpperCamelCase_    ,   training_args.output_dir   )
																		all_metrics.update(UpperCamelCase_   )

						if training_args.do_predict:
												logger.info("*** Predict ***"   )

												lowerCAmelCase__       =						trainer.predict(test_dataset=UpperCamelCase_    ,   metric_key_prefix="test"   )
												lowerCAmelCase__       =						test_output.metrics
												lowerCAmelCase__       =						data_args.n_test

												if trainer.is_world_process_zero():
																		lowerCAmelCase__       =						round(metrics["test_loss"]    ,   4   )
																		handle_metrics("test"    ,   UpperCamelCase_    ,   training_args.output_dir   )
																		all_metrics.update(UpperCamelCase_   )

																		if training_args.predict_with_generate:
																								lowerCAmelCase__       =						tokenizer.batch_decode(
																								    test_output.predictions    ,   skip_special_tokens=UpperCamelCase_    ,   clean_up_tokenization_spaces=UpperCamelCase_   )
																								lowerCAmelCase__       =						lmap(str.strip    ,   UpperCamelCase_   )
																								write_txt_file(UpperCamelCase_    ,   os.path.join(training_args.output_dir    ,   "test_generations.txt"   )   )

						if trainer.is_world_process_zero():
												save_json(UpperCamelCase_    ,   os.path.join(training_args.output_dir    ,   "all_results.json"   )   )

						return all_metrics


def        _a  (     UpperCamelCase_					:				str   ) ->   List[Any]:
						"""simple docstring"""







						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


a_							=    logging.getLogger(__name__)

a_							=    50  # max width of layer names
a_							=    70  # max width of quantizer names


def        _a  (     UpperCamelCase_					:				str   ) ->   List[str]:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				int   ) ->   List[Any]:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Any=False    ,   UpperCamelCase_					:				List[Any]=False   ) ->   Any:
						"""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(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        _a  (     UpperCamelCase_					:				Union[str, Any]   ) ->   Tuple:
						"""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  (     UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				Tuple   ) ->   str:
						"""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(UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				int   ) ->   int:
						"""simple docstring"""







						def fusea(UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Optional[int]   ):
												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        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[Any]   ) ->   Optional[int]:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				str   ) ->   int:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				Any   ) ->   Optional[int]:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Optional[Any]=25    ,   UpperCamelCase_					:				str=180    ,   UpperCamelCase_					:				Optional[int]=None   ) ->   Tuple:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				str   ) ->   Dict:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				int   ) ->   Tuple:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Union[str, Any]="both"    ,   **UpperCamelCase_					:				Dict   ) ->   Optional[int]:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				List[Any]    ,   **UpperCamelCase_					:				Any   ) ->   Any:
						"""simple docstring"""







						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_   )






