






'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class      lowercase__     :
	a_         =PegasusConfig
	a_         ={}
	a_         ="""gelu"""

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=7		,    __UpperCAmelCase=True		,    __UpperCAmelCase=False		,    __UpperCAmelCase=99		,    __UpperCAmelCase=32		,    __UpperCAmelCase=2		,    __UpperCAmelCase=4		,    __UpperCAmelCase=37		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=40		,    __UpperCAmelCase=2		,    __UpperCAmelCase=1		,    __UpperCAmelCase=0		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						seq_length
							lowerCAmelCase__       =						is_training
							lowerCAmelCase__       =						use_labels
							lowerCAmelCase__       =						vocab_size
							lowerCAmelCase__       =						hidden_size
							lowerCAmelCase__       =						num_hidden_layers
							lowerCAmelCase__       =						num_attention_heads
							lowerCAmelCase__       =						intermediate_size

							lowerCAmelCase__       =						hidden_dropout_prob
							lowerCAmelCase__       =						attention_probs_dropout_prob
							lowerCAmelCase__       =						max_position_embeddings
							lowerCAmelCase__       =						eos_token_id
							lowerCAmelCase__       =						pad_token_id
							lowerCAmelCase__       =						bos_token_id

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						ids_tensor([self.batch_size, self.seq_length - 1]		,    self.vocab_size       )
							lowerCAmelCase__       =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )		,    1       )
							lowerCAmelCase__       =						tf.concat([input_ids, eos_tensor]		,    axis=1       )

							lowerCAmelCase__       =						ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size       )

							lowerCAmelCase__       =						self.config_cls(
							    vocab_size=self.vocab_size		,    d_model=self.hidden_size		,    encoder_layers=self.num_hidden_layers		,    decoder_layers=self.num_hidden_layers		,    encoder_attention_heads=self.num_attention_heads		,    decoder_attention_heads=self.num_attention_heads		,    encoder_ffn_dim=self.intermediate_size		,    decoder_ffn_dim=self.intermediate_size		,    dropout=self.hidden_dropout_prob		,    attention_dropout=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    eos_token_ids=[2]		,    bos_token_id=self.bos_token_id		,    pad_token_id=self.pad_token_id		,    decoder_start_token_id=self.pad_token_id		,    **self.config_updates		,    )
							lowerCAmelCase__       =						prepare_pegasus_inputs_dict(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )
							return config, inputs_dict


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						TFPegasusModel(config=__UpperCAmelCase       ).get_decoder()
							lowerCAmelCase__       =						inputs_dict["input_ids"]

							lowerCAmelCase__       =						input_ids[:1, :]
							lowerCAmelCase__       =						inputs_dict["attention_mask"][:1, :]
							lowerCAmelCase__       =						inputs_dict["head_mask"]
							lowerCAmelCase__       =						1

							# first forward pass
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    head_mask=__UpperCAmelCase		,    use_cache=__UpperCAmelCase       )

							lowerCAmelCase__      ,							lowerCAmelCase__       =						outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase__       =						ids_tensor((self.batch_size, 3)		,    config.vocab_size       )
							lowerCAmelCase__       =						tf.cast(ids_tensor((self.batch_size, 3)		,    2       )		,    tf.inta       )

							# append to next input_ids and
							lowerCAmelCase__       =						tf.concat([input_ids, next_tokens]		,    axis=-1       )
							lowerCAmelCase__       =						tf.concat([attention_mask, next_attn_mask]		,    axis=-1       )

							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase       )[0]
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    attention_mask=__UpperCAmelCase		,    past_key_values=__UpperCAmelCase       )[0]

							self.parent.assertEqual(next_tokens.shape[1]		,    output_from_past.shape[1]       )

							# select random slice
							lowerCAmelCase__       =						int(ids_tensor((1,)		,    output_from_past.shape[-1]       )       )
							lowerCAmelCase__       =						output_from_no_past[:, -3:, random_slice_idx]
							lowerCAmelCase__       =						output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__UpperCAmelCase		,    __UpperCAmelCase		,    rtol=1E-3       )





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







						if attention_mask is None:
												lowerCAmelCase__       =						tf.cast(tf.math.not_equal(UpperCamelCase_    ,   config.pad_token_id   )    ,   tf.inta   )
						if decoder_attention_mask is None:
												lowerCAmelCase__       =						tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape    ,   dtype=tf.inta   ),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,   config.pad_token_id   )    ,   tf.inta   ),
												    ]    ,   axis=-1    ,   )
						if head_mask is None:
												lowerCAmelCase__       =						tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
						if decoder_head_mask is None:
												lowerCAmelCase__       =						tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
						if cross_attn_head_mask is None:
												lowerCAmelCase__       =						tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}


@require_tf
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
	a_         =(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
	a_         =(
	    {
	        """conversational""": TFPegasusForConditionalGeneration,
	        """feature-extraction""": TFPegasusModel,
	        """summarization""": TFPegasusForConditionalGeneration,
	        """text2text-generation""": TFPegasusForConditionalGeneration,
	        """translation""": TFPegasusForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	a_         =True
	a_         =False
	a_         =False

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						TFPegasusModelTester(self       )
							lowerCAmelCase__       =						ConfigTester(self		,    config_class=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							self.config_tester.run_common_tests()


	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase       )





@require_sentencepiece
@require_tokenizers
@require_tf
class      lowercase__     (			unittest.TestCase	):
	a_         =[
	    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
	    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
	]
	a_         =[
	    """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
	    """ reduce the risk of wildfires.""",
	    """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
	]  # differs slightly from pytorch, likely due to numerical differences in linear layers
	a_         ="""google/pegasus-xsum"""

	@cached_property
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							return AutoTokenizer.from_pretrained(self.model_name       )

	@cached_property
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
							return model

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.translate_src_text(**__UpperCAmelCase       )
							assert self.expected_text == generated_words

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						self.tokenizer(self.src_text		,    **__UpperCAmelCase		,    padding=__UpperCAmelCase		,    return_tensors="tf"       )
							lowerCAmelCase__       =						self.model.generate(
							    model_inputs.input_ids		,    attention_mask=model_inputs.attention_mask		,    num_beams=2		,    use_cache=__UpperCAmelCase		,    )
							lowerCAmelCase__       =						self.tokenizer.batch_decode(generated_ids.numpy()		,    skip_special_tokens=__UpperCAmelCase       )
							return generated_words


	@slow
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							self._assert_generated_batch_equal_expected()














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_   )






