





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase__  :     Tuple   			=logging.get_logger(__name__)

UpperCAmelCase__  :     Optional[Any]   			={
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class  __A       (  a      ):
				__A								=			"""marian"""
				__A								=			["""past_key_values"""]
				__A								=			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


				def __init__(						self				,	UpperCAmelCase_=58101				,	UpperCAmelCase_=None				,	UpperCAmelCase_=1024				,	UpperCAmelCase_=12				,	UpperCAmelCase_=4096				,	UpperCAmelCase_=16				,	UpperCAmelCase_=12				,	UpperCAmelCase_=4096				,	UpperCAmelCase_=16				,	UpperCAmelCase_=0.0				,	UpperCAmelCase_=0.0				,	UpperCAmelCase_=True				,	UpperCAmelCase_=True				,	UpperCAmelCase_="gelu"				,	UpperCAmelCase_=1024				,	UpperCAmelCase_=0.1				,	UpperCAmelCase_=0.0				,	UpperCAmelCase_=0.0				,	UpperCAmelCase_=0.0_2				,	UpperCAmelCase_=58100				,	UpperCAmelCase_=False				,	UpperCAmelCase_=58100				,	UpperCAmelCase_=0				,	UpperCAmelCase_=0				,	UpperCAmelCase_=True				,	**UpperCAmelCase_				,	):
								lowerCamelCase			=vocab_size
								lowerCamelCase			=decoder_vocab_size or vocab_size
								lowerCamelCase			=max_position_embeddings
								lowerCamelCase			=d_model
								lowerCamelCase			=encoder_ffn_dim
								lowerCamelCase			=encoder_layers
								lowerCamelCase			=encoder_attention_heads
								lowerCamelCase			=decoder_ffn_dim
								lowerCamelCase			=decoder_layers
								lowerCamelCase			=decoder_attention_heads
								lowerCamelCase			=dropout
								lowerCamelCase			=attention_dropout
								lowerCamelCase			=activation_dropout
								lowerCamelCase			=activation_function
								lowerCamelCase			=init_std
								lowerCamelCase			=encoder_layerdrop
								lowerCamelCase			=decoder_layerdrop
								lowerCamelCase			=use_cache
								lowerCamelCase			=encoder_layers
								lowerCamelCase			=scale_embedding  # scale factor will be sqrt(d_model) if True
								lowerCamelCase			=share_encoder_decoder_embeddings
								super().__init__(
								    pad_token_id=UpperCAmelCase_				,	eos_token_id=UpperCAmelCase_				,	is_encoder_decoder=UpperCAmelCase_				,	decoder_start_token_id=UpperCAmelCase_				,	forced_eos_token_id=UpperCAmelCase_				,	**UpperCAmelCase_				,	)




class  __A       (  a      ):


				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def _snake_case    (						self							):
								if self.task in ["default", "seq2seq-lm"]:
												lowerCamelCase			=OrderedDict(
												    [
												        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
												        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
												    ]							)

												if self.use_past:
																lowerCamelCase			={0: """batch"""}
																lowerCamelCase			={0: """batch""", 1: """past_decoder_sequence + sequence"""}
												else:
																lowerCamelCase			={0: """batch""", 1: """decoder_sequence"""}
																lowerCamelCase			={0: """batch""", 1: """decoder_sequence"""}

												if self.use_past:
																self.fill_with_past_key_values_(UpperCAmelCase_				,	direction="""inputs"""							)
								elif self.task == "causal-lm":
												# TODO: figure this case out.
												lowerCamelCase			=OrderedDict(
												    [
												        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
												        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
												    ]							)
												if self.use_past:
																lowerCamelCase	,					lowerCamelCase			=self.num_layers
																for i in range(UpperCAmelCase_							):
																				lowerCamelCase			={0: """batch""", 2: """past_sequence + sequence"""}
																				lowerCamelCase			={0: """batch""", 2: """past_sequence + sequence"""}
								else:
												lowerCamelCase			=OrderedDict(
												    [
												        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
												        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
												        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
												        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
												    ]							)

								return common_inputs


				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def _snake_case    (						self							):
								if self.task in ["default", "seq2seq-lm"]:
												lowerCamelCase			=super().outputs
								else:
												lowerCamelCase			=super(UpperCAmelCase_				,	self							).outputs
												if self.use_past:
																lowerCamelCase	,					lowerCamelCase			=self.num_layers
																for i in range(UpperCAmelCase_							):
																				lowerCamelCase			={0: """batch""", 2: """past_sequence + sequence"""}
																				lowerCamelCase			={0: """batch""", 2: """past_sequence + sequence"""}
								return common_outputs


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = -1				,	UpperCAmelCase_ = -1				,	UpperCAmelCase_ = False				,	UpperCAmelCase_ = None				,	):
								lowerCamelCase			=self._generate_dummy_inputs_for_encoder_and_decoder(
								    UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							)

								# Generate decoder inputs
								lowerCamelCase			=seq_length if not self.use_past else 1
								lowerCamelCase			=self._generate_dummy_inputs_for_encoder_and_decoder(
								    UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							)
								lowerCamelCase			={f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
								lowerCamelCase			=dict(**UpperCAmelCase_				,	**UpperCAmelCase_							)

								if self.use_past:
												if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
												else:
																import torch
												lowerCamelCase	,					lowerCamelCase			=common_inputs["""input_ids"""].shape
												lowerCamelCase			=common_inputs["""decoder_input_ids"""].shape[1]
												lowerCamelCase	,					lowerCamelCase			=self.num_attention_heads
												lowerCamelCase			=(
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												lowerCamelCase			=decoder_seq_length + 3
												lowerCamelCase			=(
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												lowerCamelCase			=torch.cat(
												    [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCAmelCase_				,	UpperCAmelCase_							)]				,	dim=1							)

												lowerCamelCase			=[]
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												lowerCamelCase	,					lowerCamelCase			=self.num_layers
												lowerCamelCase			=min(UpperCAmelCase_				,	UpperCAmelCase_							)
												lowerCamelCase			=max(UpperCAmelCase_				,	UpperCAmelCase_							) - min_num_layers
												lowerCamelCase			="""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

												for _ in range(UpperCAmelCase_							):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(UpperCAmelCase_							),
																        torch.zeros(UpperCAmelCase_							),
																        torch.zeros(UpperCAmelCase_							),
																        torch.zeros(UpperCAmelCase_							),
																    )							)
												# TODO: test this.
												lowerCamelCase			=encoder_shape if remaining_side_name == """encoder""" else decoder_shape
												for _ in range(UpperCAmelCase_				,	UpperCAmelCase_							):
																common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							))							)
								return common_inputs


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = -1				,	UpperCAmelCase_ = -1				,	UpperCAmelCase_ = False				,	UpperCAmelCase_ = None				,	):
								lowerCamelCase			=self._generate_dummy_inputs_for_encoder_and_decoder(
								    UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							)

								if self.use_past:
												if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
												else:
																import torch
												lowerCamelCase	,					lowerCamelCase			=common_inputs["""input_ids"""].shape
												# Not using the same length for past_key_values
												lowerCamelCase			=seqlen + 2
												lowerCamelCase	,					lowerCamelCase			=self.num_layers
												lowerCamelCase	,					lowerCamelCase			=self.num_attention_heads
												lowerCamelCase			=(
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												lowerCamelCase			=common_inputs["""attention_mask"""].dtype
												lowerCamelCase			=torch.cat(
												    [common_inputs["""attention_mask"""], torch.ones(UpperCAmelCase_				,	UpperCAmelCase_				,	dtype=UpperCAmelCase_							)]				,	dim=1							)
												lowerCamelCase			=[
												    (torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							)) for _ in range(UpperCAmelCase_							)
												]
								return common_inputs


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = -1				,	UpperCAmelCase_ = -1				,	UpperCAmelCase_ = False				,	UpperCAmelCase_ = None				,	):
								# Copied from OnnxConfig.generate_dummy_inputs
								# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
								# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
								lowerCamelCase			=compute_effective_axis_dimension(
								    UpperCAmelCase_				,	fixed_dimension=OnnxConfig.default_fixed_batch				,	num_token_to_add=0							)

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								lowerCamelCase			=tokenizer.num_special_tokens_to_add(UpperCAmelCase_							)
								lowerCamelCase			=compute_effective_axis_dimension(
								    UpperCAmelCase_				,	fixed_dimension=OnnxConfig.default_fixed_sequence				,	num_token_to_add=UpperCAmelCase_							)

								# Generate dummy inputs according to compute batch and sequence
								lowerCamelCase			=[""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
								lowerCamelCase			=dict(tokenizer(UpperCAmelCase_				,	return_tensors=UpperCAmelCase_							)							)
								return common_inputs


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = -1				,	UpperCAmelCase_ = -1				,	UpperCAmelCase_ = False				,	UpperCAmelCase_ = None				,	):
								if self.task in ["default", "seq2seq-lm"]:
												lowerCamelCase			=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    UpperCAmelCase_				,	batch_size=UpperCAmelCase_				,	seq_length=UpperCAmelCase_				,	is_pair=UpperCAmelCase_				,	framework=UpperCAmelCase_							)

								else:
												lowerCamelCase			=self._generate_dummy_inputs_for_causal_lm(
												    UpperCAmelCase_				,	batch_size=UpperCAmelCase_				,	seq_length=UpperCAmelCase_				,	is_pair=UpperCAmelCase_				,	framework=UpperCAmelCase_							)

								return common_inputs


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								if self.task in ["default", "seq2seq-lm"]:
												lowerCamelCase			=super()._flatten_past_key_values_(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							)
								else:
												lowerCamelCase			=super(UpperCAmelCase_				,	self							)._flatten_past_key_values_(
												    UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							)


				@property
				def _snake_case    (						self							):
								return 1E-4












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			)





