


'''simple docstring'''



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


_lowercase      :      List[Any]            =					logging.getLogger(__name__)

_lowercase      :      List[Any]            =					50  # max width of layer names
_lowercase      :      Tuple            =					70  # max width of quantizer names


def 				lowerCamelCase__				(	A       :      List[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     parser.add_argument_group('''quant_trainer arguments'''					)
						group.add_argument('''--wprec'''				,       type=A				,       default=8				,       help='''weight precision'''					)
						group.add_argument('''--aprec'''				,       type=A				,       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=A				,       nargs='''+'''				,       help='''disable quantizers by keyword'''					)
						group.add_argument('''--quant-disable-layer-module'''				,       type=A				,       help='''disable quantizers by keyword under layer.'''					)
						group.add_argument('''--quant-enable-layer-module'''				,       type=A				,       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=A				,       type=A				,       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=A				,       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 				lowerCamelCase__				(	A       :      int					):




						'''simple docstring'''






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

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


def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      Any				,       A       :      Union[str, Any]=False				,       A       :      int=False					):




						'''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(A				,       ['''embeddings''']				,       which='''weight'''				,       _disabled=A					)

												if args.quant_disable:
																		set_quantizer_by_name(A				,       ['''''']				,       _disabled=A					)

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

												if args.quant_disable_layer_module:
																		set_quantizer_by_name(A				,       [R'''layer.\d+.''' + args.quant_disable_layer_module]				,       _disabled=A					)

												if args.quant_enable_layer_module:
																		set_quantizer_by_name(A				,       [R'''layer.\d+.''' + args.quant_enable_layer_module]				,       _disabled=A					)

												if args.recalibrate_weights:
																		recalibrate_weights(A					)

												if args.fuse_qkv:
																		fuse_qkv(A				,       A					)

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

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


def 				lowerCamelCase__				(	A       :      Dict					):




						'''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 				lowerCamelCase__				(	A       :      Tuple				,       A       :      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(A					)


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[Any]					):




						'''simple docstring'''






						def fusea(A       :      Union[str, Any]				,       A       :      Optional[Any]				,       A       :      str					):
												for mod in [qq, qk, qv]:
																		if not hasattr(A				,       '''_amax'''					):
																								print('''          WARNING: NO AMAX BUFFER'''					)
																								return
												UpperCAmelCase					    =     qq._amax.detach().item()
												UpperCAmelCase					    =     qk._amax.detach().item()
												UpperCAmelCase					    =     qv._amax.detach().item()

												UpperCAmelCase					    =     max(A				,       A				,       A					)
												qq._amax.fill_(A					)
												qk._amax.fill_(A					)
												qv._amax.fill_(A					)
												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 				lowerCamelCase__				(	A       :      int				,       A       :      Tuple					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      Union[str, Any]					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Union[str, Any]=25				,       A       :      Tuple=1_80				,       A       :      List[Any]=None					):




						'''simple docstring'''






						if ignore is None:
												UpperCAmelCase					    =     []
						elif not isinstance(A				,       A					):
												UpperCAmelCase					    =     [ignore]

						UpperCAmelCase					    =     0
						for name, mod in model.named_modules():
												if not hasattr(A				,       '''weight'''					):
																		continue
												UpperCAmelCase					    =     max(A				,       len(A					)					)

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


def 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     0
						for name, mod in model.named_modules():
												if isinstance(A				,       pytorch_quantization.nn.TensorQuantizer					):
																		print(f"""{name:80} {mod}"""					)
																		count += 1
						print(f"""{count} TensorQuantizers found in model"""					)


def 				lowerCamelCase__				(	A       :      Any				,       A       :      Any				,       A       :      Dict				,       A       :      List[str]				,       A       :      Any					):




						'''simple docstring'''






						UpperCAmelCase					    =     getattr(A				,       A				,       A					)
						if quantizer_mod is not None:
												assert hasattr(A				,       A					)
												setattr(A				,       A				,       A					)
						else:
												logger.warning(f"""{name} has no {quantizer}"""					)


def 				lowerCamelCase__				(	A       :      Dict				,       A       :      List[str]				,       A       :      Optional[Any]="both"				,       **A       :      Optional[int]					):




						'''simple docstring'''






						UpperCAmelCase					    =     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(A				,       A				,       '''_input_quantizer'''				,       A				,       A					)
												if which in ["weight", "both"]:
																		set_quantizer(A				,       A				,       '''_weight_quantizer'''				,       A				,       A					)
						logger.info(A					)




def 				lowerCamelCase__				(	A       :      Any				,       A       :      Tuple				,       **A       :      Optional[int]					):




						'''simple docstring'''






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









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[str]            =					{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Dict            =					[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	_lowercase      :      Tuple            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





