



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class 	_snake_case							(			lowerCamelCase_      ):






   '''simple docstring'''






   A__	:      Union[List[PIL.Image.Image], np.ndarray]
   A__	:      Optional[List[bool]]


if is_transformers_available() and is_torch_available():
      from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline




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


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

snake_case							:       Optional[int]     			=      50  # max width of layer names
snake_case							:       Any     			=      70  # max width of quantizer names






def 							__lowercase   ( __lowerCAmelCase							:				Tuple   ):
							a__             =     parser.add_argument_group('quant_trainer arguments'   )
							group.add_argument('--wprec'					,    type=__lowerCAmelCase					,    default=8					,    help='weight precision'   )
							group.add_argument('--aprec'					,    type=__lowerCAmelCase					,    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=__lowerCAmelCase					,    nargs='+'					,    help='disable quantizers by keyword'   )
							group.add_argument('--quant-disable-layer-module'					,    type=__lowerCAmelCase					,    help='disable quantizers by keyword under layer.'   )
							group.add_argument('--quant-enable-layer-module'					,    type=__lowerCAmelCase					,    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=__lowerCAmelCase					,    type=__lowerCAmelCase					,    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=__lowerCAmelCase					,    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   ( __lowerCAmelCase							:				Union[str, Any]   ):

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

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






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Union[str, Any]=False					,    __lowerCAmelCase							:				Dict=False   ):
							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(__lowerCAmelCase					,    ['embeddings']					,    which='weight'					,    _disabled=__lowerCAmelCase   )

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

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

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

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

														if args.recalibrate_weights:
																					recalibrate_weights(__lowerCAmelCase   )

														if args.fuse_qkv:
																					fuse_qkv(__lowerCAmelCase					,    __lowerCAmelCase   )

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

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






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):
							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   ( __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				List[Any]   ):
							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(__lowerCAmelCase   )






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				str   ):

							def fusea(__lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				List[str]   ):
														for mod in [qq, qk, qv]:
																					if not hasattr(__lowerCAmelCase					,    '_amax'   ):
																												print('          WARNING: NO AMAX BUFFER'   )
																												return
														a__             =     qq._amax.detach().item()
														a__             =     qk._amax.detach().item()
														a__             =     qv._amax.detach().item()

														a__             =     max(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )
														qq._amax.fill_(__lowerCAmelCase   )
														qk._amax.fill_(__lowerCAmelCase   )
														qv._amax.fill_(__lowerCAmelCase   )
														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   ( __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				List[Any]   ):

							for name, mod in model.named_modules():
														if name.endswith('.output.dense'   ) and not name.endswith('attention.output.dense'   ):
																					a__             =     mod._input_quantizer._amax.data.detach().item()
																					mod._input_quantizer._amax.data.detach().clamp_(max=__lowerCAmelCase   )
																					a__             =     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   ( __lowerCAmelCase							:				Optional[Any]   ):

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






def 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]   ):

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






def 							__lowercase   ( __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				Optional[int]=2_5					,    __lowerCAmelCase							:				List[Any]=1_8_0					,    __lowerCAmelCase							:				Tuple=None   ):

							if ignore is None:
														a__             =     []
							elif not isinstance(__lowerCAmelCase					,    __lowerCAmelCase   ):
														a__             =     [ignore]

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

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






def 							__lowercase   ( __lowerCAmelCase							:				Dict   ):
							a__             =     0
							for name, mod in model.named_modules():
														if isinstance(__lowerCAmelCase					,    pytorch_quantization.nn.TensorQuantizer   ):
																					print(F'{name:80} {mod}'   )
																					count += 1
							print(F'{count} TensorQuantizers found in model'   )






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Dict   ):
							a__             =     getattr(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )
							if quantizer_mod is not None:
														assert hasattr(__lowerCAmelCase					,    __lowerCAmelCase   )
														setattr(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )
							else:
														logger.warning(F'{name} has no {quantizer}'   )






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Optional[int]="both"					,    **__lowerCAmelCase							:				str   ):
							a__             =     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(__lowerCAmelCase					,    __lowerCAmelCase					,    '_input_quantizer'					,    __lowerCAmelCase					,    __lowerCAmelCase   )
														if which in ["weight", "both"]:
																					set_quantizer(__lowerCAmelCase					,    __lowerCAmelCase					,    '_weight_quantizer'					,    __lowerCAmelCase					,    __lowerCAmelCase   )
							logger.info(__lowerCAmelCase   )






def 							__lowercase   ( __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				Tuple					,    **__lowerCAmelCase							:				Optional[Any]   ):

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




