






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__       =     5_0  # max width of layer names
a__       =     7_0  # max width of quantizer names

def _UpperCAmelCase   (	a						:	Optional[Any] ):
							snake_case__	  =       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 _UpperCAmelCase   (	a						:	List[Any] ):

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

							snake_case__	  =       QuantDescriptor(num_bits=args.aprec  ,					calib_method=a )
							snake_case__	  =       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 _UpperCAmelCase   (	a						:	Dict  ,					a						:	Union[str, Any]  ,					a						:	Dict=False  ,					a						:	int=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(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 _UpperCAmelCase   (	a						:	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 _UpperCAmelCase   (	a						:	int  ,					a						:	Dict ):
							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 _UpperCAmelCase   (	a						:	Dict  ,					a						:	Dict ):

							def fusea(a						:	Dict  ,					a						:	Union[str, Any]  ,					a						:	Union[str, Any] ):
														for mod in [qq, qk, qv]:
																					if not hasattr(a  ,					"""_amax""" ):
																												print("""          WARNING: NO AMAX BUFFER""" )
																												return
														snake_case__	  =       qq._amax.detach().item()
														snake_case__	  =       qk._amax.detach().item()
														snake_case__	  =       qv._amax.detach().item()

														snake_case__	  =       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 _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	str ):

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

def _UpperCAmelCase   (	a						:	str ):

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

def _UpperCAmelCase   (	a						:	Dict ):

							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)
																					snake_case__	  =       set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
																					snake_case__	  =       set(range(len(mod.weight.size() ) ) ) - axis_set
																					snake_case__	  =       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()}''' )
																					snake_case__	  =       amax

def _UpperCAmelCase   (	a						:	Tuple  ,					a						:	str=25  ,					a						:	List[str]=180  ,					a						:	Tuple=None ):

							if ignore is None:
														snake_case__	  =       []
							elif not isinstance(a  ,					a ):
														snake_case__	  =       [ignore]

							snake_case__	  =       0
							for name, mod in model.named_modules():
														if not hasattr(a  ,					"""weight""" ):
																					continue
														snake_case__	  =       max(a  ,					len(a ) )

							for name, mod in model.named_modules():
														snake_case__	  =       getattr(a  ,					"""_input_quantizer"""  ,					a )
														snake_case__	  =       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
														snake_case__	  =       F'''Act:{input_q.extra_repr()}'''
														snake_case__	  =       F'''Wgt:{weight_q.extra_repr()}'''
														snake_case__	  =       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 _UpperCAmelCase   (	a						:	List[Any] ):
							snake_case__	  =       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 _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	int  ,					a						:	Optional[int]  ,					a						:	Any  ,					a						:	Optional[Any] ):
							snake_case__	  =       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 _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Optional[Any]  ,					a						:	Optional[Any]="both"  ,					**a						:	Any ):
							snake_case__	  =       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 _UpperCAmelCase   (	a						:	str  ,					a						:	Union[str, Any]  ,					**a						:	List[Any] ):

							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 ):
																																			snake_case__	  =       F'''Warning: changing {name:{name_width}}'''
																																			for k, v in kwargs.items():
																																										s += F''' {k}={v}'''
																																										setattr(a  ,					a  ,					a )
																																			logger.info(a )








import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  VQModel
				_lowercase				:   str      =  '''sample'''



				@property
				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Tuple=(3_2, 3_2)):






											'''simple docstring'''







											snake_case__	  =       4
											snake_case__	  =       3

											snake_case__	  =       floats_tensor((batch_size, num_channels) + sizes).to(UpperCamelCase__)

											return {"sample": image}



				@property
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				@property
				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       {
											    """block_out_channels""": [3_2, 6_4],
											    """in_channels""": 3,
											    """out_channels""": 3,
											    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
											    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
											    """latent_channels""": 3,
											}
											snake_case__	  =       self.dummy_input
											return init_dict, inputs_dict



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy"""			,		output_loading_info=UpperCamelCase__)
											self.assertIsNotNone(UpperCamelCase__)
											self.assertEqual(len(loading_info["""missing_keys"""])			,		0)

											model.to(UpperCamelCase__)
											snake_case__	  =       model(**self.dummy_input)

											assert image is not None, "Make sure output is not None"



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy""")
											model.to(UpperCamelCase__).eval()

											torch.manual_seed(0)
											if torch.cuda.is_available():
																		torch.cuda.manual_seed_all(0)

											snake_case__	  =       torch.randn(1			,		model.config.in_channels			,		model.config.sample_size			,		model.config.sample_size)
											snake_case__	  =       image.to(UpperCamelCase__)
											with torch.no_grad():
																		snake_case__	  =       model(UpperCamelCase__).sample

											snake_case__	  =       output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case__	  =       torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43])
											# fmt: on
											self.assertTrue(torch.allclose(UpperCamelCase__			,		UpperCamelCase__			,		atol=1E-3))
