



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	:   int       			=       logging.getLogger(__name__)

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







def       lowerCAmelCase_     (lowerCAmelCase__:						List[Any]   ):





			"""simple docstring"""


			UpperCAmelCase_:						Optional[Any]					=			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_     (lowerCAmelCase__:						List[str]   ):





			"""simple docstring"""


			if args.calibrator == "max":
						UpperCAmelCase_:						Optional[int]					=			"""max"""
			elif args.calibrator == "percentile":
						if args.percentile is None:
									raise ValueError("""Specify --percentile when using percentile calibrator"""   )
						UpperCAmelCase_:						Union[str, Any]					=			"""histogram"""
			elif args.calibrator == "mse":
						UpperCAmelCase_:						Tuple					=			"""histogram"""
			else:
						raise ValueError(F'Invalid calibrator {args.calibrator}'   )

			UpperCAmelCase_:						str					=			QuantDescriptor(num_bits=args.aprec						,		calib_method=_A   )
			UpperCAmelCase_:						Dict					=			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_     (lowerCAmelCase__:						int						,		lowerCAmelCase__:						Any						,		lowerCAmelCase__:						Any=False						,		lowerCAmelCase__:						str=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_     (lowerCAmelCase__:						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_     (lowerCAmelCase__:						Dict						,		lowerCAmelCase__:						Union[str, Any]   ):





			"""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_     (lowerCAmelCase__:						int						,		lowerCAmelCase__:						List[Any]   ):





			"""simple docstring"""


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

						UpperCAmelCase_:						str					=			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_     (lowerCAmelCase__:						Tuple						,		lowerCAmelCase__:						List[Any]   ):





			"""simple docstring"""


			for name, mod in model.named_modules():
						if name.endswith(""".output.dense"""   ) and not name.endswith("""attention.output.dense"""   ):
									UpperCAmelCase_:						Optional[int]					=			mod._input_quantizer._amax.data.detach().item()
									mod._input_quantizer._amax.data.detach().clamp_(max=_A   )
									UpperCAmelCase_:						List[str]					=			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_     (lowerCAmelCase__:						str   ):





			"""simple docstring"""


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







def       lowerCAmelCase_     (lowerCAmelCase__:						List[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_:						Any					=			set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
									UpperCAmelCase_:						List[str]					=			set(range(len(mod.weight.size()   )   )   ) - axis_set
									UpperCAmelCase_:						List[str]					=			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_:						Optional[Any]					=			amax







def       lowerCAmelCase_     (lowerCAmelCase__:						Dict						,		lowerCAmelCase__:						Union[str, Any]=2_5						,		lowerCAmelCase__:						Any=1_8_0						,		lowerCAmelCase__:						str=None   ):





			"""simple docstring"""


			if ignore is None:
						UpperCAmelCase_:						Union[str, Any]					=			[]
			elif not isinstance(_A						,		_A   ):
						UpperCAmelCase_:						Union[str, Any]					=			[ignore]

			UpperCAmelCase_:						int					=			0
			for name, mod in model.named_modules():
						if not hasattr(_A						,		"""weight"""   ):
									continue
						UpperCAmelCase_:						Union[str, Any]					=			max(_A						,		len(_A   )   )

			for name, mod in model.named_modules():
						UpperCAmelCase_:						List[Any]					=			getattr(_A						,		"""_input_quantizer"""						,		_A   )
						UpperCAmelCase_:						List[Any]					=			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_:						int					=			F'Act:{input_q.extra_repr()}'
						UpperCAmelCase_:						Optional[int]					=			F'Wgt:{weight_q.extra_repr()}'
						UpperCAmelCase_:						List[Any]					=			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_     (lowerCAmelCase__:						List[Any]   ):





			"""simple docstring"""


			UpperCAmelCase_:						Optional[Any]					=			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_     (lowerCAmelCase__:						Tuple						,		lowerCAmelCase__:						Union[str, Any]						,		lowerCAmelCase__:						Optional[int]						,		lowerCAmelCase__:						Tuple						,		lowerCAmelCase__:						Optional[int]   ):





			"""simple docstring"""


			UpperCAmelCase_:						Tuple					=			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_     (lowerCAmelCase__:						Tuple						,		lowerCAmelCase__:						Union[str, Any]						,		lowerCAmelCase__:						Tuple="both"						,		**lowerCAmelCase__:						List[str]   ):





			"""simple docstring"""


			UpperCAmelCase_:						List[Any]					=			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_     (lowerCAmelCase__:						Optional[int]						,		lowerCAmelCase__:						Optional[int]						,		**lowerCAmelCase__:						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_:						Any					=			F'Warning: changing {name:{name_width}}'
															for k, v in kwargs.items():
																		s += F' {k}={v}'
																		setattr(_A						,		_A						,		_A   )
															logger.info(_A   )









import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 						__magic_name__			(							lowerCamelCase__		,       lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	List[Any]    =   IFInpaintingSuperResolutionPipeline
   lowerCamelCase__ :	str    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
   lowerCamelCase__ :	List[Any]    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}							)
   lowerCamelCase__ :	str    =   PipelineTesterMixin.required_optional_params - {'latents'}



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






     """simple docstring"""






     return self._get_superresolution_dummy_components()



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=0       )							->    Tuple:






     """simple docstring"""






     if str(lowercase_       ).startswith('''mps'''       ):
       a__  =torch.manual_seed(lowercase_       )
     else:
       a__  =torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

     a__  =floats_tensor((1, 3, 16, 16),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )

     a__  ={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''original_image''': original_image,
         '''mask_image''': mask_image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }

     return inputs



   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available(),    reason='''XFormers attention is only available with CUDA and `xformers` installed''',    )
   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_save_load_optional_components()



   @unittest.skipIf(torch_device != '''cuda''',    reason='''float16 requires CUDA'''       )
   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






     super().test_save_load_floataa(expected_max_diff=1E-1       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     self._test_save_load_local()



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2,    )
