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    =					logging.getLogger(__name__)

UpperCAmelCase    =					50  # max width of layer names
UpperCAmelCase    =					70  # max width of quantizer names






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[Any]:




      """simple docstring"""

      snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      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=_lowerCamelCase      )
      snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=False      ,      SCREAMING_SNAKE_CASE=False      )->						int:




      """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(_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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Dict:




      """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(_lowerCamelCase      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      def fusea(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ):
            for mod in [qq, qk, qv]:
                  if not hasattr(_lowerCamelCase      ,      '''_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(_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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      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=_lowerCamelCase      )
                  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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      for name, mod in model.named_modules():
            if hasattr(_lowerCamelCase      ,      '''_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(_lowerCamelCase      ,      dtype=amax.dtype      ,      device=amax.device      ) * amax
                  print(f'''expanding {name} {amax} -> {mod._weight_quantizer._amax}'''      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      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)
                  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=_lowerCamelCase      ,      keepdims=_lowerCamelCase      ).detach()
                  logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'''      )
                  snake_case_ =   amax






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=25      ,      SCREAMING_SNAKE_CASE=180      ,      SCREAMING_SNAKE_CASE=None      )->						Any:




      """simple docstring"""

      if ignore is None:
            snake_case_ =   []
      elif not isinstance(_lowerCamelCase      ,      _lowerCamelCase      ):
            snake_case_ =   [ignore]

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

      for name, mod in model.named_modules():
            snake_case_ =   getattr(_lowerCamelCase      ,      '''_input_quantizer'''      ,      _lowerCamelCase      )
            snake_case_ =   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
            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(_lowerCamelCase      ) <= line_width:
                  logger.info(_lowerCamelCase      )
            else:
                  logger.info(f'''{name:{name_width}} {act_str}'''      )
                  logger.info(f'''{"  ":{name_width}} {wgt_str}'''      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE="both"      ,      **SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      **SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

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



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort







class lowerCAmelCase_		(				lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 pass







@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 @property
 def 		UpperCamelCase__  (			self      ):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ort.SessionOptions()
       snake_case_ =   False
       return options



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''      )
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''      )
       snake_case_ =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   revision='''onnx'''	,   safety_checker=_UpperCAmelCase	,   feature_extractor=_UpperCAmelCase	,   provider=self.gpu_provider	,   sess_options=self.gpu_options	,   )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   '''A red cat sitting on a park bench'''

       snake_case_ =   np.random.RandomState(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   mask_image=_UpperCAmelCase	,   guidance_scale=7.5	,   num_inference_steps=10	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images
       snake_case_ =   images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''      )
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''      )
       snake_case_ =   LMSDiscreteScheduler.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   subfolder='''scheduler'''	,   revision='''onnx'''      )
       snake_case_ =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   revision='''onnx'''	,   scheduler=_UpperCAmelCase	,   safety_checker=_UpperCAmelCase	,   feature_extractor=_UpperCAmelCase	,   provider=self.gpu_provider	,   sess_options=self.gpu_options	,   )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   '''A red cat sitting on a park bench'''

       snake_case_ =   np.random.RandomState(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   mask_image=_UpperCAmelCase	,   guidance_scale=7.5	,   num_inference_steps=20	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images
       snake_case_ =   images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3