





'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class lowerCamelCase						:
  _lowercase  :	CommonSchedulerState

  # setable values
  _lowercase  :	jnp.ndarray
  _lowercase  :	jnp.ndarray
  _lowercase  :	Optional[int]       					=				None







  @classmethod
  def 				UpperCAmelCase_				(		cls     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						List[str]:






        """simple docstring"""





        return cls(common=lowercase__     ,     init_noise_sigma=lowercase__     ,     timesteps=lowercase__		)





@dataclass
class lowerCamelCase						(a__					):
  _lowercase  :	DDPMSchedulerState





class lowerCamelCase						(a__						,      a__					):
  _lowercase  :	List[str]       					=				[e.name for e in FlaxKarrasDiffusionSchedulers]

  _lowercase  :	jnp.dtype







  @property
  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        return True







  @register_to_config
  def __init__(		self     ,     lowercase__ = 1_000     ,     lowercase__ = 0.0_001     ,     lowercase__ = 0.02     ,     lowercase__ = "linear"     ,     lowercase__ = None     ,     lowercase__ = "fixed_small"     ,     lowercase__ = True     ,     lowercase__ = "epsilon"     ,     lowercase__ = jnp.floataa     ,     )		->						Tuple:






        """simple docstring"""





        _snake_case		:	int					     =     dtype







  def 				UpperCAmelCase_				(		self     ,     lowercase__ = None		)		->						DDPMSchedulerState:






        """simple docstring"""





        if common is None:
              _snake_case		:	Optional[Any]					     =     CommonSchedulerState.create(self		)

        # standard deviation of the initial noise distribution
        _snake_case		:	str					     =     jnp.array(1.0     ,     dtype=self.dtype		)

        _snake_case		:	Tuple					     =     jnp.arange(0     ,     self.config.num_train_timesteps		).round()[::-1]

        return DDPMSchedulerState.create(
            common=lowercase__     ,     init_noise_sigma=lowercase__     ,     timesteps=lowercase__     ,     )







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None		)		->						jnp.ndarray:






        """simple docstring"""





        return sample







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = ()		)		->						DDPMSchedulerState:






        """simple docstring"""





        _snake_case		:	str					     =     self.config.num_train_timesteps // num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # rounding to avoid issues when num_inference_step is power of 3
        _snake_case		:	Optional[Any]					     =     (jnp.arange(0     ,     lowercase__		) * step_ratio).round()[::-1]

        return state.replace(
            num_inference_steps=lowercase__     ,     timesteps=lowercase__     ,     )







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__=None     ,     lowercase__=None		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     state.common.alphas_cumprod[t]
        _snake_case		:	Dict					     =     jnp.where(t > 0     ,     state.common.alphas_cumprod[t - 1]     ,     jnp.array(1.0     ,     dtype=self.dtype		)		)

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        _snake_case		:	str					     =     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

        if variance_type is None:
              _snake_case		:	str					     =     self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small":
              _snake_case		:	List[str]					     =     jnp.clip(lowercase__     ,     a_min=1E-2_0		)
        # for rl-diffuser https://arxiv.org/abs/2205.09991
        elif variance_type == "fixed_small_log":
              _snake_case		:	List[str]					     =     jnp.log(jnp.clip(lowercase__     ,     a_min=1E-2_0		)		)
        elif variance_type == "fixed_large":
              _snake_case		:	Tuple					     =     state.common.betas[t]
        elif variance_type == "fixed_large_log":
              # Glide max_log
              _snake_case		:	str					     =     jnp.log(state.common.betas[t]		)
        elif variance_type == "learned":
              return predicted_variance
        elif variance_type == "learned_range":
              _snake_case		:	int					     =     variance
              _snake_case		:	Optional[Any]					     =     state.common.betas[t]
              _snake_case		:	Optional[int]					     =     (predicted_variance + 1) / 2
              _snake_case		:	Any					     =     frac * max_log + (1 - frac) * min_log

        return variance







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = True     ,     )		->						Union[FlaxDDPMSchedulerOutput, Tuple]:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     timestep

        if key is None:
              _snake_case		:	str					     =     jax.random.PRNGKey(0		)

        if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
              _snake_case     ,							_snake_case		:	Dict					     =     jnp.split(lowercase__     ,     sample.shape[1]     ,     axis=1		)
        else:
              _snake_case		:	Tuple					     =     None

        # 1. compute alphas, betas
        _snake_case		:	int					     =     state.common.alphas_cumprod[t]
        _snake_case		:	Any					     =     jnp.where(t > 0     ,     state.common.alphas_cumprod[t - 1]     ,     jnp.array(1.0     ,     dtype=self.dtype		)		)
        _snake_case		:	Tuple					     =     1 - alpha_prod_t
        _snake_case		:	List[str]					     =     1 - alpha_prod_t_prev

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
              _snake_case		:	Any					     =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
              _snake_case		:	Union[str, Any]					     =     model_output
        elif self.config.prediction_type == "v_prediction":
              _snake_case		:	List[Any]					     =     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
        else:
              raise ValueError(
                  F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
                  ''' for the FlaxDDPMScheduler.'''		)

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
              _snake_case		:	List[Any]					     =     jnp.clip(lowercase__     ,     -1     ,     1		)

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        _snake_case		:	Optional[Any]					     =     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
        _snake_case		:	List[Any]					     =     state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        _snake_case		:	Union[str, Any]					     =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        def random_variance():
              _snake_case		:	Optional[Any]					     =     jax.random.split(lowercase__     ,     num=1		)
              _snake_case		:	str					     =     jax.random.normal(lowercase__     ,     shape=model_output.shape     ,     dtype=self.dtype		)
              return (self._get_variance(lowercase__     ,     lowercase__     ,     predicted_variance=lowercase__		) ** 0.5) * noise

        _snake_case		:	Dict					     =     jnp.where(t > 0     ,     random_variance()     ,     jnp.zeros(model_output.shape     ,     dtype=self.dtype		)		)

        _snake_case		:	Union[str, Any]					     =     pred_prev_sample + variance

        if not return_dict:
              return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=lowercase__     ,     state=lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						jnp.ndarray:






        """simple docstring"""





        return add_noise_common(state.common     ,     lowercase__     ,     lowercase__     ,     lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						jnp.ndarray:






        """simple docstring"""





        return get_velocity_common(state.common     ,     lowercase__     ,     lowercase__     ,     lowercase__		)







  def __len__(		self		)		->						str:






        """simple docstring"""





        return self.config.num_train_timesteps









'''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


UpperCAmelCase					:		str		=							logging.getLogger(__name__)

UpperCAmelCase					:		Dict		=							5_0  # max width of layer names
UpperCAmelCase					:		Union[str, Any]		=							7_0  # max width of quantizer names




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Dict					     =     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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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

      _snake_case		:	Tuple					     =     QuantDescriptor(num_bits=args.aprec			,      calib_method=lowerCAmelCase_	)
      _snake_case		:	str					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=False			,      lowerCAmelCase_=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(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    _a    (							lowerCAmelCase_	):





      """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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      def fusea(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):
            for mod in [qq, qk, qv]:
                  if not hasattr(lowerCAmelCase_			,      '''_amax'''	):
                        print('''          WARNING: NO AMAX BUFFER'''	)
                        return
            _snake_case		:	Tuple					     =     qq._amax.detach().item()
            _snake_case		:	Tuple					     =     qk._amax.detach().item()
            _snake_case		:	List[Any]					     =     qv._amax.detach().item()

            _snake_case		:	List[str]					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_	):





      """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		:	int					     =     set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis	)
                  _snake_case		:	Dict					     =     set(range(len(mod.weight.size()	)	)	) - axis_set
                  _snake_case		:	Optional[int]					     =     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		:	Tuple					     =     amax




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_=25			,      lowerCAmelCase_=180			,      lowerCAmelCase_=None	):





      """simple docstring"""



      if ignore is None:
            _snake_case		:	Dict					     =     []
      elif not isinstance(lowerCAmelCase_			,      lowerCAmelCase_	):
            _snake_case		:	Optional[int]					     =     [ignore]

      _snake_case		:	str					     =     0
      for name, mod in model.named_modules():
            if not hasattr(lowerCAmelCase_			,      '''weight'''	):
                  continue
            _snake_case		:	Optional[int]					     =     max(lowerCAmelCase_			,      len(lowerCAmelCase_	)	)

      for name, mod in model.named_modules():
            _snake_case		:	Optional[Any]					     =     getattr(lowerCAmelCase_			,      '''_input_quantizer'''			,      lowerCAmelCase_	)
            _snake_case		:	Tuple					     =     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		:	Optional[int]					     =     f'''Act:{input_q.extra_repr()}'''
            _snake_case		:	Any					     =     f'''Wgt:{weight_q.extra_repr()}'''
            _snake_case		:	Optional[int]					     =     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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	str					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_="both"			,      **lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      **lowerCAmelCase_	):





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


