import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class _lowerCAmelCase (  A__				):
      UpperCAmelCase_  :	List[Any]     =					42
      UpperCAmelCase_  :	List[str]     =					None







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Union[str, Any]   ,      __UpperCAmelCase     :				List[Any]=0.999   ,      __UpperCAmelCase     :				List[str]="cosine"   ,      )       ->  List[str]:







  if alpha_transform_type == "cosine":

    def alpha_bar_fn(__UpperCAmelCase     :				Optional[int] ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(__UpperCAmelCase     :				List[str] ):
      return math.exp(t * -12.0 )

  else:
    raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )

  lowercase							:       str											=      []
  for i in range(__UpperCAmelCase ):
    lowercase							:       int											=      i / num_diffusion_timesteps
    lowercase							:       Tuple											=      (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase )   ,      __UpperCAmelCase ) )
  return torch.tensor(__UpperCAmelCase   ,      dtype=torch.floataa )






class _lowerCAmelCase (  A__       ,      A__				):
      UpperCAmelCase_  :	List[Any]     =					[e.name for e in KarrasDiffusionSchedulers]
      UpperCAmelCase_  :	List[str]     =					1







      @register_to_config
      def __init__(     self     ,						lowercase = 1000     ,						lowercase = 0.0001     ,						lowercase = 0.02     ,						lowercase = "linear"     ,						lowercase = None     ,						lowercase = False     ,						lowercase = "epsilon"     ,						lowercase = "linspace"     ,						lowercase = 0     ,						):
        if trained_betas is not None:
          lowercase							:       List[Any]											=      torch.tensor(_lowerCAmelCase     ,						dtype=torch.floataa    )
        elif beta_schedule == "linear":
          lowercase							:       Any											=      torch.linspace(_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						dtype=torch.floataa    )
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase							:       str											=      (
              torch.linspace(beta_start**0.5     ,						beta_end**0.5     ,						_lowerCAmelCase     ,						dtype=torch.floataa    ) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase							:       Dict											=      betas_for_alpha_bar(_lowerCAmelCase    )
        else:
          raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

        lowercase							:       Optional[int]											=      1.0 - self.betas
        lowercase							:       List[str]											=      torch.cumprod(self.alphas     ,						dim=0    )

        lowercase							:       List[Any]											=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
        lowercase							:       List[Any]											=      np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
        lowercase							:       List[Any]											=      torch.from_numpy(_lowerCAmelCase    )

        # setable values
        lowercase							:       str											=      None
        lowercase							:       Tuple											=      use_karras_sigmas
        self.set_timesteps(_lowerCAmelCase     ,						_lowerCAmelCase    )
        lowercase							:       Optional[int]											=      []
        lowercase							:       Optional[Any]											=      False







      @property
      def     lowerCamelCase__			(     self    ):
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase    ):
        if isinstance(_lowerCAmelCase     ,						torch.Tensor    ):
          lowercase							:       Union[str, Any]											=      timestep.to(self.timesteps.device    )
        lowercase							:       Union[str, Any]											=      (self.timesteps == timestep).nonzero().item()
        lowercase							:       List[str]											=      self.sigmas[step_index]
        lowercase							:       Optional[int]											=      sample / ((sigma**2 + 1) ** 0.5)
        lowercase							:       int											=      True
        return sample







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase     ,						lowercase    ):

        def lms_derivative(lowercase    ):
          lowercase							:       Optional[int]											=      1.0
          for k in range(_lowerCAmelCase    ):
            if current_order == k:
              continue
            prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
          return prod

        lowercase							:       Union[str, Any]											=      integrate.quad(_lowerCAmelCase     ,						self.sigmas[t]     ,						self.sigmas[t + 1]     ,						epsrel=1e-4    )[0]

        return integrated_coeff







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = None    ):
        lowercase							:       Tuple											=      num_inference_steps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
          lowercase							:       Dict											=      np.linspace(0     ,						self.config.num_train_timesteps - 1     ,						_lowerCAmelCase     ,						dtype=_lowerCAmelCase    )[
              ::-1
          ].copy()
        elif self.config.timestep_spacing == "leading":
          lowercase							:       Optional[Any]											=      self.config.num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowercase							:       Union[str, Any]											=      (np.arange(0     ,						_lowerCAmelCase    ) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase    )
          timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
          lowercase							:       int											=      self.config.num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowercase							:       Tuple											=      (np.arange(self.config.num_train_timesteps     ,						0     ,						-step_ratio    )).round().copy().astype(_lowerCAmelCase    )
          timesteps -= 1
        else:
          raise ValueError(
              F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

        lowercase							:       Any											=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
        lowercase							:       Tuple											=      np.log(_lowerCAmelCase    )
        lowercase							:       List[str]											=      np.interp(_lowerCAmelCase     ,						np.arange(0     ,						len(_lowerCAmelCase    )    )     ,						_lowerCAmelCase    )

        if self.use_karras_sigmas:
          lowercase							:       Tuple											=      self._convert_to_karras(in_sigmas=_lowerCAmelCase    )
          lowercase							:       List[str]											=      np.array([self._sigma_to_t(_lowerCAmelCase     ,						_lowerCAmelCase    ) for sigma in sigmas]    )

        lowercase							:       Any											=      np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )

        lowercase							:       Union[str, Any]											=      torch.from_numpy(_lowerCAmelCase    ).to(device=_lowerCAmelCase    )
        if str(_lowerCAmelCase    ).startswith('''mps'''    ):
          # mps does not support float64
          lowercase							:       int											=      torch.from_numpy(_lowerCAmelCase    ).to(_lowerCAmelCase     ,						dtype=torch.floataa    )
        else:
          lowercase							:       List[Any]											=      torch.from_numpy(_lowerCAmelCase    ).to(device=_lowerCAmelCase    )

        lowercase							:       List[str]											=      []







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase    ):
        # get log sigma
        lowercase							:       Any											=      np.log(_lowerCAmelCase    )

        # get distribution
        lowercase							:       str											=      log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        lowercase							:       Tuple											=      np.cumsum((dists >= 0)     ,						axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
        lowercase							:       Optional[Any]											=      low_idx + 1

        lowercase							:       int											=      log_sigmas[low_idx]
        lowercase							:       Union[str, Any]											=      log_sigmas[high_idx]

        # interpolate sigmas
        lowercase							:       int											=      (low - log_sigma) / (low - high)
        lowercase							:       Optional[Any]											=      np.clip(_lowerCAmelCase     ,						0     ,						1    )

        # transform interpolation to time range
        lowercase							:       List[str]											=      (1 - w) * low_idx + w * high_idx
        lowercase							:       Optional[int]											=      t.reshape(sigma.shape    )
        return t







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        lowercase							:       Union[str, Any]											=      in_sigmas[-1].item()
        lowercase							:       Optional[Any]											=      in_sigmas[0].item()

        lowercase							:       Tuple											=      7.0  # 7.0 is the value used in the paper
        lowercase							:       Optional[int]											=      np.linspace(0     ,						1     ,						self.num_inference_steps    )
        lowercase							:       int											=      sigma_min ** (1 / rho)
        lowercase							:       int											=      sigma_max ** (1 / rho)
        lowercase							:       int											=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase = 4     ,						lowercase = True     ,						):
        if not self.is_scale_input_called:
          warnings.warn(
              '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
              '''See `StableDiffusionPipeline` for a usage example.'''    )

        if isinstance(_lowerCAmelCase     ,						torch.Tensor    ):
          lowercase							:       Union[str, Any]											=      timestep.to(self.timesteps.device    )
        lowercase							:       str											=      (self.timesteps == timestep).nonzero().item()
        lowercase							:       Dict											=      self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          lowercase							:       List[Any]											=      sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          lowercase							:       List[str]											=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
          lowercase							:       Optional[Any]											=      model_output
        else:
          raise ValueError(
              F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

        # 2. Convert to an ODE derivative
        lowercase							:       Optional[int]											=      (sample - pred_original_sample) / sigma
        self.derivatives.append(_lowerCAmelCase    )
        if len(self.derivatives    ) > order:
          self.derivatives.pop(0    )

        # 3. Compute linear multistep coefficients
        lowercase							:       Optional[Any]											=      min(step_index + 1     ,						_lowerCAmelCase    )
        lowercase							:       int											=      [self.get_lms_coefficient(_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase    ) for curr_order in range(_lowerCAmelCase    )]

        # 4. Compute previous sample based on the derivatives path
        lowercase							:       Any											=      sample + sum(
            coeff * derivative for coeff, derivative in zip(_lowerCAmelCase     ,						reversed(self.derivatives    )    )    )

        if not return_dict:
          return (prev_sample,)

        return LMSDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase     ,						pred_original_sample=_lowerCAmelCase    )







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase     ,						lowercase     ,						):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowercase							:       Optional[Any]											=      self.sigmas.to(device=original_samples.device     ,						dtype=original_samples.dtype    )
        if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase    ):
          # mps does not support float64
          lowercase							:       Tuple											=      self.timesteps.to(original_samples.device     ,						dtype=torch.floataa    )
          lowercase							:       Dict											=      timesteps.to(original_samples.device     ,						dtype=torch.floataa    )
        else:
          lowercase							:       List[Any]											=      self.timesteps.to(original_samples.device    )
          lowercase							:       Union[str, Any]											=      timesteps.to(original_samples.device    )

        lowercase							:       Any											=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

        lowercase							:       str											=      sigmas[step_indices].flatten()
        while len(sigma.shape    ) < len(original_samples.shape    ):
          lowercase							:       List[str]											=      sigma.unsqueeze(-1    )

        lowercase							:       Dict											=      original_samples + noise * sigma
        return noisy_samples







      def __len__(     self    ):
        return self.config.num_train_timesteps
'''simple docstring'''
import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Optional[Any]   ,      __UpperCAmelCase     :				Tuple   ,      __UpperCAmelCase     :				Union[str, Any]   ,      __UpperCAmelCase     :				List[str] )       ->  List[str]:
  # Initialise PyTorch model
  lowercase							:       Any											=      ElectraConfig.from_json_file(__UpperCAmelCase )
  print(f"""Building PyTorch model from configuration: {config}""" )

  if discriminator_or_generator == "discriminator":
    lowercase							:       Union[str, Any]											=      ElectraForPreTraining(__UpperCAmelCase )
  elif discriminator_or_generator == "generator":
    lowercase							:       Tuple											=      ElectraForMaskedLM(__UpperCAmelCase )
  else:
    raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'''' )

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      __UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase   ,      discriminator_or_generator=__UpperCAmelCase )

  # Save pytorch-model
  print(f"""Save PyTorch model to {pytorch_dump_path}""" )
  torch.save(model.state_dict()   ,      __UpperCAmelCase )


if __name__ == "__main__":
 _A		:			str	      =argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--discriminator_or_generator''',
     default=None,
     type=str,
     required=True,
     help=(
         '''Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '''
         '''\'generator\'.'''
     ),
 )
 _A		:			Any	      =parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
 )