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 						__snake_case						(			__UpperCamelCase		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							None







def     A__	(       __A ,			__A=0.999 ,			__A="cosine" ,			):







    '''simple docstring'''





    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__A						):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__A						):
            return math.exp(t * -12.0						)

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

    _lowerCamelCase  :		Tuple			    =    []
    for i in range(_UpperCAmelCase						):
        _lowerCamelCase  :		List[Any]			    =    i / num_diffusion_timesteps
        _lowerCamelCase  :		Optional[int]			    =    (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 						__snake_case						(			__UpperCamelCase			,	__UpperCamelCase		):





      '''simple docstring'''






      _snake_case     		=							[e.name for e in KarrasDiffusionSchedulers]
      _snake_case     		=							1



      @register_to_config
      def __init__(       self  :					int     ,	_UpperCamelCase  :					int = 1000     ,	_UpperCamelCase  :					float = 0.0_0_0_1     ,	_UpperCamelCase  :					float = 0.0_2     ,	_UpperCamelCase  :					str = "linear"     ,	_UpperCamelCase  :					Optional[Union[np.ndarray, List[float]]] = None     ,	_UpperCamelCase  :					Optional[bool] = False     ,	_UpperCamelCase  :					str = "epsilon"     ,	_UpperCamelCase  :					str = "linspace"     ,	_UpperCamelCase  :					int = 0     ,	)					->List[str]:


          """simple docstring"""


          if trained_betas is not None:
              _lowerCamelCase  :		List[str]			    =    torch.tensor(a_     ,	dtype=torch.floataa)
          elif beta_schedule == "linear":
              _lowerCamelCase  :		Union[str, Any]			    =    torch.linspace(a_     ,	a_     ,	a_     ,	dtype=torch.floataa)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              _lowerCamelCase  :		Dict			    =    (
                  torch.linspace(beta_start**0.5     ,	beta_end**0.5     ,	a_     ,	dtype=torch.floataa) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              _lowerCamelCase  :		Optional[int]			    =    betas_for_alpha_bar(a_)
          else:
              raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

          _lowerCamelCase  :		List[str]			    =    1.0 - self.betas
          _lowerCamelCase  :		List[Any]			    =    torch.cumprod(self.alphas     ,	dim=0)

          _lowerCamelCase  :		str			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		Tuple			    =    np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
          _lowerCamelCase  :		Tuple			    =    torch.from_numpy(a_)

          # setable values
          _lowerCamelCase  :		Dict			    =    None
          _lowerCamelCase  :		Union[str, Any]			    =    use_karras_sigmas
          self.set_timesteps(a_     ,	a_)
          _lowerCamelCase  :		int			    =    []
          _lowerCamelCase  :		Tuple			    =    False



      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Union[str, Any]:


          """simple docstring"""


          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor])					->Optional[int]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					Any)					->Union[str, Any]:


          """simple docstring"""


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

          _lowerCamelCase  :		List[Any]			    =    integrate.quad(a_     ,	self.sigmas[t]     ,	self.sigmas[t + 1]     ,	epsrel=1E-4)[0]

          return integrated_coeff



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					Union[str, torch.device] = None)					->int:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    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":
              _lowerCamelCase  :		Optional[int]			    =    np.linspace(0     ,	self.config.num_train_timesteps - 1     ,	a_     ,	dtype=a_)[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              _lowerCamelCase  :		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
              _lowerCamelCase  :		Any			    =    (np.arange(0     ,	a_) * step_ratio).round()[::-1].copy().astype(a_)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              _lowerCamelCase  :		List[str]			    =    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
              _lowerCamelCase  :		str			    =    (np.arange(self.config.num_train_timesteps     ,	0     ,	-step_ratio)).round().copy().astype(a_)
              timesteps -= 1
          else:
              raise ValueError(
                  F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.""")

          _lowerCamelCase  :		Dict			    =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
          _lowerCamelCase  :		str			    =    np.log(a_)
          _lowerCamelCase  :		int			    =    np.interp(a_     ,	np.arange(0     ,	len(a_))     ,	a_)

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

          _lowerCamelCase  :		Union[str, Any]			    =    np.concatenate([sigmas, [0.0]]).astype(np.floataa)

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

          _lowerCamelCase  :		List[Any]			    =    []



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					Optional[int])					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    np.log(a_)

          # get distribution
          _lowerCamelCase  :		Optional[Any]			    =    log_sigma - log_sigmas[:, np.newaxis]

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

          _lowerCamelCase  :		int			    =    log_sigmas[low_idx]
          _lowerCamelCase  :		Optional[int]			    =    log_sigmas[high_idx]

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

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					torch.FloatTensor)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		float			    =    in_sigmas[-1].item()
          _lowerCamelCase  :		float			    =    in_sigmas[0].item()

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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					Union[float, torch.FloatTensor]     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					int = 4     ,	_UpperCamelCase  :					bool = True     ,	)					->List[str]:


          """simple docstring"""


          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(a_     ,	torch.Tensor):
              _lowerCamelCase  :		Any			    =    timestep.to(self.timesteps.device)
          _lowerCamelCase  :		Dict			    =    (self.timesteps == timestep).nonzero().item()
          _lowerCamelCase  :		int			    =    self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              _lowerCamelCase  :		str			    =    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              _lowerCamelCase  :		str			    =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
              _lowerCamelCase  :		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
          _lowerCamelCase  :		List[Any]			    =    (sample - pred_original_sample) / sigma
          self.derivatives.append(a_)
          if len(self.derivatives) > order:
              self.derivatives.pop(0)

          # 3. Compute linear multistep coefficients
          _lowerCamelCase  :		Optional[int]			    =    min(step_index + 1     ,	a_)
          _lowerCamelCase  :		List[str]			    =    [self.get_lms_coefficient(a_     ,	a_     ,	a_) for curr_order in range(a_)]

          # 4. Compute previous sample based on the derivatives path
          _lowerCamelCase  :		List[str]			    =    sample + sum(
              coeff * derivative for coeff, derivative in zip(a_     ,	reversed(self.derivatives)))

          if not return_dict:
              return (prev_sample,)

          return LMSDiscreteSchedulerOutput(prev_sample=a_     ,	pred_original_sample=a_)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	_UpperCamelCase  :					torch.FloatTensor     ,	)					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    self.sigmas.to(device=original_samples.device     ,	dtype=original_samples.dtype)
          if original_samples.device.type == "mps" and torch.is_floating_point(a_):
              # mps does not support float64
              _lowerCamelCase  :		Union[str, Any]			    =    self.timesteps.to(original_samples.device     ,	dtype=torch.floataa)
              _lowerCamelCase  :		str			    =    timesteps.to(original_samples.device     ,	dtype=torch.floataa)
          else:
              _lowerCamelCase  :		List[Any]			    =    self.timesteps.to(original_samples.device)
              _lowerCamelCase  :		Union[str, Any]			    =    timesteps.to(original_samples.device)

          _lowerCamelCase  :		Union[str, Any]			    =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

          _lowerCamelCase  :		Any			    =    original_samples + noise * sigma
          return noisy_samples




      def __len__(       self  :					Dict)					->Union[str, Any]:


          """simple docstring"""


          return self.config.num_train_timesteps









import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





