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

import numpy as np
import torch

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

@dataclass
class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					torch.FloatTensor
 A__    :					torch.FloatTensor




def 					lowerCamelCase__					(    a__	:	Optional[int]						,   a__	:	Optional[Any]=0.999						,   a__	:	Any="cosine"						,   )		->						List[str]:
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(a__	:	Union[str, Any]  ):
                     return math.exp(t * -12.0  )

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

       UpperCamelCase_											=							[]
       for i in range(a__  ):
              UpperCamelCase_											=							i / num_diffusion_timesteps
              UpperCamelCase_											=							(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(a__  ) / alpha_bar_fn(a__  )						,   a__  )  )
       return torch.tensor(a__						,   dtype=torch.floataa  )







class 		lowercase_							(							__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE					):
 A__    :					List[Any]    =     1

 @register_to_config
 def __init__(   self  ,						__UpperCamelCase = 1_0_0_0  ,						__UpperCamelCase = 0.0_001  ,						__UpperCamelCase = 0.02  ,						__UpperCamelCase = "linear"  ,						__UpperCamelCase = 0.0  ,						__UpperCamelCase = None  ,						__UpperCamelCase = True  ,						):




        """simple docstring"""


        if trained_betas is not None:
               UpperCamelCase_											=							torch.from_numpy(__UpperCamelCase  )
        elif beta_schedule == "linear":
               UpperCamelCase_											=							torch.linspace(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						dtype=torch.floataa  )
        elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               UpperCamelCase_											=							(
                   torch.linspace(beta_start**0.5  ,						beta_end**0.5  ,						__UpperCamelCase  ,						dtype=torch.floataa  ) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               UpperCamelCase_											=							betas_for_alpha_bar(__UpperCamelCase  )
        elif beta_schedule == "sigmoid":
               # GeoDiff sigmoid schedule
               UpperCamelCase_											=							torch.linspace(-6  ,						6  ,						__UpperCamelCase  )
               UpperCamelCase_											=							torch.sigmoid(__UpperCamelCase  ) * (beta_end - beta_start) + beta_start
        else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''  )

        UpperCamelCase_											=							1.0 - self.betas
        UpperCamelCase_											=							torch.cumprod(self.alphas  ,						dim=0  )
        UpperCamelCase_											=							torch.tensor(1.0  )

        UpperCamelCase_											=							torch.tensor(1.0  )

        # standard deviation of the initial noise distribution
        UpperCamelCase_											=							1.0

        # setable values
        UpperCamelCase_											=							None
        UpperCamelCase_											=							torch.from_numpy(np.arange(0  ,						__UpperCamelCase  )[::-1].copy()  )

        UpperCamelCase_											=							eta

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ):




        """simple docstring"""


        return sample

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = 1_0  ,						__UpperCamelCase = 1_0  ,						__UpperCamelCase = None  ,						):




        """simple docstring"""


        UpperCamelCase_											=							min(self.config.num_train_timesteps  ,						__UpperCamelCase  )
        UpperCamelCase_											=							num_inference_steps

        UpperCamelCase_											=							[]

        UpperCamelCase_											=							{}
        for j in range(0  ,						num_inference_steps - jump_length  ,						__UpperCamelCase  ):
               UpperCamelCase_											=							jump_n_sample - 1

        UpperCamelCase_											=							num_inference_steps
        while t >= 1:
               UpperCamelCase_											=							t - 1
               timesteps.append(__UpperCamelCase  )

               if jumps.get(__UpperCamelCase  ,						0  ) > 0:
                      UpperCamelCase_											=							jumps[t] - 1
                      for _ in range(__UpperCamelCase  ):
                             UpperCamelCase_											=							t + 1
                             timesteps.append(__UpperCamelCase  )

        UpperCamelCase_											=							np.array(__UpperCamelCase  ) * (self.config.num_train_timesteps // self.num_inference_steps)
        UpperCamelCase_											=							torch.from_numpy(__UpperCamelCase  ).to(__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							t - self.config.num_train_timesteps // self.num_inference_steps

        UpperCamelCase_											=							self.alphas_cumprod[t]
        UpperCamelCase_											=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        UpperCamelCase_											=							1 - alpha_prod_t
        UpperCamelCase_											=							1 - alpha_prod_t_prev

        # 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
        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
        # without eta.
        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
        UpperCamelCase_											=							(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = True  ,						):




        """simple docstring"""


        UpperCamelCase_											=							timestep
        UpperCamelCase_											=							timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        UpperCamelCase_											=							self.alphas_cumprod[t]
        UpperCamelCase_											=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        UpperCamelCase_											=							1 - alpha_prod_t

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        UpperCamelCase_											=							(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
               UpperCamelCase_											=							torch.clamp(__UpperCamelCase  ,						-1  ,						1  )

        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
        # substitute formula (7) in the algorithm coming from DDPM paper
        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
        # DDIM schedule gives the same results as DDPM with eta = 1.0
        # Noise is being reused in 7. and 8., but no impact on quality has
        # been observed.

        # 5. Add noise
        UpperCamelCase_											=							model_output.device
        UpperCamelCase_											=							randn_tensor(model_output.shape  ,						generator=__UpperCamelCase  ,						device=__UpperCamelCase  ,						dtype=model_output.dtype  )
        UpperCamelCase_											=							self.eta * self._get_variance(__UpperCamelCase  ) ** 0.5

        UpperCamelCase_											=							0
        if t > 0 and self.eta > 0:
               UpperCamelCase_											=							std_dev_t * noise

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        UpperCamelCase_											=							(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        UpperCamelCase_											=							alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
        UpperCamelCase_											=							(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
        UpperCamelCase_											=							mask * prev_known_part + (1.0 - mask) * prev_unknown_part

        if not return_dict:
               return (
                   pred_prev_sample,
                   pred_original_sample,
               )

        return RePaintSchedulerOutput(prev_sample=__UpperCamelCase  ,						pred_original_sample=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase=None  ):




        """simple docstring"""


        UpperCamelCase_											=							self.config.num_train_timesteps // self.num_inference_steps

        for i in range(__UpperCamelCase  ):
               UpperCamelCase_											=							self.betas[timestep + i]
               if sample.device.type == "mps":
                      # randn does not work reproducibly on mps
                      UpperCamelCase_											=							randn_tensor(sample.shape  ,						dtype=sample.dtype  ,						generator=__UpperCamelCase  )
                      UpperCamelCase_											=							noise.to(sample.device  )
               else:
                      UpperCamelCase_											=							randn_tensor(sample.shape  ,						generator=__UpperCamelCase  ,						device=sample.device  ,						dtype=sample.dtype  )

               # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
               UpperCamelCase_											=							(1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						):




        """simple docstring"""


        raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""  )

 def __len__(   self  ):




        """simple docstring"""


        return self.config.num_train_timesteps





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   start_method="""spawn"""  )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      _A							=       rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



