
"""simple docstring"""




import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
_A			:       Dict   		=       logging.get_logger(__name__)


_A			:       str   		=       """https://openaipublic.azureedge.net/jukebox/models/"""
_A			:       Optional[Any]   		=       {
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}


def   __magic_name__	(      __snake_case				:       str  ) ->				Any:


       if key.endswith(".model.1.bias"  ) and len(key.split("."  )  ) > 10:
              lowercase    :				List[str]       		=							key.replace(".model.1.bias"     , ".conv1d_1.bias"  )
       elif key.endswith(".model.1.weight"  ) and len(key.split("."  )  ) > 10:
              lowercase    :				Any       		=							key.replace(".model.1.weight"     , ".conv1d_1.weight"  )
       elif key.endswith(".model.3.bias"  ) and len(key.split("."  )  ) > 10:
              lowercase    :				List[str]       		=							key.replace(".model.3.bias"     , ".conv1d_2.bias"  )
       elif key.endswith(".model.3.weight"  ) and len(key.split("."  )  ) > 10:
              lowercase    :				Tuple       		=							key.replace(".model.3.weight"     , ".conv1d_2.weight"  )

       if "conditioner_blocks.0." in key:
              lowercase    :				Any       		=							key.replace("conditioner_blocks.0"     , "conditioner_blocks"  )

       if "prime_prior" in key:
              lowercase    :				Tuple       		=							key.replace("prime_prior"     , "encoder"  )

       if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
              lowercase    :				List[Any]       		=							key.replace(".emb."     , "."  )

       if key.endswith("k"  ):  # replace vqvae.X.k with vqvae.X.codebook
              return key.replace(".k"     , ".codebook"  )
       if "y_emb." in key:
              return key.replace("y_emb."     , "metadata_embedding."  )

       if "x_emb.emb." in key:
              lowercase    :				Union[str, Any]       		=							key.replace("0.x_emb.emb"     , "embed_tokens"  )

       if "prime_state_ln" in key:
              return key.replace("prime_state_ln"     , "encoder.final_layer_norm"  )
       if ".ln" in key:
              return key.replace(".ln"     , ".layer_norm"  )
       if "_ln" in key:
              return key.replace("_ln"     , "_layer_norm"  )

       if "prime_state_proj" in key:
              return key.replace("prime_state_proj"     , "encoder.proj_in"  )
       if "prime_x_out" in key:
              return key.replace("prime_x_out"     , "encoder.lm_head"  )
       if "prior.x_out" in key:
              return key.replace("x_out"     , "fc_proj_out"  )
       if "x_emb" in key:
              return key.replace("x_emb"     , "embed_tokens"  )

       return key


def   __magic_name__	(      __snake_case				:       Union[str, Any]     , __snake_case				:       int     , __snake_case				:       Tuple     , __snake_case				:       List[str]  ) ->				Dict:
       lowercase    :				int       		=							{}
       import re

       lowercase    :				Union[str, Any]       		=							re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"  )
       lowercase    :				str       		=							re.compile(
           r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"  )
       lowercase    :				Union[str, Any]       		=							re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"  )

       lowercase    :				int       		=							re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"  )
       lowercase    :				List[str]       		=							re.compile(
           r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"  )
       lowercase    :				str       		=							re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"  )

       lowercase    :				Optional[Any]       		=							re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"  )
       lowercase    :				Dict       		=							re.compile(
           r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"  )
       lowercase    :				List[str]       		=							re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"  )

       for original_key, value in state_dict.items():
              # rename vqvae.encoder keys
              if re_encoder_block_conv_in.fullmatch(__A  ):
                     lowercase    :				Optional[Any]       		=							re_encoder_block_conv_in.match(__A  )
                     lowercase    :				Optional[int]       		=							regex_match.groups()
                     lowercase    :				int       		=							int(groups[2]  ) * 2 + int(groups[3]  )
                     lowercase    :				List[str]       		=							f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
                     lowercase    :				Optional[int]       		=							re_encoder_block_conv_in.sub(__A     , __A  )

              elif re_encoder_block_resnet.fullmatch(__A  ):
                     lowercase    :				int       		=							re_encoder_block_resnet.match(__A  )
                     lowercase    :				Dict       		=							regex_match.groups()
                     lowercase    :				List[str]       		=							int(groups[2]  ) * 2 + int(groups[3]  )
                     lowercase    :				List[Any]       		=							{"1": 1, "3": 2}[groups[-2]]
                     lowercase    :				Union[str, Any]       		=							f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
                     lowercase    :				Dict       		=							f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     lowercase    :				str       		=							prefix + resnet_block
                     lowercase    :				Any       		=							re_encoder_block_resnet.sub(__A     , __A  )

              elif re_encoder_block_proj_out.fullmatch(__A  ):
                     lowercase    :				Tuple       		=							re_encoder_block_proj_out.match(__A  )
                     lowercase    :				List[str]       		=							regex_match.groups()
                     lowercase    :				Union[str, Any]       		=							f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
                     lowercase    :				str       		=							re_encoder_block_proj_out.sub(__A     , __A  )

              # rename vqvae.decoder keys
              elif re_decoder_block_conv_out.fullmatch(__A  ):
                     lowercase    :				Dict       		=							re_decoder_block_conv_out.match(__A  )
                     lowercase    :				Any       		=							regex_match.groups()
                     lowercase    :				int       		=							int(groups[2]  ) * 2 + int(groups[3]  ) - 2
                     lowercase    :				int       		=							f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
                     lowercase    :				Optional[int]       		=							re_decoder_block_conv_out.sub(__A     , __A  )

              elif re_decoder_block_resnet.fullmatch(__A  ):
                     lowercase    :				str       		=							re_decoder_block_resnet.match(__A  )
                     lowercase    :				List[str]       		=							regex_match.groups()
                     lowercase    :				int       		=							int(groups[2]  ) * 2 + int(groups[3]  ) - 2
                     lowercase    :				Any       		=							{"1": 1, "3": 2}[groups[-2]]
                     lowercase    :				Union[str, Any]       		=							f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
                     lowercase    :				str       		=							f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     lowercase    :				Optional[Any]       		=							prefix + resnet_block
                     lowercase    :				Optional[int]       		=							re_decoder_block_resnet.sub(__A     , __A  )

              elif re_decoder_block_proj_in.fullmatch(__A  ):
                     lowercase    :				Union[str, Any]       		=							re_decoder_block_proj_in.match(__A  )
                     lowercase    :				List[str]       		=							regex_match.groups()
                     lowercase    :				Any       		=							f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
                     lowercase    :				Tuple       		=							re_decoder_block_proj_in.sub(__A     , __A  )

              # rename prior cond.model to upsampler.upsample_block and resnet
              elif re_prior_cond_conv_out.fullmatch(__A  ):
                     lowercase    :				Union[str, Any]       		=							re_prior_cond_conv_out.match(__A  )
                     lowercase    :				int       		=							regex_match.groups()
                     lowercase    :				Tuple       		=							int(groups[1]  ) * 2 + int(groups[2]  ) - 2
                     lowercase    :				str       		=							f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
                     lowercase    :				Optional[Any]       		=							re_prior_cond_conv_out.sub(__A     , __A  )

              elif re_prior_cond_resnet.fullmatch(__A  ):
                     lowercase    :				List[str]       		=							re_prior_cond_resnet.match(__A  )
                     lowercase    :				Optional[int]       		=							regex_match.groups()
                     lowercase    :				Tuple       		=							int(groups[1]  ) * 2 + int(groups[2]  ) - 2
                     lowercase    :				Any       		=							{"1": 1, "3": 2}[groups[-2]]
                     lowercase    :				List[str]       		=							f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
                     lowercase    :				Tuple       		=							f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     lowercase    :				str       		=							prefix + resnet_block
                     lowercase    :				Tuple       		=							re_prior_cond_resnet.sub(__A     , __A  )

              elif re_prior_cond_proj_in.fullmatch(__A  ):
                     lowercase    :				Dict       		=							re_prior_cond_proj_in.match(__A  )
                     lowercase    :				Tuple       		=							regex_match.groups()
                     lowercase    :				Union[str, Any]       		=							f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
                     lowercase    :				Tuple       		=							re_prior_cond_proj_in.sub(__A     , __A  )

              # keep original key
              else:
                     lowercase    :				Optional[int]       		=							original_key

              lowercase    :				Dict       		=							replace_key(__A  )

              if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
                     print(f"""failed converting {original_key} to {key}, does not match"""  )

              # handle missmatched shape
              elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
                     lowercase    :				Optional[int]       		=							model_state_dict[f"""{key_prefix}.{key}"""]
                     print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""  )
                     lowercase    :				Optional[int]       		=							original_key

              lowercase    :				Dict       		=							original_key
              lowercase    :				Union[str, Any]       		=							value

       return new_dict


@torch.no_grad()
def   __magic_name__	(      __snake_case				:       List[Any]=None     , __snake_case				:       Optional[Any]=None  ) ->				Optional[Any]:


       for file in MODEL_MAPPING[model_name]:
              if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/"  )[-1]}"""  ):
                     lowercase    :				Union[str, Any]       		=							requests.get(f"""{PREFIX}{file}"""     , allow_redirects=__A  )
                     os.makedirs(f"""{pytorch_dump_folder_path}/"""     , exist_ok=__A  )
                     open(f"""{pytorch_dump_folder_path}/{file.split("/"  )[-1]}"""     , "wb"  ).write(r.content  )

       lowercase    :				Optional[int]       		=							MODEL_MAPPING[model_name.split("/"  )[-1]]

       lowercase    :				Optional[Any]       		=							JukeboxConfig.from_pretrained(__A  )
       lowercase    :				Any       		=							JukeboxModel(__A  )

       lowercase    :				Dict       		=							[]
       lowercase    :				Optional[Any]       		=							{}
       for i, dict_name in enumerate(__A  ):
              lowercase    :				Dict       		=							torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/"  )[-1]}"""  )["model"]

              lowercase    :				List[Any]       		=							{}
              for k in old_dic.keys():
                     if k.endswith(".b"  ):
                            lowercase    :				Optional[Any]       		=							old_dic[k]
                     elif k.endswith(".w"  ):
                            lowercase    :				int       		=							old_dic[k]
                     elif "level_2" not in dict_name and "cond.model." in k:
                            lowercase    :				Any       		=							old_dic[k]
                     else:
                            lowercase    :				Any       		=							old_dic[k]

              lowercase    :				Union[str, Any]       		=							"vqvae" if i == 0 else f"""priors.{3 - i}"""
              lowercase    :				Tuple       		=							fix_jukebox_keys(__A     , model.state_dict()     , __A     , __A  )
              weight_dict.append(__A  )

       lowercase    :				Any       		=							weight_dict.pop(0  )
       model.vqvae.load_state_dict(__A  )
       for i in range(len(__A  )  ):
              model.priors[i].load_state_dict(weight_dict[2 - i]  )

       Path(__A  ).mkdir(exist_ok=__A  )
       with open(f"""{pytorch_dump_folder_path}/mapping.json"""     , "w"  ) as txtfile:
              json.dump(__A     , __A  )

       print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
       model.save_pretrained(__A  )

       return weight_dict


if __name__ == "__main__":
   _A			:       List[Any]   		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""jukebox-5b-lyrics""",
       type=str,
       help="""Name of the model you\'d like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default="""jukebox-5b-lyrics-converted""",
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   _A			:       Tuple   		=       parser.parse_args()
   convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : torch.FloatTensor
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None




def    lowerCAmelCase_ (     __A,  __A=0.999,  __A="cosine",  )     ->				Tuple:







      '''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}"""							)

      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      A							(				UpperCAmelCase_		,				UpperCAmelCase_     ):


   @register_to_config
   def __init__(self     :						List[str]       ,  __UpperCAmelCase     :						int = 1_0_0_0       ,  __UpperCAmelCase     :						str = "fixed_small_log"       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						Optional[float] = 1.0       ,  __UpperCAmelCase     :						str = "epsilon"       ,  __UpperCAmelCase     :						str = "squaredcos_cap_v2"       ,  )						->				Optional[int]:
         """simple docstring"""



         if beta_schedule != "squaredcos_cap_v2":
               raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"		)

         UpperCAmelCase__								=  betas_for_alpha_bar(__UpperCAmelCase		)

         UpperCAmelCase__								=  1.0 - self.betas
         UpperCAmelCase__								=  torch.cumprod(self.alphas       ,  dim=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__								=  variance_type


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						Optional[int] = None		)						->				torch.FloatTensor:
         """simple docstring"""



         return sample


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Union[str, torch.device] = None		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  num_inference_steps
         UpperCAmelCase__								=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         UpperCAmelCase__								=  (np.arange(0       ,  __UpperCAmelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
         UpperCAmelCase__								=  torch.from_numpy(__UpperCAmelCase		).to(__UpperCAmelCase		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Optional[int]=None       ,  __UpperCAmelCase     :						Tuple=None       ,  __UpperCAmelCase     :						List[str]=None		)						->				Tuple:
         """simple docstring"""



         if prev_timestep is None:
               UpperCAmelCase__								=  t - 1

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

         if prev_timestep == t - 1:
               UpperCAmelCase__								=  self.betas[t]
         else:
               UpperCAmelCase__								=  1 - alpha_prod_t / 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
         UpperCAmelCase__								=  beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
               UpperCAmelCase__								=  self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
               UpperCAmelCase__								=  torch.log(torch.clamp(__UpperCAmelCase       ,  min=1E-20		)		)
               UpperCAmelCase__								=  torch.exp(0.5 * variance		)
         elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               UpperCAmelCase__								=  variance.log()
               UpperCAmelCase__								=  beta.log()

               UpperCAmelCase__								=  (predicted_variance + 1) / 2
               UpperCAmelCase__								=  frac * max_log + (1 - frac) * min_log

         return variance


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						Optional[int] = None       ,  __UpperCAmelCase     :						List[str]=None       ,  __UpperCAmelCase     :						bool = True       ,  )						->				Union[UnCLIPSchedulerOutput, Tuple]:
         """simple docstring"""



         UpperCAmelCase__								=  timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
               UpperCAmelCase__		,      UpperCAmelCase__								=  torch.split(__UpperCAmelCase       ,  sample.shape[1]       ,  dim=1		)
         else:
               UpperCAmelCase__								=  None

         # 1. compute alphas, betas
         if prev_timestep is None:
               UpperCAmelCase__								=  t - 1

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

         if prev_timestep == t - 1:
               UpperCAmelCase__								=  self.betas[t]
               UpperCAmelCase__								=  self.alphas[t]
         else:
               UpperCAmelCase__								=  1 - alpha_prod_t / alpha_prod_t_prev
               UpperCAmelCase__								=  1 - beta

         # 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":
               UpperCAmelCase__								=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
               UpperCAmelCase__								=  model_output
         else:
               raise ValueError(
                   f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
                   " for the UnCLIPScheduler."		)

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
               UpperCAmelCase__								=  torch.clamp(
                   __UpperCAmelCase       ,  -self.config.clip_sample_range       ,  self.config.clip_sample_range		)

         # 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
         UpperCAmelCase__								=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         UpperCAmelCase__								=  alpha ** 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
         UpperCAmelCase__								=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         UpperCAmelCase__								=  0
         if t > 0:
               UpperCAmelCase__								=  randn_tensor(
                   model_output.shape       ,  dtype=model_output.dtype       ,  generator=__UpperCAmelCase       ,  device=model_output.device		)

               UpperCAmelCase__								=  self._get_variance(
                   __UpperCAmelCase       ,  predicted_variance=__UpperCAmelCase       ,  prev_timestep=__UpperCAmelCase       ,  )

               if self.variance_type == "fixed_small_log":
                     UpperCAmelCase__								=  variance
               elif self.variance_type == "learned_range":
                     UpperCAmelCase__								=  (0.5 * variance).exp()
               else:
                     raise ValueError(
                         f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                         " for the UnCLIPScheduler."		)

               UpperCAmelCase__								=  variance * variance_noise

         UpperCAmelCase__								=  pred_prev_sample + variance

         if not return_dict:
               return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=__UpperCAmelCase       ,  pred_original_sample=__UpperCAmelCase		)





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						torch.FloatTensor       ,  __UpperCAmelCase     :						torch.IntTensor       ,  )						->				torch.FloatTensor:
         """simple docstring"""



         UpperCAmelCase__								=  self.alphas_cumprod.to(device=original_samples.device       ,  dtype=original_samples.dtype		)
         UpperCAmelCase__								=  timesteps.to(original_samples.device		)

         UpperCAmelCase__								=  alphas_cumprod[timesteps] ** 0.5
         UpperCAmelCase__								=  sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape		) < len(original_samples.shape		):
               UpperCAmelCase__								=  sqrt_alpha_prod.unsqueeze(-1		)

         UpperCAmelCase__								=  (1 - alphas_cumprod[timesteps]) ** 0.5
         UpperCAmelCase__								=  sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
               UpperCAmelCase__								=  sqrt_one_minus_alpha_prod.unsqueeze(-1		)

         UpperCAmelCase__								=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples



